@article {pmid40614457,
year = {2025},
author = {Yan, H and Wang, Z and Li, J},
title = {MSC-transformer-based 3D-attention with knowledge distillation for multi-action classification of separate lower limbs.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107806},
doi = {10.1016/j.neunet.2025.107806},
pmid = {40614457},
issn = {1879-2782},
abstract = {Deep learning has been extensively applied to motor imagery (MI) classification using electroencephalogram (EEG). However, most existing deep learning models do not extract features from EEG using dimension-specific attention mechanisms based on the characteristics of each dimension (e.g., spatial dimension), while effectively integrate local and global features. Furthermore, implicit information generated by the models has been ignored, leading to underutilization of essential information of EEG. Although MI classification has been relatively thoroughly investigated, the exploration of classification including real movement (RM) and motor observation (MO) is very limited, especially for separate lower limbs. To address the above problems and limitations, we proposed a multi-scale separable convolutional Transformer-based filter-spatial-temporal attention model (MSC-T3AM) to classify multiple lower limb actions. In MSC-T3AM, spatial attention, filter and temporal attention modules are embedded to allocate appropriate attention to each dimension. Multi-scale separable convolutions (MSC) are separately applied after the projections of query, key, and value in self-attention module to improve computational efficiency and classification performance. Furthermore, knowledge distillation (KD) was utilized to help model learn suitable probability distribution. The comparison results demonstrated that MSC-T3AM with online KD achieved best performance in classification accuracy, exhibiting an elevation of 2 %-19 % compared to a few counterpart models. The visualization of features extracted by MSC-T3AM with online KD reiterated the superiority of the proposed model. The ablation results showed that filter and temporal attention modules contributed most for performance improvement (improved by 2.8 %), followed by spatial attention module (1.2 %) and MSC module (1 %). Our study also suggested that online KD was better than offline KD and the case without KD. The code of MSC-T3AM is available at: https://github.com/BICN001/MSC-T3AM.},
}

@article {pmid40611671,
year = {2025},
author = {Alemu, RZ and Blakeman, A and Fung, AL and Hazen, M and Negandhi, J and Papsin, BC and Cushing, SL and Gordon, KA},
title = {Children With Bilateral Cochlear Implants Show Emerging Spatial Hearing of Stationary and Moving Sound.},
journal = {Trends in hearing},
volume = {29},
number = {},
pages = {23312165251356333},
doi = {10.1177/23312165251356333},
pmid = {40611671},
issn = {2331-2165},
mesh = {Humans ; *Sound Localization ; *Cochlear Implants ; Child ; Male ; Female ; *Cochlear Implantation/instrumentation ; Auditory Threshold ; Speech Perception ; Adolescent ; Cues ; Acoustic Stimulation ; *Persons with Hearing Disabilities/rehabilitation/psychology ; Case-Control Studies ; Eye Movements ; Noise/adverse effects ; Head Movements ; *Hearing Loss, Bilateral/physiopathology/rehabilitation/psychology ; },
abstract = {Spatial hearing in children with bilateral cochlear implants (BCIs) was assessed by: (a) comparing localization of stationary and moving sound, (b) investigating the relationship between sound localization and sensitivity to interaural level and timing differences (ILDs/ITDs), (c) evaluating effects of aural preference on sound localization, and (d) exploring head and eye (gaze) movements during sound localization. Children with BCIs (n = 42, MAge = 12.3 years) with limited duration of auditory deprivation and peers with typical hearing (controls; n = 37, MAge = 12.9 years) localized stationary and moving sound with unrestricted head and eye movements. Sensitivity to binaural cues was measured by a lateralization task to ILDs and ITDs. Spatial separation effects were measured by spondee-word recognition thresholds (SNR thresholds) when noise was presented in front (colocated/0°) or with 90° of left/right separation. BCI users had good speech reception thresholds (SRTs) in quiet but higher SRTs in noise than controls. Spatial separation of noise from speech revealed a greater advantage for the right ear across groups. BCI users showed increased errors localizing stationary sound and detecting moving sound direction compared to controls. Decreased ITD sensitivity occurred with poorer localization of stationary sound in BCI users. Gaze movements in BCI users were more random than controls for stationary and moving sounds. BCIs support symmetric hearing in children with limited duration of auditory deprivation and promote spatial hearing which is albeit impaired. Spatial hearing was thus considered to be "emerging." Remaining challenges may reflect disruptions in ITD sensitivity and ineffective gaze movements.},
}

Humans
*Sound Localization
*Cochlear Implants
Child
Male
Female
*Cochlear Implantation/instrumentation
Auditory Threshold
Speech Perception
Adolescent
Cues
Acoustic Stimulation
*Persons with Hearing Disabilities/rehabilitation/psychology
Case-Control Studies
Eye Movements
Noise/adverse effects
Head Movements
*Hearing Loss, Bilateral/physiopathology/rehabilitation/psychology

@article {pmid40611622,
year = {2025},
author = {Dahò, M and Monzani, D},
title = {The multifaceted nature of inner speech: Phenomenology, neural correlates, and implications for aphasia and psychopathology.},
journal = {Cognitive neuropsychology},
volume = {},
number = {},
pages = {1-21},
doi = {10.1080/02643294.2025.2527983},
pmid = {40611622},
issn = {1464-0627},
abstract = {This narrative review explores the phenomenon of inner speech - mental speech without visible articulation - and its implications for cognitive science and clinical practice. Despite its importance, the many neural mechanisms underlying inner speech remain unclear. We propose classifying inner speech into monologic, dialogal, elicited, and spontaneous forms, and discuss related phenomenological and neural correlates theories. A literature review on PubMed (1990-2024) identified 83 studies. Dialogal forms recruit Theory of Mind networks, compared to monologic forms. Task-elicited inner speech activates the left inferior frontal gyrus more strongly, while spontaneous inner speech engages Heschl's gyrus, suggesting auditory involvement. Evidence regarding aphasia suggests inner speech may be partially preserved even when overt speech is impaired, offering a potential route for rehabilitation. Future research should also address the emotional aspects of inner speech, its role in psychopathology, and its developmental trajectory. Such studies may improve interventions for disorders related to dysfunctional inner speech.Abbreviation: ACC: anterior cingulate cortex; ALE: activation likelihood estimation; AVH: auditory verbal hallucination; BMI: brain-machine interface; CD: corollary discharge; ConDialInt: consciousness-dialogue-intentionality; DES: descriptive experience sampling; DTI: diffusion tensor imaging; dPMC: dorsal premotor cortex; dmPFC: dorsomedial prefrontal cortex; IFG: inferior frontal gyrus; M1: primary motor cortex; MedFG: medial frontal gyrus; MFG: middle frontal gyrus; MTG: middle temporal gyrus; MRI: magnetic resonance imaging; preSMA: presupplementary motor area; PrG: precentral gyrus; SMA: supplementary motor area; SMG: supramarginal gyrus; SPC: superior parietal cortex; SPL: superior parietal lobule; STG: superior temporal gyrus; STS: superior temporal sulcus; TVA: temporal vocal areas; ToM: theory of mind; vmPFC: ventromedial prefrontal cortex.},
}

@article {pmid40611619,
year = {2025},
author = {Ponomarev, T and Vasilyev, A and Novikova, E and Pokidko, A and Zaitseva, N and Zaitsev, D and Kaplan, A},
title = {Brain mechanisms of (dis)agreement: ERP evidence from binary choice responses.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf167},
pmid = {40611619},
issn = {1460-2199},
support = {121032300070-1//Lomonosov Moscow State University/ ; },
mesh = {Humans ; Male ; Female ; *Evoked Potentials/physiology ; Electroencephalography ; *Brain/physiology ; Young Adult ; Adult ; *Choice Behavior/physiology ; Brain-Computer Interfaces ; *Decision Making/physiology ; },
abstract = {Agreement and disagreement are essential brain processes that enable effective communication and decision-making. However, a clear neurophysiological framework explaining their organization is still lacking. The present study aimed to identify EEG correlates of implicit agreement and disagreement, developing a novel experimental paradigm to model these internal responses. Participants were tasked with mentally responding to binary ("yes" or "no") questions and evaluating the accuracy of a computer system's attempts to "guess" their responses. Event-related potentials (ERP) revealed distinct patterns associated with agreement and disagreement in two key contexts: when participants read the final word of a question and when they observed the computer's "guess." Disagreement, compared to agreement, elicited larger ERP amplitudes, specifically an enhanced N400 component in the first context and increased feedback-related negativity in the second. Considering the associations of these ERP components with cognitive processes, this research offers robust evidence linking agreement and disagreement to the brain's effort in reconciling personal beliefs and expectations with new information. Furthermore, the experimental framework and findings provide a foundation for the development of brain-computer interfaces (BCIs) capable of detecting "yes" and "no" commands based on their intrinsic EEG predictors, offering promising applications in assistive technologies and neural communication systems.},
}

Humans
Male
Female
*Evoked Potentials/physiology
Electroencephalography
*Brain/physiology
Young Adult
Adult
*Choice Behavior/physiology
Brain-Computer Interfaces
*Decision Making/physiology

@article {pmid40611612,
year = {2025},
author = {Saeed, S and Wang, H and Jia, M and Liu, TT and Xu, L and Zhang, X and Hu, SH},
title = {The spectrum of overlapping anti-NMDAR encephalitis and demyelinating syndromes: a systematic review of presentation, diagnosis, management, and outcomes.},
journal = {Annals of medicine},
volume = {57},
number = {1},
pages = {2517813},
doi = {10.1080/07853890.2025.2517813},
pmid = {40611612},
issn = {1365-2060},
mesh = {Humans ; *Anti-N-Methyl-D-Aspartate Receptor Encephalitis/diagnosis/therapy/complications/immunology ; *Demyelinating Diseases/diagnosis/therapy/immunology/complications ; Autoantibodies ; Treatment Outcome ; },
abstract = {BACKGROUND: Anti-NMDAR encephalitis frequently overlaps with demyelinating diseases (MOGAD, NMOSD, MS), creating complex syndromes with diverse presentations and challenging management.

METHODS: Systematic search of databases including MEDLINE, Google Scholar, Embase, Scopus, Cochrane Library, and Web of Science up to March 2024 for studies on co-existing anti-NMDAR encephalitis and demyelinating syndromes. Data extracted on clinical characteristics, diagnostics, treatments, and outcomes.

RESULTS: Twenty-five studies identified 256 patients (16.2%) with co-existing Anti-NMDAR encephalitis and demyelinating syndromes, primarily MOGAD (94.5%), with fewer cases involving NMOSD or MS. The Anti-NMDAR + MOGAD subgroup exhibited seizures (51-72.7%), psychiatric symptoms (45.5-71.4%), cognitive dysfunction (30.6%), and movement disorders (30.6%). All patients had CSF anti-NMDAR antibodies, with MOG (60%) or AQP4 (25%) antibodies. Use of standardized, cell-based assays and adherence to established criteria are essential to avoid false positives, particularly for MOG. MRI abnormalities were seen in 75% of patients. First-line immunotherapies were effective in 70% of cases; 80% of refractory cases responded to second-line therapies.

CONCLUSIONS: Anti-NMDAR encephalitis overlapping with demyelinating diseases is challenging. Tailored treatments based on detailed immune profiles are key to better outcomes.},
}

Humans
*Anti-N-Methyl-D-Aspartate Receptor Encephalitis/diagnosis/therapy/complications/immunology
*Demyelinating Diseases/diagnosis/therapy/immunology/complications
Autoantibodies
Treatment Outcome

@article {pmid40611081,
year = {2025},
author = {Wei, Y and Xu, Y and Chen, W and Zheng, J and Chen, H and Chen, S},
title = {Can heart rate variability demonstrate the effects and the levels of mindfulness? A repeated-measures study on experienced and novice mindfulness practitioners.},
journal = {BMC complementary medicine and therapies},
volume = {25},
number = {1},
pages = {231},
pmid = {40611081},
issn = {2662-7671},
mesh = {Humans ; *Heart Rate/physiology ; *Mindfulness ; Male ; Female ; Adult ; Young Adult ; Middle Aged ; Meditation ; },
abstract = {BACKGROUND: Heart rate variability (HRV) is a potential biomarker that might demonstrate the effects of mindfulness, but it might be influenced by practice experiences. This study wanted to elucidate the possibility of using HRV metrics to reveal the effects of mindfulness and examine its variation between novice and experienced mindfulness practitioners.

METHODS: Forty-six participants (20 experienced practitioners, 26 novices) were enrolled to practice 14-day mindfulness training. HRV data were collected during three phases (20 min baseline, T1; 20 min mindfulness, T2; 20 min post-mindfulness, T3) using Holter monitoring. The linear mixed model was conducted to explore the effects of group and time based on standardized data.

RESULTS: The experienced group had higher full-scale scores of FFMQ both in the pre-test (t = -3.34, df = 44, p = 0.002) and the post-test (t = -2.35, df = 44, p = 0.025). Both groups showed significant changes in HRV indices (e.g., RMSSD, SDNN, LnHF) from T1 to T2 or T3 (p < 0.05). In the experienced group, significant fluctuations (p < 0.05) were observed at T2, followed by recovery at T3, in SD1/SD2, Sample Entropy, normalized High Frequency (HFn), DFA_α1, and DFA_α2. In contrast, the novice participants only showed monotonic changes in SD1/SD2 and DFA_α1.

CONCLUSIONS: This study revealed significant HRV changes during mindfulness practice, with distinct patterns observed between novice and experienced practitioners.},
}

Humans
*Heart Rate/physiology
*Mindfulness
Male
Female
Adult
Young Adult
Middle Aged
Meditation

@article {pmid40609489,
year = {2025},
author = {Cui, H and Hu, D and Yang, T and Huang, C and Yang, Z and Dong, S},
title = {Humidity sensors based on surface-functionalized tunable photonic crystal grating.},
journal = {Talanta},
volume = {296},
number = {},
pages = {128521},
doi = {10.1016/j.talanta.2025.128521},
pmid = {40609489},
issn = {1873-3573},
abstract = {Photonic crystal (PC)-based humidity sensors detect changes in humidity using periodic structural color variations and have significant potential in the humidity detection field. However, current technologies typically rely on observing these structural color changes with the human eye. The human eye has limited color discrimination, thus resulting in insufficient detection accuracy. Meanwhile, viewing angles and ambient lighting can also disrupt observations. Here, we propose a humidity sensor based on surface-functionalized tunable PC grating. The tunable PC grating consists of a 600 nm polystyrene (PS) microsphere PC and a humidity-sensitive hydrogel. As ambient humidity increases, the hydrophilic amide groups (-CONH2) inside the hydrogel interact with the hydrogen bonds between water molecules and triggers hydrogel swelling, exerts interfacial stress on the PS microsphere lattice, thus expanding the lattice spacing of the PS microspheres and causing a red shift in the reflected wavelength. Integrating the surface-functionalized tunable PC grating into a Czerny-Turner (C-T) optical system enables us to directly translate humidity into precise spectral shifts, overcoming the limitations of human eye-based observations. Experimental results demonstrate a strong linear response over the range of 24-94 % relative humidity (RH), as well as excellent repeatability and long-term stability. We provide an innovative solution for high-precision optical humidity sensing.},
}

@article {pmid40609413,
year = {2025},
author = {Wang, Y and Gao, Y and He, R and Gao, Y and Xu, Z and Wang, C and Liu, F},
title = {Global ocean surface pCO2 retrieval and the influence of mesoscale eddies on its performance.},
journal = {The Science of the total environment},
volume = {993},
number = {},
pages = {179856},
doi = {10.1016/j.scitotenv.2025.179856},
pmid = {40609413},
issn = {1879-1026},
abstract = {CO2 exchange at air-sea interface is crucial for global carbon cycle. Uncertainties in CO2 flux quantification are constrained by ocean surface partial pressure of CO2 (pCO2) variations. While regional pCO2 retrieval algorithms exist, the impact of mesoscale eddies on accuracy remains understudies. We improve the global ocean surface pCO2 retrieval algorithm using XGBoost, incorporating sea surface temperature (SST), chlorophyll-a (Chl-a), sea surface salinity (SSS), mixed layer depth (MLD), and sea surface height (SSH), achieving high performance (R[2]= 0.95, RMSE = 10.52 μatm) at daily resolution. The SHAP method and the sequential feature removal method were used to assesses the individual impacts. The results reveal that SSH significantly enhances model accuracy, increasing R[2] by ∼10% and decreasing RMSE by ∼38%. Regional evaluations show better performance in the Atlantic, with overestimation (underestimation) at ocean gyre fronts (interiors). The models perform better in summer, while in winter, more overestimation is observed in the North Pacific. The future prediction in global field shows excellent spatiotemporal extrapolation performance. The results verify mesoscale dynamics significantly impact the retrieval accuracy in energetic regions. Relative error normalized quantities were calculated for cyclonic and anticyclonic eddies in eddy-active regions to analyze the influence of energetic mesoscale dynamic, suggesting that regional and seasonal variations in errors are linked to differences in eddy-induced nutrient flux and baroclinic instabilities.},
}

@article {pmid40609325,
year = {2025},
author = {Ren, X and Zhou, C and Jiang, Y and Zhao, J and Tina, X and Xu, N and Fu, M and Ni, P and Li, T and Zhang, X},
title = {Generation of an induced pluripotent stem cell line (HZSMHCi002-A) from a patient with neuronal intranuclear inclusion disease carrying GGC repeat expansion in the NOTCH2NLC gene.},
journal = {Stem cell research},
volume = {87},
number = {},
pages = {103761},
doi = {10.1016/j.scr.2025.103761},
pmid = {40609325},
issn = {1876-7753},
abstract = {The NOTCH2NLC gene contains a GGC repeat expansion in its 5' untranslated region. This expansion is associated with neuronal intranuclear inclusion disease (NIID). NIID is a rare neurodegenerative disorder. Its clinical features include cognitive decline, paroxysmal symptoms, and autonomic dysfunction. We generated an induced pluripotent stem cell (iPSC) line from a female patient's PBMCs carrying a high GGC repeat expansion in NOTCH2NLC. The iPSC line displayed typical pluripotent morphology. It expressed key pluripotency markers and demonstrated differentiation potential in teratoma assays. This cell line serves as a useful model for studying disease mechanisms and developing therapeutic strategies.},
}

@article {pmid40609285,
year = {2025},
author = {Xu, JJ and Chen, YL and Yu, H and Chen, DF and Li, HF and Wu, ZY},
title = {Genetic and Clinical Features of SLC2A1-Related Paroxysmal Exercise-Induced Dyskinesia.},
journal = {Pediatric neurology},
volume = {170},
number = {},
pages = {31-37},
doi = {10.1016/j.pediatrneurol.2025.06.006},
pmid = {40609285},
issn = {1873-5150},
abstract = {BACKGROUND: Paroxysmal exercise-induced dyskinesia (PED) is a rare movement disorder characterized by choreoathetosis and dystonia triggered by sustained exercise, commonly affecting the lower extremities. PED is an autosomal dominant disorder genetically linked to mutations in the SLC2A1 gene. The transmembrane protein Glut1, encoded by the SLC2A1 gene, can transport glucose from blood to the brain. This study aimed to characterize the genetic and clinical features of SLC2A1-related PED.

METHODS: We reported two Chinese PED families presenting with involuntary movements after prolonged exercise. Whole-exome sequencing was performed on two probands, and cosegregation analysis was subsequently carried out in available family members. Additionally, we summarized and analyzed the genetic and clinical features of SLC2A1-related PED by retrieving information from the literature.

RESULTS: Genetic testing identified two missense mutations in SLC2A1 in these families, including a known disease-causing mutation, c.997C>T (p.R333W), and a novel mutation, c.823G>C (p.A275P). Upon review of the literature, mutations in certain regions of the Glut1 protein, particularly in transmembrane segments 3, 4, 5, 7, and 8, together with the intracellular domain, were more frequently seen in PED. Among the various types of epilepsy, absence seizures were the most common in patients with PED. Furthermore, familial PED had a later onset and a higher cerebrospinal fluid/blood glucose ratio. Patients with missense mutations exhibited a later onset than those with truncated mutations.

CONCLUSIONS: Our study identified a new disease-causing mutation and, through an extensive literature review, provided a detailed genetic and clinical description of PED associated with SLC2A1 mutations.},
}

@article {pmid40608885,
year = {2025},
author = {Yang, Z and Si, X and Jin, W and Huang, D and Zang, Y and Yin, S and Ming, D},
title = {SEEG Emotion Recognition Based on Transformer Network With Channel Selection and Explainability.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3585528},
pmid = {40608885},
issn = {2168-2208},
abstract = {Brain-computer interface (BCI) technology for emotion recognition holds significant potential for future applications in the treatment of refractory emotional disorders. Stereo-electroencephalography (SEEG), being less invasive, can precisely record neural activities originating from the cortex and the deep structures of the brain. Thus, it has broad application prospects in constructing emotion recognition BCI. In this study, SEEG data from nine subjects were collected to construct an emotion dataset, and a Spatial Transformer-based Hybrid Network (STHN) was proposed for SEEG emotion recognition. The triple-classification accuracy of STHN reached 83.56%, outperforming the baseline methods such as EEGNet, TSception, and the deep convolution neural network. Moreover, STHN can assign weights to each SEEG channel and select those channels that contribute more significantly to emotion recognition. It was found that when using the top 30% weighted SEEG channels as model inputs, the accuracy did not decrease significantly. Most of the channels with higher weights were located in brain regions strongly associated with emotions, such as the frontal lobe, the temporal lobe, and the hippocampus. This indicates that STHN is not merely a "black-box" model but possesses a degree of explainability. To the best of our knowledge, this is the first study to develop an SEEG emotion recognition algorithm, which is expected to play a crucial role in the monitoring and treatment of patients with refractory emotional disorders in the future.},
}

@article {pmid40608881,
year = {2025},
author = {Yu, X and Yu, X},
title = {Brain-Controlled Wheeled Mobile Robots: A Framework Combining Probabilistic Brain-Computer Interface and Model Predictive Control.},
journal = {IEEE transactions on cybernetics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TCYB.2025.3580726},
pmid = {40608881},
issn = {2168-2275},
abstract = {Brain-controlled systems have experienced significant advancements in overall performance, largely driven by continuous optimization and innovation in electroencephalography (EEG) acquisition experimental paradigms and decoding algorithms. However, their applications still face challenges, including limited control precision and low efficiency. In this article, we focus on a wheeled mobile robot (WMR) as the control object and propose a novel brain-controlled framework that combines a probabilistic brain-computer interface (BCI) and a model predictive controller (MPC). First, the probabilistic BCI is developed, featuring the sigmoid fitting-filter bank canonical correlation analysis (SF-FBCCA) algorithm, which serves as the core of the BCI system by decoding EEG signals and generating brain commands along with their associated probabilities. Second, an auxiliary MPC is integrated into the probabilistic BCI system to provide decision-making assistance while preserving the users' primary brain control authority. The weights of the cost function are adaptively determined based on the command probabilities. Finally, simulation-based evaluations were conducted using the WMR in a path-keeping scenario. The results demonstrate that the proposed framework significantly improves control accuracy and efficiency compared to direct brain control approaches, reducing the average lateral error by 58.02% and the average yaw angle error by 60.06%. Additionally, the MPC employing adaptive weights further improves overall performance. These findings offer theoretical insights and technical references for future research on BCI-based control frameworks.},
}

@article {pmid40606836,
year = {2025},
author = {Cantillo-Negrete, J and Rodríguez-García, ME and Carrillo-Mora, P and Arias-Carrión, O and Ortega-Robles, E and Galicia-Alvarado, MA and Valdés-Cristerna, R and Ramirez-Nava, AG and Hernandez-Arenas, C and Quinzaños-Fresnedo, J and Pacheco-Gallegos, MDR and Marín-Arriaga, N and Carino-Escobar, RI},
title = {The ReHand-BCI trial: a randomized controlled trial of a brain-computer interface for upper extremity stroke neurorehabilitation.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1579988},
pmid = {40606836},
issn = {1662-4548},
abstract = {BACKGROUND: Brain-computer interfaces (BCI) are a promising complementary therapy for stroke rehabilitation due to the close-loop feedback that can be provided with these systems, but more evidence is needed regarding their clinical and neuroplasticity effects.

METHODS: A randomized controlled trial was performed using the ReHand-BCI system that provides feedback with a robotic hand orthosis. The experimental group (EG) used the ReHand-BCI, while sham-BCI was given to the control group (CG). Both groups performed 30 therapy sessions, with primary outcomes being the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT). Secondary outcomes were hemispheric dominance, measured with electroencephalography and functional magnetic resonance imaging, white matter integrity via diffusion tensor imaging, and corticospinal tract integrity and excitability, measured with transcranial magnetic stimulation.

RESULTS: At post-treatment, patients in both groups had significantly different FMA-UE scores (EG: baseline = 24.5[20, 36], post-treatment 28[23, 43], CG: baseline = 26[16, 37.5], post-treatment = 34[17.3, 46.5]), while only the EG had significantly different ARAT scores at post-treatment (EG: baseline = 8.5[5, 26], post-treatment = 20[7, 36], CG: baseline = 3[1.8, 30.5], post-treatment = 15[2.5, 40.8]). In addition, across the intervention, the EG showed trends of more pronounced ipsilesional cortical activity and higher ipsilesional corticospinal tract integrity, although these differences were not statistically different compared to the control group, likely due to the study's sample size.

CONCLUSION: To the authors' knowledge, this is the first clinical trial that has assessed such a wide range of physiological effects across a long BCI intervention, implying that a more pronounced ipsilesional hemispheric dominance is associated with upper extremity motor recovery. Therefore, the study brings light into the neuroplasticity effects of a closed-loop BCI-based neurorehabilitation intervention in stroke.

CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/, identifier NCT04724824.},
}

@article {pmid40606655,
year = {2025},
author = {Jacob, JE and Chandrasekharan, S},
title = {Editorial: Advanced EEG analysis techniques for neurological disorders.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1637890},
doi = {10.3389/fninf.2025.1637890},
pmid = {40606655},
issn = {1662-5196},
}

@article {pmid40605914,
year = {2025},
author = {Yang, L and Zhu, W},
title = {Mifnet: a MamBa-based interactive frequency convolutional neural network for motor imagery decoding.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {106},
pmid = {40605914},
issn = {1871-4080},
abstract = {Motor imagery (MI) decoding remains a critical challenge in brain-computer interface (BCI) systems due to the low signal-to-noise ratio, non-stationarity, and complex spatiotemporal dynamics of electroencephalography (EEG) signals. Although deep learning architectures have advanced MI-EEG decoding, existing approaches-including convolutional neural networks (CNNs), Transformers, and recurrent neural networks (RNNs)-still face limitations in capturing global temporal dependencies, maintaining positional coherence, and ensuring computational efficiency. To address these challenges, we propose MIFNet, a MamBa-based Interactive Frequency Convolutional Neural Network that systematically integrates spectral, spatial, and temporal feature extraction. Specifically, MIFNet incorporates: non-overlapping frequency decomposition, which selectively extracts motor imagery-related mu (8-12 Hz) and beta (12-32 Hz) rhythms; a ConvEncoder module, which autonomously learns to fuse spectral-spatial features from both frequency bands; and a MamBa-based temporal module, leveraging selective state-space models (SSMs) to efficiently capture long-range dependencies with linear complexity. Extensive experiments on three public MI-EEG datasets (BCIC-IV-2A, OpenBMI, and High Gamma) demonstrate that MIFNet outperforms existing models, achieving an average classification accuracy improvement of 12.3%, 8.3%, 4.7%, and 5.5% over EEGNet, FBCNet, IFNet, and Conformer, respectively. Ablation studies further validate the necessity of each component, with the MamBa module contributing a 5.5% improvement in accuracy on the BCIC-IV-2A dataset. Moreover, MIFNet exhibits strong generalization performance in cross-validation settings, establishing a robust foundation for real-time BCI applications. Our findings highlight the potential of hybridizing CNNs with state-space models (SSMs) for improving EEG decoding performance, effectively bridging the gap between localized feature extraction and global temporal modeling.},
}

@article {pmid40603471,
year = {2025},
author = {Liao, W and Liu, H and Wang, W},
title = {Advancing BCI with a transformer-based model for motor imagery classification.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {23380},
pmid = {40603471},
issn = {2045-2322},
support = {2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; 2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; 2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Neural Networks, Computer ; Algorithms ; Machine Learning ; Deep Learning ; },
abstract = {Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extraction and susceptibility to noise. This paper introduces EEGEncoder, a deep learning framework that employs modified transformers and Temporal Convolutional Networks (TCNs) to surmount these limitations. We propose a novel fusion architecture, named Dual-Stream Temporal-Spatial Block (DSTS), to capture temporal and spatial features, improving the accuracy of Motor Imagery classification task. Additionally, we use multiple parallel structures to enhance the model's performance. When tested on the BCI Competition IV-2a dataset, our proposed model achieved an average accuracy of 86.46% for subject dependent and average 74.48% for subject independent.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Imagination/physiology
Neural Networks, Computer
Algorithms
Machine Learning
Deep Learning

@article {pmid40603333,
year = {2025},
author = {Isaev, MR and Mokienko, OA and Lyukmanov, RK and Ikonnikova, ES and Cherkasova, AN and Suponeva, NA and Piradov, MA and Bobrov, PD},
title = {Correction: A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1132},
doi = {10.1038/s41597-025-05466-y},
pmid = {40603333},
issn = {2052-4463},
}

@article {pmid40602422,
year = {2025},
author = {Jin, J and Liang, W and Xu, R and Chen, W and Xu, R and Wang, X and Cichocki, A},
title = {A transformer-based network with second-order pooling for motor imagery EEG classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeae8},
pmid = {40602422},
issn = {1741-2552},
abstract = {OBJECTIVE: Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer interfaces (BCIs) have attracted the attention of neuroinformatics researchers. Numerous deep learning models have been developed to decode EEG signals. Although deep learning models, particularly those based on convolutional neural networks (CNNs), have shown promise in decoding EEG signals, most existing methods focus on attention mechanisms while neglecting high-order statistical dependencies that are critical for accurately capturing the complex structure of EEG data.

APPROACH: To address this limitation, we propose a neural network integrating a transpose-attention mechanism and second-order pooling (SecTNet). The proposed model tackles two fundamental challenges in EEG decoding. It metrics the covariance structure of EEG signals using Riemannian geometry on symmetric positive definite (SPD) matrices, and it enhances the discriminability of these SPD features by introducing attention mechanisms that adaptively model inter-channel dependencies. Specifically, SecTNet is composed of three key components. First, a multi-scale spatial-temporal convolution module extracts detailed local features. Second, a transpose-attention mechanism captures dependency information from the internal interactions between channels. Lastly, a second-order pooling layer captures high-order statistical correlations in the EEG feature space.

MAIN RESULTS: SecTNet is evaluated on two publicly available EEG datasets, namely BCI Competition IV 2a dataset and OpenBMI dataset. It achieves an average accuracy of 86.88% on the BCI Competition IV dataset 2a and 74.99% on the OpenBMI dataset. Moreover, results show that SecTNet maintains competitive performance even when trained on only 50% of the data, demonstrating strong generalization under limited data conditions.

SIGNIFICANCE: These results demonstrate the broad applicability and effectiveness of SecTNet in enhancing MI-BCI performance. SecTNet provides a robust and generalizable framework for EEG decoding, supporting the development of BCI applications across diverse real-world scenarios.},
}

@article {pmid40602419,
year = {2025},
author = {Li, L and Wei, B},
title = {A two-stage EEG zero-shot classification algorithm guided by class reconstruction.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeaea},
pmid = {40602419},
issn = {1741-2552},
abstract = {Researchers have long been dedicated to decoding human visual representations from neural signals. These studies are crucial in uncovering the mechanisms of visual processing in the human brain. Electroencephalogram(EEG) signals have garnered widespread attention recently due to their noninvasive nature and low cost. EEG classification is one of the most popular topics in brain-computer interface(BCI) research. However, most traditional EEG classification algorithms are difficult to generalize to unseen classes that were not involved in the training phase. The main objective of this work is to improve the performance of these EEG classification algorithms for unseen classes. In this work, we propose a two-stage zero-shot EEG classification algorithm guided by class reconstruction. The method is specifically designed with a two-stage training strategy based on class reconstruction. This structure and training strategy enable the model to thoroughly learn the relations and distinctions among EEG embeddings of different classes. The Contrastive Language-Image Pre-training(CLIP) model has a well-aligned latent space and powerful cross-modality generalization ability. The method bridges the modality gap between EEG, images, and text using CLIP features. It significantly improves the model's performance in unseen classes. We conducted experiments on the ImageStimulus-EEG dataset to evaluate the performance of the proposed method. Meanwhile, it was compared with the state-of-the-art model and the baseline model. The experimental results demonstrate that our model achieves superior performance in among Top-1, Top-3, and Top-5 classification accuracy for a 50-way zero-shot classification task, reaching 17.77%, 38.76% and 54.75%, respectively. These results further validate the effectiveness of the proposed method in EEG zero-shot classification.},
}

@article {pmid40602315,
year = {2025},
author = {Del Pup, F and Zanola, A and Tshimanga, LF and Bertoldo, A and Finos, L and Atzori, M},
title = {The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: A preliminary study.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110608},
doi = {10.1016/j.compbiomed.2025.110608},
pmid = {40602315},
issn = {1879-0534},
abstract = {Deep learning is significantly advancing the analysis of electroencephalography (EEG) data by effectively discovering highly nonlinear patterns within the signals. Data partitioning and cross-validation are crucial for assessing model performance and ensuring study comparability, as they can produce varied results and data leakage due to specific signal properties (e.g., biometric). Such variability in model evaluation leads to incomparable studies and, increasingly, overestimated performance claims, which are detrimental to the field. Nevertheless, no comprehensive guidelines for proper data partitioning and cross-validation exist in the domain, nor is there a quantitative evaluation of the impact of different approaches on model accuracy, reliability, and generalizability. To assist researchers in identifying optimal experimental strategies, this paper thoroughly investigates the role of data partitioning and cross-validation in evaluating EEG deep learning models. Five cross-validation settings are compared across three supervised cross-subject classification tasks (brain-computer interfaces, Parkinson's, and Alzheimer's disease classification) and four established architectures of increasing complexity (ShallowConvNet, EEGNet, DeepConvNet, and Temporal-based ResNet). The comparison of over 100,000 trained models underscores, first, the importance of using subject-based cross-validation strategies for evaluating EEG deep learning architectures, except when within-subject analyses are acceptable (e.g., BCI). Second, it highlights the greater reliability of nested approaches (e.g., N-LNSO) compared to non-nested counterparts, which are prone to data leakage and favor larger models overfitting to validation data. In conclusion, this work provides EEG deep learning researchers with an analysis of data partitioning and cross-validation and offers guidelines to avoid data leakage, currently undermining the domain with potentially overestimated performance claims.},
}

@article {pmid40602314,
year = {2025},
author = {Huang, S and Wei, Q},
title = {A deep learning model combining convolutional neural networks and a selective kernel mechanism for SSVEP-Based BCIs.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110691},
doi = {10.1016/j.compbiomed.2025.110691},
pmid = {40602314},
issn = {1879-0534},
abstract = {Existing deep learning methods for brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) face several challenges, such as overfitting when training data are insufficient, and the difficulty of effectively capturing global temporal features due to limited receptive fields. To address these challenges, we propose a novel deep learning model, FBCNN-TKS, which extracts harmonic components from SSVEP signals using a filter bank technique, followed by feature extraction through convolutional neural networks (CNNs) and a temporal kernel selection (TKS) module, and finally the weighted sum of cross-entropy loss and center loss is used as the objective function for model optimization. The key innovation of our approach lies in the introduction of the TKS module, which significantly enhances feature extraction capability by providing a broader receptive field. Additionally, dilated and grouped convolutions are used in TKS module to reduce the number of model parameters, minimizing the risk of overfitting and improving classification accuracy. Experimental results manifest that FBCNN-TKS outperforms state-of-the-art methods in terms of classification accuracy and information transfer rate (ITR). Specifically, FBCNN-TKS achieved the highest ITRs of 251.54 bpm and 203.47 bpm with the highest accuracies of 83.10 % and 72.98 % on public datasets Benchmark and BETA respectively at the data length of 0.4s, exhibiting superior performance. The FBCNN-TKS model bears big potential for the development of high-performance SSVEP-BCI character spelling systems.},
}

@article {pmid40601454,
year = {2025},
author = {Zhang, Y and Yu, Y and Li, H and Wu, A and Chen, X and Liu, J and Zeng, LL and Hu, D},
title = {DMAE-EEG: A Pretraining Framework for EEG Spatiotemporal Representation Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3581991},
pmid = {40601454},
issn = {2162-2388},
abstract = {Electroencephalography (EEG) plays a crucial role in neuroscience research and clinical practice, but it remains limited by nonuniform data, noise, and difficulty in labeling. To address these challenges, we develop a pretraining framework named DMAE-EEG, a denoising masked autoencoder for mining generalizable spatiotemporal representation from massive unlabeled EEG. First, we propose a novel brain region topological heterogeneity (BRTH) division method to partition the nonuniform data into fixed patches based on neuroscientific priors. Second, we design a denoised pseudo-label generator (DPLG), which utilizes a denoising reconstruction pretext task to enable the learning of generalizable representations from massive unlabeled EEG, suppressing the influence of noise and artifacts. Furthermore, we utilize an asymmetric autoencoder with self-attention as the backbone in the proposed DMAE-EEG, which captures long-range spatiotemporal dependencies and interactions from unlabeled EEG data across 14 public datasets. The proposed DMAE-EEG is validated on both generative (signal quality enhancement) and discriminative tasks (motion intention recognition). In the quality enhancement, DMAE-EEG outperforms existing statistical methods with normalized mean squared error (nMSE) reduction of 27.78%-50.00% under corruption levels of 25%, 50%, and 75%, respectively. In motion intention recognition, DMAE-EEG achieves a relative improvement of 2.71%-6.14% in intrasession classification balanced accuracy across 2-6 class motor execution and imagery tasks, outperforming state-of-the-art methods. Overall, the results suggest that the pretraining framework DMAE-EEG can capture generalizable spatiotemporal representations from massive unlabeled EEG and enhance the knowledge transferability across sessions, subjects, and tasks in various downstream scenarios, advancing EEG-aided diagnosis and brain-computer communication and control, and other clinical practice.},
}

@article {pmid40601441,
year = {2025},
author = {Si, X and Han, Y and Li, S and Zhang, S and Ming, D},
title = {The cortical spatial responses and decoding of emotion imagery towards a novel fNIRS-based affective BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3584765},
pmid = {40601441},
issn = {1558-0210},
abstract = {Functional near-infrared spectroscopy (fNIRS), with its non-invasive and high spatial resolution, holds promise in developing novel affective brain-computer interface (BCI). Similar to motor imagery BCI, emotion imagery BCI could recognize internal emotions and convey them to the external world. This holds clinical value for expressing emotions in patients with neurological impairments and serves as a proactive emotion regulation method. However, the fNIRS features of emotion imagery for affective BCI and the discriminability of different emotion categories remain unclear. Here, this study designed a novel emotion verbal imagery paradigm (imagining descriptions of happy or sad scenes). First, task-related hemodynamic responses were analyzed from 17 subjects. Then, statistical analyses were then conducted to reveal the significant cortical spatial response patterns. Additionally, decoding experiments and model interpretability are employed to assist in validating the feasibility of the emotion imagery BCI. Results showed: (1) Happy imagery recruited frontoparietal regions, such as the left dorsal secondary motor cortex, ventral secondary motor cortex, and inferior parietal lobe. (2) Sad imagery mainly recruited the right dorsolateral prefrontal cortex. (3) The left dorsal sensorimotor cortex exhibited selective responsiveness to happy imagery and sad imagery. (4) The classification results of the emotion imagery task exceeded the random level. (5) Emotional categories activation responses showed significant similarity with the hemodynamic responses of the imagination tasks. Taken together, by proposing the emotion imagery fNIRS paradigm, this work could shed light on the development of feature non-invasive BCI.},
}

@article {pmid40600191,
year = {2025},
author = {Ma Thi, C and Nguyen The, HA and Nguyen Minh, K and Vu Thanh, L and Nguyen Dinh, H and Huynh Thi, NY and Ha Thi, TH and Hoang Tien, TN and Au Dao, DT and Nguyen Hoang, KL and Huynh Kha, V and Le Hoang, TL},
title = {UET175: EEG dataset of motor imagery tasks in Vietnamese stroke patients.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1580931},
pmid = {40600191},
issn = {1662-4548},
}

@article {pmid40598468,
year = {2025},
author = {Chen, Y and Zhao, N and Zhang, J and Wu, X and Huang, J and Xu, X and Cai, F and Chen, S and Xu, L and Yan, W and Hong, Y and Wang, Y and Ling, H and Ji, J and Chen, G and Gu, H and Zhang, J and Wu, Q},
title = {Molecular signatures of invasive and non-invasive pituitary adenomas: a comprehensive analysis of DNA methylation and gene expression.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {373},
pmid = {40598468},
issn = {1741-7015},
mesh = {Humans ; *DNA Methylation ; *Pituitary Neoplasms/genetics/pathology ; *Adenoma/genetics/pathology ; Male ; Female ; Middle Aged ; Adult ; *Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics ; Neoplasm Invasiveness ; Gene Expression Profiling ; },
abstract = {BACKGROUND: Pituitary adenomas (PAs) are benign tumors in the pituitary gland. However, 30-40% of these tumors are invasive, complicating diagnosis and treatment. Invasive pituitary adenomas (IPAs) often respond poorly to conventional therapies, emphasizing the need for better diagnostic and therapeutic strategies. Understanding DNA methylation patterns in IPAs may reveal new biomarkers and therapeutic targets, leading to more effective management of this challenging disease.

METHODS: Reduced representation bisulfite sequencing (RRBS) and RNA sequencing (RNA-seq) were performed on 129 samples from the Second Affiliated Hospital of Zhejiang University, including 69 tissue samples from invasive and non-invasive pituitary adenomas (NPA) and 60 blood samples from IPA, NPA and healthy individuals. Differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were identified in tissues. Pearson correlation analysis was used to identify associations between DNA methylation status and gene expression, as well as the effect of methylation on gene expression at different sites. Blood samples were analyzed to detect DMRs and DEGs, correlating with tissue-derived findings. Finally, ROC analysis and a random forest model were used to identify biomarkers for discriminating invasive from non-invasive phenotypes.

RESULTS: We identified 347 DMRs between IPA and NPA, of which 63% (219/347) were hypomethylated. Additionally, 543 mRNAs showed differential expression, with 350 upregulated and 193 downregulated. 17 genes demonstrated concurrent aberrant methylation and expression, primarily within introns, promoters, and CpG islands (CGIs). Notably, only protein tyrosine phosphatase receptor type T (PTPRT) exhibited a remarkably high correlation (r = 0.81) between its DNA methylation levels and mRNA expression levels. This correlation was observed within the intronic region/opensea of the gene's CGIs. Plasma sample analysis revealed 852 DMRs between IPA and NPA, with 52% (447/852) being hypomethylated. Three tumor tissue-derived blood biomarkers (MIR4535, SLC8A1-AS1, and TTC34) accurately discriminated between IPA and NPA patients with a combined AUC of 0.980. These markers also differentiated NPA from healthy controls, though with different methylation patterns.

CONCLUSIONS: The relationship between DNA methylation and gene expression is complex. Plasma-based DNA methylation markers can effectively discriminate between IPA and NPA, as well as between NPA and healthy individuals (N group).},
}

Humans
*DNA Methylation
*Pituitary Neoplasms/genetics/pathology
*Adenoma/genetics/pathology
Male
Female
Middle Aged
Adult
*Gene Expression Regulation, Neoplastic
Biomarkers, Tumor/genetics
Neoplasm Invasiveness
Gene Expression Profiling

@article {pmid40598460,
year = {2025},
author = {Lu, R and Pang, Z and Gao, T and He, Z and Hu, Y and Zhuang, J and Zhang, Q and Gao, Z},
title = {Multisensory BCI promotes motor recovery via high-order network-mediated interhemispheric integration in chronic stroke.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {380},
pmid = {40598460},
issn = {1741-7015},
support = {82372570//the National Science Foundation of China/ ; 82372570//the National Science Foundation of China/ ; 82271422//the National Science Foundation of China/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 22ZR1479000//Shanghai Natural Science Foundation/ ; 20234Y0043//Shanghai Municipal Health Commission/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Recovery of Function/physiology ; *Stroke/physiopathology ; Aged ; *Feedback, Sensory/physiology ; Chronic Disease ; Magnetic Resonance Imaging ; Adult ; Neuronal Plasticity ; },
abstract = {BACKGROUND: Chronic stroke patients often experience persistent motor impairments, and current rehabilitation therapies rarely achieve substantial functional recovery. Sensory feedback during movement plays a pivotal role in driving neuroplasticity. This study introduces a novel multi-modal sensory feedback brain-computer interface (Multi-FDBK-BCI) system that integrates proprioceptive, tactile, and visual stimuli into motor imagery-based training. We aimed to explore the potential therapeutic efficacy and elucidate its neural mechanisms underlying motor recovery.

METHODS: Thirty-nine chronic stroke patients were randomized to either the Multi-FDBK-BCI group (n = 20) or the conventional motor imagery therapy group (n = 19). Motor recovery was assessed using the Fugl-Meyer Assessment (primary outcome), Motor Status Scale, Action Research Arm Test, and surface electromyography. Functional MRI was used to examine brain activation patterns during upper limb tasks, while Granger causality analysis and machine learning evaluated inter-regional connectivity changes and their predictive value for recovery.

RESULTS: Multi-FDBK-BCI training led to significantly greater motor recovery compared to conventional therapy. Functional MRI revealed enhanced activation of high-order transmodal networks-including the default mode, dorsal/ventral attention, and frontoparietal networks-during paralyzed limb movement, with activation strength positively correlated with motor improvement. Granger causality analysis identified a distinct information flow pattern: signals from the lesioned motor cortex were relayed through transmodal networks to the intact motor cortex, promoting interhemispheric communication. These functional connectivity changes not only supported motor recovery but also served as robust predictors of therapeutic outcomes.

CONCLUSIONS: Our findings highlight the Multi-FDBK-BCI system as a promising strategy for chronic stroke rehabilitation, leveraging activity-dependent neuroplasticity within high-order transmodal networks. This multi-modal approach holds significant potential for patients with limited recovery options and sheds new light on the neural drivers of motor restoration, warranting further investigation in clinical neurorehabilitation.

TRIAL REGISTRATION: All data used in the present study were obtained from a research trial registered with the ClinicalTrials.gov database (ChiCTR-ONC-17010739, registered 26 February 2017, starting from 10 January 2017).},
}

Humans
*Brain-Computer Interfaces
Male
Female
*Stroke Rehabilitation/methods
Middle Aged
*Recovery of Function/physiology
*Stroke/physiopathology
Aged
*Feedback, Sensory/physiology
Chronic Disease
Magnetic Resonance Imaging
Adult
Neuronal Plasticity

@article {pmid40596215,
year = {2025},
author = {Rabbani, M and Sabith, NUS and Parida, A and Iqbal, I and Mamun, SM and Khan, RA and Ahmed, F and Ahamed, SI},
title = {EEG based real time classification of consecutive two eye blinks for brain computer interface applications.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {21007},
pmid = {40596215},
issn = {2045-2322},
mesh = {Humans ; *Blinking/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Support Vector Machine ; Neural Networks, Computer ; Young Adult ; Machine Learning ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Human eye blinks are considered a significant contaminant or artifact in electroencephalogram (EEG), which impacts EEG-based medical or scientific applications. However, eye blink detection can instead be transformed into a potential application of brain-computer interfaces (BCI). This study introduces a novel real-time EEG-based framework for classifying three blink states: no blink, single blink, and two consecutive blinks in one model. EEG data were collected from ten healthy participants using an 8-channel wearable headset under controlled blinking conditions. The data were preprocessed and analyzed using four feature extraction techniques: basic statistical, time-domain, amplitude-driven, and frequency-domain methods. The most significant features were selected to develop three machine learning models: XGBoost, support vector machine (SVM), and neural network (NN). We achieved the highest accuracy of 89.0% for classifying multiple-eye blink detection. To further enhance the model's capacity and suitability for real-life BCI applications, we trained and employed the You Only Look Once (YOLO) model, achieving a recall of 98.67%, a precision of 95.39%, and mAP50 of 99.5%, demonstrating its superior accuracy and robustness in classifying two consecutive eye blinks. In conclusion, this study will be the first groundwork and open a new dimension in EEG-based BCI research by classifying multiple-eye blink detection.},
}

Humans
*Blinking/physiology
*Brain-Computer Interfaces
*Electroencephalography/methods
Male
Adult
Female
Support Vector Machine
Neural Networks, Computer
Young Adult
Machine Learning
Signal Processing, Computer-Assisted
Brain/physiology

@article {pmid40595635,
year = {2025},
author = {Liu, L and Gao, Z and Niu, X and Yu, H and Xin, X and Gu, Y and Ma, G and Gu, Y and Liu, Y and Fang, S and Marquardt, T and Wang, L},
title = {SEMA3B switches axon-axon to axon-glia interactions required for unmyelinated axon envelopment and integrity.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5433},
pmid = {40595635},
issn = {2041-1723},
support = {32100758//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Axons/metabolism ; *Semaphorins/metabolism/genetics ; Mice ; Schwann Cells/metabolism ; Hyperalgesia/metabolism ; Male ; Mice, Inbred C57BL ; *Nerve Fibers, Unmyelinated/metabolism ; Peripheral Nerve Injuries/metabolism ; Endocytosis ; *Neuroglia/metabolism ; Cell Communication ; },
abstract = {During peripheral nerve (PN) development, unmyelinated axons (nmAs) tightly fasciculate before being separated and enveloped by non-myelinating Schwann cells (nmSCs), glial cells essential for maintaining nmA integrity. How such a switch from axon-axon to axon-glia interactions is achieved remains poorly understood. Here, we find that inactivating SC-derived SEMA3B or its axonal receptor components in mice leads to incomplete nmA separation and envelopment by nmSCs, eliciting hyperalgesia and allodynia. Conversely, increasing SEMA3B levels in SCs accelerates nmA separation and envelopment. SEMA3B transiently promotes nmA defasciculation accompanied by cell adhesion molecule (CAM) endocytosis, subsequently facilitating nmA-nmSC association. Restoring SEMA3B expression following PN injury promotes nmA-nmSC re-association and alleviates hyperalgesia and allodynia. We propose that SEMA3B-induced CAM turnover facilitates a switch from axon-axon to axon-glia interactions promoting nmA envelopment by nmSCs, which may be exploitable for alleviating PN injury-induced pain by accelerating the restoration of nmA integrity.},
}

Animals
*Axons/metabolism
*Semaphorins/metabolism/genetics
Mice
Schwann Cells/metabolism
Hyperalgesia/metabolism
Male
Mice, Inbred C57BL
*Nerve Fibers, Unmyelinated/metabolism
Peripheral Nerve Injuries/metabolism
Endocytosis
*Neuroglia/metabolism
Cell Communication

@article {pmid40594904,
year = {2025},
author = {Sayem, M and Rafi, MA and Mishu, ID and Mahmud, Z},
title = {Comprehensive genomic analysis reveals virulence and antibiotic resistance genes in a multidrug-resistant Bacillus cereus isolated from hospital wastewater in Bangladesh.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {22915},
pmid = {40594904},
issn = {2045-2322},
mesh = {*Bacillus cereus/genetics/pathogenicity/isolation & purification/drug effects ; *Wastewater/microbiology ; Bangladesh ; *Drug Resistance, Multiple, Bacterial/genetics ; Phylogeny ; Hospitals ; Virulence/genetics ; Genome, Bacterial ; Whole Genome Sequencing ; Genomics/methods ; Anti-Bacterial Agents/pharmacology ; Virulence Factors/genetics ; Humans ; },
abstract = {Hospital wastewater represents a significant reservoir for antimicrobial-resistant bacteria, including multidrug-resistant (MDR) Bacillus cereus, a pathogen of growing concern due to its potential impact on public health and environmental safety. This study characterizes the genomic features, antimicrobial resistance (AMR) mechanisms, and virulence potential of Bacillus cereus MBC, isolated from hospital wastewater in Dhaka, Bangladesh. Using whole-genome sequencing (WGS) and advanced bioinformatics, we analyzed the isolate's taxonomy, phylogenetics, functional annotation, and biosynthetic potential. The genome, spanning 5.6 Mb with a GC content of 34.84%, contained 5,881 protein-coding sequences, including 1,424 hypothetical proteins, and 28 genes associated with AMR. Phylogenetic analysis revealed a close genetic relationship with Bacillus cereus ATCC 14579, sharing virulence factors such as hemolysin BL (HBL), non-hemolytic enterotoxin (NHE), and cytotoxin K (CytK), all contributing to its pathogenicity. The ability to form biofilms further enhances the strain's persistence and resistance in hospital environments. AMR profiling identified genes conferring resistance to beta-lactams (e.g., BcI, BcII, BcIII), tetracyclines (tetB(P)), glycopeptides (vanY), and fosfomycin, highlighting the bacterium's capacity to resist a wide array of antibiotics. Functional annotation revealed metabolic pathways involved in iron acquisition and the biosynthesis of siderophores such as petrobactin and bacillibactin, reinforcing the bacterium's adaptability in nutrient-limited environments. Mobile genetic elements, including prophages, CRISPR-Cas systems, and transposable elements, suggest significant horizontal gene transfer (HGT), enhancing genetic plasticity and resistance spread. Pangenomic analysis, involving 125 B. cereus strains, revealed a high degree of genetic diversity and close relationships with strains from clinical, food, and agricultural environments, emphasizing the overlap between clinical and environmental reservoirs of resistance. The strain's isolation from hospital wastewater underscores the complex interplay between environmental contaminants and bacterial evolution, which fosters MDR traits. Our findings underscore the urgent need for enhanced genomic surveillance and wastewater management strategies to mitigate the spread of MDR B. cereus and AMR genes in hospital environments.},
}

*Bacillus cereus/genetics/pathogenicity/isolation & purification/drug effects
*Wastewater/microbiology
Bangladesh
*Drug Resistance, Multiple, Bacterial/genetics
Phylogeny
Hospitals
Virulence/genetics
Genome, Bacterial
Whole Genome Sequencing
Genomics/methods
Anti-Bacterial Agents/pharmacology
Virulence Factors/genetics
Humans

@article {pmid40594760,
year = {2025},
author = {Kanna, RK and Shoran, P and Yadav, M and Ahmed, MN and Burje, S and Shukla, G and Sinha, A and Hussain, MR and Khalid, S},
title = {Improving EEG based brain computer interface emotion detection with EKO ALSTM model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {20727},
pmid = {40594760},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Algorithms ; *Brain/physiology ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; },
abstract = {Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered compelling communication equipment enabling command, communication, and action without using neuromuscular or muscle channels. Various techniques for automatic emotion identification based on body language, speech, or facial expressions are nowadays in use. However, the monitoring of exterior emotions, which are easily manipulated, limits the applicability of these procedures. EEG-based emotion detection research might yield significant benefits for enhancing BCI application performance and user experience. To overcome these issues, this study proposed a novel EKO-ALSTM for emotion detection in EEG-based brain-computer interfaces. The proposed study comprises EEG-based signals that record the electrical activity of the brain connected to various emotional states, which are gathered as real-time acquired EEG signals for emotion detection. The data was pre-processed using a bandpass filter to remove unwanted frequency noise for the obtained data. Then, feature extraction is performed using DWT from pre-processed data. Specifically, the proposed approach is implemented using Python software. The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. The results demonstrated that the proposed method achieved better performance in EEG-based BCI emotion detection with an accuracy of 97.93%, a positive predictive value of 96.24%, a sensitivity of 97.81%, and a specificity of 97.75%. This study emphasizes that innovative approaches have significantly increased the accuracy of emotion identification when applied to EEG-based emotion recognition systems. Additionally, the findings suggest that integrating advanced machine learning techniques can further enhance the effectiveness and reliability of these systems in real-world applications, paving the way for more responsive and intuitive BCI technologies.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Emotions/physiology
Algorithms
*Brain/physiology
Male
Adult
Signal Processing, Computer-Assisted
Female

@article {pmid40594416,
year = {2025},
author = {Wechakarn, P and Klomchitcharoen, S and Jatupornpoonsub, T and Jirakittayakorn, N and Puttanawarut, C and Likitsuntonwong, W and Chaimongkolnukul, K and Wongsawat, Y},
title = {Modified stereotactic neurosurgery techniques for rodent surgery enhance survival and reduce surgery time in a severe traumatic brain injury model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {22166},
pmid = {40594416},
issn = {2045-2322},
mesh = {Animals ; *Brain Injuries, Traumatic/surgery/mortality ; *Stereotaxic Techniques ; Disease Models, Animal ; Rats ; *Neurosurgical Procedures/methods/instrumentation ; Male ; Operative Time ; Rats, Sprague-Dawley ; Mice ; },
abstract = {Controlled cortical impact (CCI) is the most widely used mechanical model of traumatic brain injury (TBI) in rodent brains. This neurosurgical procedure generally involves the use of a stereotaxic system, which requires reaching a specific brain region with the most accurate position possible. In this study, a modified stereotaxic system for TBI induction was developed to evaluate preclinical research in rodents for conducting neural stimulation experiments by using an implanted electrode to assist in rehabilitation after severe TBI. The proposed model aims to reduce animal mortality during surgery and alleviate the negative side effects potentially caused by prolonged anesthesia drug usage. Isoflurane is applied as an anesthetic drug before stereotaxic surgery in rodents, which promotes hypothermia in the animal body. The result showed notable improvement in rodent survival after applying an active warming pad system to prevent hypothermia. Compared with the conventional stereotaxic system, the modified CCI device with a mounted 3D-printed header significantly improved performance in the surgical procedure, decreasing the total operation time by 21.7%, especially in the Bregma‒Lambda measurement. These findings indicate the tangible capability of our modified stereotaxic system, which allows surgeons to perform stereotaxic surgery faster and lowers the risk of intraoperative mortality.},
}

Animals
*Brain Injuries, Traumatic/surgery/mortality
*Stereotaxic Techniques
Disease Models, Animal
Rats
*Neurosurgical Procedures/methods/instrumentation
Male
Operative Time
Rats, Sprague-Dawley
Mice

@article {pmid40594365,
year = {2025},
author = {Hadi-Saleh, Z and Mosleh, M and Al-Shahe, MA and Mosleh, M},
title = {Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {21202},
pmid = {40594365},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Movement/physiology ; Brain/physiology ; },
abstract = {The importance of using Brain-Computer Interface (BCI) systems based on electro encephalography (EEG) signal to decode Motor Imagery(MI) is very impressive because of the possibility of analyzing and translating brain signals related to movement intentions. This technology has many applications in the fields of medicine, rehabilitation, mind-controlled computers and assistive technologies. Despite significant progress in EEG-based BCI systems, there are challenges such as signal noise, low decoding accuracy, instability and changeability of signals, etc. To address these limitations, this article presents a new approach to classify MI from EEG signals with the help of synergistic Hilbert-Huang Transform(HHT) as pre-processing, Permutation Conditional Mutual Information Common Space Pattern (PCMICSP) as features and optimized back propagation neural network(BPNN) based on Honey Badger Algorithm(HBA) as classifier. Using the ergodicity of the HBA, along with chaotic mechanisms and global convergence, this approach encodes and optimizes the weights and thresholds of a BPNN. Initially, a comprehensive optimal solution is obtained through the honey badger algorithm. Subsequently, this solution is further refined to reach a more precise optimal state by introducing chaotic disturbances. The proposed method efficiency was confirmed through experimental analysis on a set of data of benchmark that is generally accessible of EEGMMIDB (imagery database or motor movement of EEG). Our experimental analysis outcome showed that mechanism development is important. Now, two EEG signal levels were taken into consideration: the first being an epileptic and the other being non-epileptic. The presented technique generated a max accuracy of 89.82% in comparison with other methods.},
}

*Electroencephalography/methods
Humans
*Algorithms
*Neural Networks, Computer
*Brain-Computer Interfaces
Signal Processing, Computer-Assisted
*Imagination/physiology
Movement/physiology
Brain/physiology

@article {pmid40590757,
year = {2025},
author = {Chang, T and Cho, SI and Chai, JY and Min, KD},
title = {Implications of predator species richness in terms of zoonotic spillover transmission of filovirus diseases in Africa.},
journal = {Transactions of the Royal Society of Tropical Medicine and Hygiene},
volume = {},
number = {},
pages = {},
doi = {10.1093/trstmh/traf065},
pmid = {40590757},
issn = {1878-3503},
support = {NRF-2021R1C1C2012611//National Research Foundation of Korea/ ; },
abstract = {BACKGROUND: A rich biodiversity of predators has been suggested to suppress the risk of zoonotic spillover by regulating prey abundance and behavior. We evaluated the association between predator species richness and spillover events of Ebolavirus and Marburgvirus in Africa.

METHODS: Historical records of filovirus outbreaks, along with ecological, geographical and socioeconomic factors, were considered in this environmental study. We used the maximum entropy approach (Maxent modeling) and stacked species distribution models to estimate predator species richness. Logistic regression analyses accounting for spatiotemporal autocorrelations were conducted to assess the association between predator species richness and spillover risk, adjusting for potential confounders.

RESULTS: Higher species richness of certain predators-the order Strigiformes and the family Colubridae-was associated with lower risks of Ebolavirus spillover, but not with Marburgvirus spillover. The third quartile (OR=0.02, 95% Bayesian credible interval [BCI]=0.00-0.84) and fourth quartile (OR=0.07, 95% BCI=0.00-0.42) of Strigiformes species richness, as well as the third quartile (OR=0.15, 95% BCI=0.01-0.73) and fourth quartile (OR=0.53, 95% BCI=0.03-0.85) of Colubridae species richness, were significantly associated with reduced odds of Ebolavirus index cases.

CONCLUSION: These findings support a possible role for predator species richness in suppressing zoonotic spillover.},
}

@article {pmid40590380,
year = {2025},
author = {Kushwaha, N and Mishra, N and Lalawat, RS and Padhy, PK and Gupta, VK},
title = {Automated posture adjustment system for immobilized patients using EEG signals.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10255842.2025.2523322},
pmid = {40590380},
issn = {1476-8259},
abstract = {This paper presents a Brain Computing Interface (BCI) system utilizing Electroencephalography (EEG) for human posture Identification. The proposed approach follows a structured five-step process, ensuring accurate and efficient classification. The dataset collected using the MindRove EEG device captures brain activity during four motor imagery tasks: Leftward, Rightward, Upward, and Zeroth. Pre-processing involved filtering, followed by feature extraction using a Convolutional Recurrent Denoising Autoencoder (CRDAE) model. After that Classification is performed using artificial intelligence (AI) models, including Gated Recurrent Unit (GRU) with Attention, Temporal Transformer (TT), Bidirectional Long Short-Term Memory with attention mechanisms (Bi-LSTM with AM), and proposed Graph Transformer All Attention (GTAA). The GTAA model demonstrates superior performance, achieving the highest classification accuracy among the evaluated models. Additionally, the proposed system validated against the BCI Competition IV 2a datasets and ten-fold subject cross-validation, demonstrating its reliability and efficiency for real-time BCI applications. This study underscores the potential of integrating advanced AI techniques with EEG signal measurement and instrumentation for practical implementations.},
}

@article {pmid40590025,
year = {2025},
author = {Deuel, TA and Wenlock, J and McGovern, A and Rosenthal, J and Pampin, J},
title = {Musical auditory feedback BCI: clinical pilot study of the Encephalophone.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1592640},
pmid = {40590025},
issn = {1662-5161},
abstract = {INTRODUCTION: Therapeutic strategies for patients with severe acquired motor disability are relatively limited and show variable efficacy. Innovative technologies such as brain-computer interfaces (BCIs) have been developed recently that might benefit certain types of patients.

METHODS: Here, we tested a previously described auditory BCI, the Encephalophone, which may offer new options to improve quality of life and function. Eleven subjects with acquired moderate to severe motor disability, who had lost their ability to express themselves musically, were enrolled and 10 completed a clinical pilot study of the hands-free Encephalophone brain-computer interface (BCI). Subjects were briefly instructed on the use of the Encephalophone BCI, which uses EEG measured motor imagery to allow users to generate musical notes in real time without requiring movement. Subjects then underwent a pitch-matching task, a measure of accuracy, to attempt to match a given target pitch 3 times within 10 s. They were allowed free play, where they could improvise music over a backing track. After 2-3 songs - approximately 10 min - of freely improvised playing, subjects repeated the pitch-matching task. There were 3 sessions of testing and free play per subject, within 2 weeks, with at least 1 day separating sessions.

RESULTS: All subjects, on average, improved their pitch-matching accuracy by 15.6 percentage points and increased their number of hits by 58.7% over the 3 sessions, with all subjects scoring accuracy percentages significantly above random probability (19.05%). A subjective self-reporting survey of ratings of such factors as a feeling of expressing oneself, enjoyment, discomfort, and feeling of control showed a generally favorable response.

DISCUSSION: We suggest that this training approach using an auditory BCI may provide an innovative solution to challenges in recovery from motor disability.

CLINICAL TRIAL REGISTRATION: https://research.providence.org/clinical-research, Swedish Health Services #: STUDY2017000301.},
}

@article {pmid40588550,
year = {2025},
author = {Tian, Y and Li, H and Ye, W and Yuan, X and Guo, X and Guo, F},
title = {Temperature-dependent modulation of light-induced circadian responses in Drosophila melanogaster.},
journal = {The EMBO journal},
volume = {},
number = {},
pages = {},
pmid = {40588550},
issn = {1460-2075},
support = {32171008//the National Natural Science Foundation of China/ ; 32471210//the National Natural Science Foundation of China/ ; 2023-PT310-01//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2025ZFJH01-01//the Fundamental Research Funds for the Central Universities/ ; 226-2024-00133//the Fundamental Research Funds for the Central Universities/ ; },
abstract = {Animals entrain their circadian rhythms to multiple external signals, such as light and temperature, which are integrated in master clock neurons to adjust circadian phases. However, the precise mechanisms underlying this process remain unclear. Here, we use in vivo two-photon calcium imaging while precisely controlling temperature to investigate how the Drosophila melanogaster circadian clock integrates light and temperature inputs in circadian neurons. We show that light responses modulate the circadian clock in central pacemaker neurons, with temperature acting as a fine-tuning mechanism to achieve optimal adaptation. Our results suggest that temperature-sensitive dorsal clock neurons DN1as regulate the light-induced firing of s-LNv circadian pacemaker neurons and release of the neuropeptide PDF through inhibitory glutamatergic signaling. Specifically, higher temperatures suppress s-LNv firing upon light exposure, while lower temperatures enhance this response. Behavioral analyses further indicate that lower temperatures accelerate phase adjustment, whereas higher temperatures decelerate them in response to new light-dark cycles. This novel mechanism of temperature-dependent modulation of circadian phase adjustment provides new insights into the adaptive strategies of animals for survival in fluctuating environments.},
}

@article {pmid40588517,
year = {2025},
author = {Ding, Y and Udompanyawit, C and Zhang, Y and He, B},
title = {EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5401},
pmid = {40588517},
issn = {2041-1723},
support = {NS124564, NS131069, NS127849, NS096761//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Robotics/methods/instrumentation ; *Fingers/physiology ; Male ; Adult ; Female ; *Hand/physiology ; Young Adult ; Movement/physiology ; Brain/physiology ; Neural Networks, Computer ; Imagination/physiology ; },
abstract = {Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible to a wide audience but currently face challenges, including unintuitive mappings and imprecise control. In this study, we present a real-time noninvasive robotic control system using movement execution (ME) and motor imagery (MI) of individual finger movements to drive robotic finger motions. The proposed system advances state-of-the-art electroencephalography (EEG)-BCI technology by decoding brain signals for intended finger movements into corresponding robotic motions. In a study involving 21 able-bodied experienced BCI users, we achieved real-time decoding accuracies of 80.56% for two-finger MI tasks and 60.61% for three-finger tasks. Brain signal decoding was facilitated using a deep neural network, with fine-tuning enhancing BCI performance. Our findings demonstrate the feasibility of naturalistic noninvasive robotic hand control at the individuated finger level.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
*Robotics/methods/instrumentation
*Fingers/physiology
Male
Adult
Female
*Hand/physiology
Young Adult
Movement/physiology
Brain/physiology
Neural Networks, Computer
Imagination/physiology

@article {pmid40588007,
year = {2025},
author = {Mahoney, TB and Grayden, DB and John, SE},
title = {Sub-scalp EEG for sensorimotor brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade9f1},
pmid = {40588007},
issn = {1741-2552},
abstract = {To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating its effectiveness in recording and classifying sensorimotor neural activity. Approach: Two experiments were conducted in this study. The first aim was to demonstrate the high spatial resolution of sub-scalp EEG through analysis of somatosensory evoked potentials in sheep models. The second focused on the practical application of sub-scalp EEG, classifying motor execution using data collected during a sheep behavioural experiment. Main Results: We successfully demonstrated the recording of sensorimotor rhythms using sub-scalp EEG in sheep models. Important spatial, temporal, and spectral features of these signals were identified, and we were able to classify motor execution with above-chance performance. These results are comparable to previous work that investigated signal quality and motor execution classification using ECoG and endovascular arrays in sheep models. Significance: These results suggest that sub-scalp EEG may provide signal quality that approaches that of more invasive neural recording methods such as ECoG and endovascular arrays, and support the use of sub-scalp EEG for chronic BCI applications.},
}

@article {pmid40587936,
year = {2025},
author = {Vadivelan D, S and Sethuramalingam, P},
title = {A hybrid approach for EEG motor imagery classification using adaptive margin disparity and knowledge transfer in convolutional neural networks.},
journal = {Computers in biology and medicine},
volume = {195},
number = {},
pages = {110675},
doi = {10.1016/j.compbiomed.2025.110675},
pmid = {40587936},
issn = {1879-0534},
abstract = {- Motor Imagery (MI) using Electroencephalography (EEG) is essential in Brain-Computer Interface (BCI) technology, enabling interaction with external devices by interpreting brain signals. Recent advancements in Convolutional Neural Networks (CNNs) have significantly improved EEG classification tasks; however, traditional CNN-based methods rely on fixed convolution modes and kernel sizes, limiting their ability to capture diverse temporal and spatial features from one-dimensional EEG-MI signals. This paper introduces the Adaptive Margin Disparity with Knowledge Transfer 2D Model (AMD-KT2D), a novel framework designed to enhance EEG-MI classification. The process begins by transforming EEG-MI signals into 2D time-frequency representations using the Optimized Short-Time Fourier Transform (OptSTFT), which optimizes windowing functions and time-frequency resolution to preserve dynamic temporal and spatial features. The AMD-KT2D framework integrates a guide-learner architecture where Improved ResNet50 (IResNet50), pre-trained on a large-scale dataset, extracts high-level spatial-temporal features, while a Customized 2D Convolutional Neural Network (C2DCNN) captures multi-scale features. To ensure feature alignment and knowledge transfer, the Adaptive Margin Disparity Discrepancy (AMDD) loss function minimizes domain disparity, facilitating multi-scale feature learning in C2DCNN. The optimized learner model then classifies EEG-MI images into left and right-hand movement motor imagery classes. Experimental results on the real-world EEG-MI dataset collected using the Emotiv Epoc Flex system demonstrated that AMD-KT2D achieved a classification accuracy of 96.75 % for subject-dependent and 92.17 % for subject-independent, showcasing its effectiveness in leveraging domain adaptation, knowledge transfer, and multi-scale feature learning for advanced EEG-based BCI applications.},
}

@article {pmid40587626,
year = {2025},
author = {Li, Z and Huang, Z and Li, J and Tang, Y and Li, J and Ding, X},
title = {Shear-Aligned Flexible Polarized Fluorescent Antennas for Wearable Visible Light Communications.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c06121},
pmid = {40587626},
issn = {1944-8252},
abstract = {Wearable visible light communication systems face fundamental limitations in dense multi-input multioutput configurations due to signal crosstalk between channels. Here, we demonstrate shear-aligned flexible polarized fluorescent antennas (FPFAs) fabricated through a scalable thermally assisted brush-coating induction (BCI) process. By systematically investigating the synergistic effects of ″coffee-ring″ phenomena and shear forces on halloysite nanotube alignment, we reveal the underlying physical mechanism enabling the formation of highly ordered structures with an orientation degree of 0.89. We encapsulate these structures in a sandwich configuration that maintains polarization performance while exhibiting mechanical stability, with parallel fracture strength 4.25 times higher than conventional designs. When integrated with quantum dot fluorescent conversion layers, these FPFAs achieve a 4.95-fold improvement in signal-to-noise ratio (SNR) compared to traditional receivers across wide viewing angles, even under extreme bending conditions. The resulting wearable communication system maintains 85.1% transmission accuracy at distances up to 9 m under ambient lighting, a 935% improvement over conventional approaches, with superior resilience to environmental disturbances including rain and fog. This work establishes an effective strategy for polarization multiplexing in wearable optical communications, with applications spanning healthcare monitoring, secure communications, and augmented reality interfaces in dynamic environments.},
}

@article {pmid40586134,
year = {2025},
author = {Zhang, Q and Liu, B and Wang, Z and Zhou, J and Yang, X and Zhou, Q and Zhao, Y and Li, S and Zhou, J and Wang, C},
title = {Training-Free Regulation of Grasping by Intracortical Tactile Feedback Designed via S1-M1 Communication.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e03011},
doi = {10.1002/advs.202503011},
pmid = {40586134},
issn = {2198-3844},
support = {2021ZD0201600//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 2021ZD0201604//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 82327810//National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China/ ; },
abstract = {Tactile feedback is essential for grip force control when operating a neuroprosthesis. Due to limited knowledge of cortical sensorimotor coordination, artificial feedback is mostly counterintuitive, requiring training to be associated with grasping behaviors. The current study investigates sensorimotor communication by recording neural activities from the primary sensory cortex (S1) and the primary motor cortex (M1) while macaques grasp targets of various textures and loads. Intracortical micro-stimulation is also delivered to S1 to validate the intervention of sensorimotor communication in grasping. The findings identify an S1→M1 functional pathway through which tactile information is transferred. The pathway is shared by both natural and artificial neural propagations. Moreover, it is demonstrated that sensory and motor decoding of neural activities in M1, as well as the actual grip force, are modulated by stimulation designed via S1→M1 communication, without prior training. The work provides a biomimetic strategy to design intuitive haptic feedback for brain-machine interfaces utilizing the S1→M1 pathway.},
}

@article {pmid40585760,
year = {2025},
author = {Zheng, Q and Wu, Y and Zhu, J and Feng, K and Bai, Y and Li, G and Ni, G},
title = {Applications and Challenges of Auditory Brain-Computer Interfaces in Objective Auditory Assessments for Pediatric Cochlear Implants.},
journal = {Exploration (Beijing, China)},
volume = {5},
number = {3},
pages = {20240078},
pmid = {40585760},
issn = {2766-2098},
abstract = {Cochlear implants (CI) are the premier intervention for individuals with severe to profound hearing impairment. Worldwide, an estimated 600,000 individuals have enhanced their hearing through cochlear implantation, with nearly half being children. The evaluations after implantation are crucial for appropriate clinical interventions and care. Current clinical practice lacks methods to assess the recovery of advanced auditory functions in cochlear-implanted children. Yet, recent advancements in electroencephalographic (EEG) techniques show promise in accurately evaluating auditory rehabilitation in this demographic. This review elucidates the evolution of brain-computer interface (BCI) technology for auditory assessment, focusing primarily on its application in pediatric cochlear implant recipients. Emphasis is placed on promising clinical biomarkers for auditory rehabilitation and the neural adaptability accompanying cortical adjustments after implantation. Additionally, we discuss emerging challenges and prospects in applying BCI technology to these children.},
}

@article {pmid40584823,
year = {2025},
author = {Jiang, M and Pan, X and Wang, X and Luo, Q},
title = {Will the embedded semantic radicals be activated when recognizing Chinese phonograms?.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1550536},
pmid = {40584823},
issn = {1662-5161},
abstract = {INTRODUCTION: A majority of Chinese characters are phonograms composed of phonetic and semantic radicals that serve different functions. While radical processing in character recognition has drawn significant interest, there is inconsistency regarding the semantic activation of embedded semantic radicals, and little is known about the duration of such sub-lexical semantic activation.

METHODS: Using a priming character decision task and a between-subjects design, this study examined whether semantic radicals embedded in SP phonograms (semantic radicals on the left and phonetic radicals on the right) can be automatically activated and how long such activation persists. We manipulated semantic relatedness between embedded radicals and target characters, prime frequency, and stimulus onset asynchronies (SOAs).

RESULTS: Facilitatory effects were observed on targets preceded by low-frequency primes at an SOA of 500 ms. No significant priming effects were found at SOAs of 100 ms or 1000 ms, regardless of prime frequency.

DISCUSSION: These findings suggest that sub-lexical semantic activation can occur and remain robust at 500 ms but may dissipate before 1000 ms. The study contributes valuable evidence for the automaticity and time course of embedded semantic radical processing in Chinese phonogram recognition, thereby enhancing our understanding of sub-lexical semantic processing in logographic writing systemse.},
}

@article {pmid40584523,
year = {2025},
author = {Shen, Y and Jiang, L and Lai, J and Hu, J and Liang, F and Zhang, X and Ma, F},
title = {A comprehensive review of rehabilitation approaches for traumatic brain injury: efficacy and outcomes.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1608645},
pmid = {40584523},
issn = {1664-2295},
abstract = {Traumatic Brain Injury (TBI), particularly in moderate-to-severe cases, remains a leading cause of long-term disability worldwide, affecting over 64 million individuals annually. Its complex and multifactorial nature demands an integrated, multidisciplinary rehabilitation approach to address the diverse physical, cognitive, behavioral, and psychosocial impairments that follow injury. We conducted a structured literature search using PubMed, Scopus, and Web of Science databases for suitable studies. This comprehensive review critically examines key rehabilitation strategies for TBI, including neuropsychological assessments, cognitive and neuroplasticity-based interventions, psychosocial support, and community reintegration through occupational therapy. The review emphasizes emerging technological innovations such as virtual reality, robotics, brain-computer interfaces, and tele-rehabilitation, which are expanding access to care and enhancing recovery outcomes. Furthermore, it also explores regenerative approaches, such as stem cell therapies and nanotechnology, highlighting their future potential in neurorehabilitation. Special attention is given to the importance of rigorous outcome evaluation, including standardized functional measures, neuropsychological testing, and advanced statistical methodologies to assess treatment efficacy and clinical significance. Patient-centered care is emphasized as a core element-rehabilitation plans are tailored to each individual's cognitive profile, functional needs, and life goals. Studies show this approach leads to better outcomes in executive functioning, emotional wellbeing, and community reintegration. It identifies gaps in current research, such as the lack of longitudinal studies, predictors of individualized treatment success, cost-benefit evaluations, and strategies to manage comorbidities like PTSD. Thus, combining conventional and technology-assisted rehabilitation-guided by patient-centered strategies-can enhance recovery in moderate-to-severe TBI. Future research should focus on long-term effectiveness, cost-efficiency, and scalable personalized care models.},
}

@article {pmid40584436,
year = {2025},
author = {Zamani, S and Sadeghi, J and Kamalabadi-Farahani, M and Aghayan, SN and Arabpour, Z and Djalilian, AR and Salehi, M},
title = {Comparison of cellular, mechanical, and optical properties of different polymers for corneal tissue engineering.},
journal = {Iranian journal of basic medical sciences},
volume = {28},
number = {8},
pages = {1082-1099},
doi = {10.22038/ijbms.2025.85468.18477},
pmid = {40584436},
issn = {2008-3866},
abstract = {OBJECTIVES: The invention of corneal tissue engineering is essential for vision due to the lack of effective treatments and donated corneas. Finding the right polymer is crucial for reducing inflammation, ensuring biocompatibility, and mimicking natural cornea properties.

MATERIALS AND METHODS: In this study, solvent casting and physical crosslinking (freeze-thaw cycles) were used to fabricate polymeric scaffolds of Polyvinyl alcohol, alginate, gelatin, carboxymethyl chitosan, carboxymethyl cellulose, polyacrylic acid, polyvinyl pyrrolidone, and their combinations. The mechanical evaluation of scaffolds for tension and suture ability was conducted. Biodegradability, swelling, water vapor, bacterial permeability, anti-inflammatory properties, blood compatibility, Blood Clotting Index (BCI), pH alterations, and cell compatibility with human Mesenchymal Stem cells (MSCs) were investigated with MTT. The hydrophilicity of the samples and the ability to adhere to surfaces were also compared with the contact angle and adhesive test, respectively. Finally, quantitative and qualitative analysis was used to check the transparency of the samples.

RESULTS: The mechanical strength of polyvinyl alcohol and polyvinyl pyrrolidone samples was highest, showing good suture ability. All samples had blood compatibility below 5% and cell compatibility above 75%. Polyvinyl alcohol was the most transparent at around 93%. Carboxymethyl chitosan effectively inhibited bacterial permeability, while its anti-inflammatory potential showed no significant difference.

CONCLUSION: This study aims to choose the best polymer composition for corneal tissue engineering. The selection depends on the study's goals, like mechanical strength or transparency. Comparing polymers across different dimensions provides better insight for polymer selection.},
}

@article {pmid40584269,
year = {2025},
author = {Ji, D and Yu, H and Xiao, X and Huang, Y and Zhou, X and Xu, M and Jung, TP and Ming, D},
title = {A user-friendly BCI encoding by high frequency single-frequency-SDMA SSaVEF using MEG.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {101},
doi = {10.1007/s11571-025-10279-1},
pmid = {40584269},
issn = {1871-4080},
abstract = {Magnetoencephalography (MEG) delivers high spatial resolution and superior detection performance for high-frequency signals compared to Electroencephalography (EEG). Therefore, researchers can leverage MEG for high-frequency steady-state asymmetric visual evoked potential (SSaVEP). Current SSaVEP encoding typically uses low-frequency stimulation with relatively large stimulus areas, hindering the applicability of this encoding method in user-friendly brain-computer interface (BCI) systems. This study introduces an ultra critical flicker frequency (ultra-CFF) single-frequency-SDMA steady-state asymmetric visual evoked field (SSaVEF) encoding powered by MEG and presents an eight-command SSaVEF-BCI system. The BCI system features a 60 Hz SSVEF visual stimulus landmark and eight visual targets spaced 45° apart. Ten participants took part in the offline experiments, during which data from 41 channels in the occipital region were collected. This study analyzed the spatiotemporal characteristics, frequency-space characteristics, signal-to-noise ratio, and other features of the SSaVEF signals. We also evaluated the system's performance using the multi-DCPM algorithm. Using the multi-DCPM algorithm, the system achieved an impressive average classification accuracy of 81.65% with 4-s length data. With a data length of 1 s, the system achieved an average Information Transfer Rate (ITR) of 32.05 bits/min, with the highest individual ITR reached an astonishing 64.45 bits/min. This study represents the exploration of a high-frequency spatial encoding SSVEF-BCI system based on MEG. The results demonstrate MEG's feasibility and potential of applying MEG in such BCI systems, providing both theoretical and practical value for the further development and implementation of future BCI systems.},
}

@article {pmid40584164,
year = {2025},
author = {Sharma, MK and Chaudhary, S and Shenoy, S},
title = {Development and testing of range of motion driven motor unit recruitment device for knee rehabilitation: A randomized controlled trial.},
journal = {MethodsX},
volume = {14},
number = {},
pages = {103382},
doi = {10.1016/j.mex.2025.103382},
pmid = {40584164},
issn = {2215-0161},
abstract = {Existing research on neuromuscular electrical stimulation (NMES) identifies two primary control approaches: therapist-operated systems and participant-controlled systems. Therapist-operated NMES devices typically employ switches and potentiometers for control, whereas participant-controlled systems offer diverse input methods, including switches, buttons, joysticks, electromyography electrodes, voice-activated commands, and sip-and-puff devices. A critical limitation of current NMES technology lies in its failure to mimic the body's natural muscle recruitment process during electrical stimulation, resulting in premature fatigue and diminished user engagement. A particularly significant drawback is the absence of joint range-of-motion dependency observed during voluntary movements and active involvement of participant. This limitation prevents precise control over spatial and temporal parameters, such as modulating motor unit recruitment relative to joint position, during neuromuscular rehabilitation. Furthermore, existing devices cannot accurately reproduce the co-contraction dynamics and reciprocal activation patterns seen in synergistic, agonist, and antagonist muscle groups during natural movement. Addressing these challenges requires developing innovative NMES technology capable of activating the neuromuscular system while replicating natural voluntary recruitment patterns. Such advancements would not only improve muscle strengthening outcomes but also enhance participant adherence through more effective cortical and peripheral neuromuscular engagement.•Development of neuromuscular electrical stimulation (NMES) device to replicate natural neuromuscular activation patterns through bio-inspired stimulation protocols.•Engineered to mitigate existing limitations of conventional NMES systems, optimizing therapeutic applications for neuromuscular re-education and functional recovery.•Integrates muscle synergy principles, enabling synchronized synergistic, agonist and antagonist activation for enhanced cortical and peripheral neuromuscular engagement and optimize functional rehabilitation outcomes.•Advances rehabilitation strategies by combining dual focus on muscular reconditioning and neural adaptation for holistic recovery.•Demonstrates potential to amplify strength gains while fostering neuroplasticity, supporting long-term functional recovery in neuromuscular rehabilitation.},
}

@article {pmid40581689,
year = {2025},
author = {Olza, A and Soto, D and Santana, R},
title = {Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {17},
pmid = {40581689},
issn = {2198-4018},
support = {IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; KK-2023/00090//Elkartek/ ; KK-2023/00090//Elkartek/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; CEX2020-001010-S//Severo Ochoa programme/ ; CEX2020-001010-S//Severo Ochoa programme/ ; },
abstract = {In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community.},
}

@article {pmid40581220,
year = {2025},
author = {Alsamri, J and Alamgeer, M and Alamri, MZ and Ghaleb, M and Asklany, SA and Almansour, H and Alsafari, S and Alghamdi, EA},
title = {Longitudinal EEG-Based Assessment of Neuroplasticity and Adaptive Responses to Transcranial Focused Ultrasound Stimulation.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110521},
doi = {10.1016/j.jneumeth.2025.110521},
pmid = {40581220},
issn = {1872-678X},
abstract = {BACKGROUND: An emerging non-invasive neuromodulation technique named Transcranial-focused ultrasound stimulation (tFUS) offered several advantages than the conventional methods in terms of high spatial precision and penetration depth. In neurological disorders, this emerging method have gained a lot of attention, because of has the potential for therapeutic modulation of brain activity. Then, lack of standardized, Real-Time (RT) assessment protocols will result in unclear comprehehnsion regarding the way the repeated tFUS applications may impacts the neuroplasticity and adaptive brain responses in a long-term. Here, the short-term and long-term neuroplastic modifications were effectively identified by the the longitudinal integration of EEG biomarkers with tFUS stimulation sessions. An adaptive modulation strategies customized for individual neural responses are also facilitated by this hypothesis.

NEW METHODS: To integrate the tFUS with high-resolution electroencephalogram (EEG) monitoring in many sessions, Integrated Longitudinal Evaluation Protocol (ILEP) model was suggested in this study. To extract amplitude, latency, spectral dynamics, and connectivity features from evoked potentials, pre-, during-, and post-stimulation EEG signals were identified by the protocol. Then, for monitoring neuroadaptive trajectories over time, the intrgration of the statistical modeling and neural network (NN)-based pattern recognition was employed, and it will assist in analysing those features. For the purpose of differentiating the short-term oscillatory effects from long-term neuroplastic shifts, the following ways will helps in processing the EEG signals: time-frequency decomposition, event-related potential (ERP) analysis, and machine learning (ML) classifiers. Here, the subject-specific response patterns and temporal evolution of brain dynamics were effectively detected by the application of the Deep learning (DL) models.

RESULTS ANALYSIS: After the tFUS, both the short-term and long-term modifications in brain activity were effectively detected by the application of ILEP, and it was demonstrated by the outcomes of the simulation and empirical data. Here, the location-specific, session-dependent EEG modifications are consistent with the adaptive neuroplastic processes, and it was revealed by the outcomes of the simulation. Then, accurate neuroadaptive signals were separated from noise and temporary conditions, and it was facilitated by the potential of the model.

A dynamic, session-over-session monitoring of brain responses was facilitated by the ILEP model. But static images was offered by those conventional methods. With an integration of closed-loop feedback and advanced neural modelling, the suggested model executes better than the conventional methods. This suggested model also facilitates in offering a customized neuromodulation therapies.

CONCLUSION: For monitoring the neuroplastic modifications induced by tFUS,this suggested ILEP model becomes an effective, sacalable. So, this suggested model facilitates an adaptive assessment model for that tracking, and it was demonstrated in this study. The future, RT, closed-loop neuromodulation systems in therapeutic and cognitive enhancement contexts may get benefits from the integration of EEG feedback mechanisms in the suggested model.},
}

@article {pmid40579374,
year = {2025},
author = {Yang, C and Zhang, L and Liu, J and Li, K and Li, S and Yang, Z and Bishop, JR and Deng, W and Yao, L and Lui, S and Gong, Q},
title = {More Severe Brain Network Hierarchy Disorganization in Treatment-Naive Deficit Compared to Non-deficit Schizophrenia and Underlying Neurotransmitter Associations.},
journal = {Schizophrenia bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1093/schbul/sbae231},
pmid = {40579374},
issn = {1745-1701},
support = {82102007//National Natural Science Foundation of China/ ; 82120108014//National Natural Science Foundation of China/ ; 82071908//National Natural Science Foundation of China/ ; 82202110//National Natural Science Foundation of China/ ; 2022YFC2009901//National Key Research and Development Program of China/ ; 2022YFC2009900//National Key Research and Development Program of China/ ; 2021JDTD0002//Sichuan Science and Technology Program/ ; 2022-YF09-00062-SN//Chengdu Science and Technology Office, major technology application demonstration project/ ; 2022-GH03-00017-HZ//Chengdu Science and Technology Office, major technology application demonstration project/ ; ZYGD23003//West China Hospital, Sichuan University/ ; ZYAI24010//West China Hospital, Sichuan University/ ; ZYGX2022YGRH008//Fundamental Research Funds for the Central Universities/ ; GZB20240493//Postdoctoral Fellowship Program of CPSF/ ; T2019069//Humboldt Foundation Friedrich Wilhelm Bessel Research Award and Chang Jiang Scholars/ ; },
abstract = {BACKGROUND AND HYPOTHESIS: Deficit schizophrenia (DS) represents a distinct entity characterized by primary and enduring negative symptoms, yet the neurobiological differences between DS and non-DS (NDS) remain undetermined. Using a gradient-based approach, we hypothesize that DS and NDS will exhibit convergent and divergent brain functional hierarchy patterns, each with a specific underlying neurotransmitter architecture.

STUDY DESIGN: Resting-state functional magnetic resonance imaging images were acquired from 44 treatment-naive DS, 55 treatment-naive NDS, and 60 matched healthy controls (HCs). Gradient metrics were calculated using the BrainSpace toolbox. The spatial correlation between gradient abnormalities in DS or NDS and density maps of 10 neurotransmitters derived by the JuSpace toolbox was analyzed to link the neuroimaging to underlying neurotransmitter information.

STUDY RESULTS: Both DS and NDS exhibited compressed gradient patterns compared to HC, suggesting reduced network differentiation, with more severe disorganization in DS. The ventral attention network was associated with depression symptoms in DS, whereas the visual network was related to total, general, and paranoid symptom scores in NDS. Moreover, spatial correlation of neurotransmitter analysis revealed that the gradient alterations of DS were primarily related to the serotonergic system while those of NDS were predominantly associated with the dopamine system.

CONCLUSIONS: The study suggests that independent from the potential effects of antipsychotic medication, DS and NDS are characterized by different neuropathology in brain hierarchy patterns, potentially linked to neurochemical metabolic distinction. Our findings support the hypothesis that DS is a distinct subtype versus NDS from neurodevelopmental perspective.},
}

@article {pmid40578761,
year = {2025},
author = {Brands, R and Fuchs, L and Seyffer, JM and Bajcinca, N and Bartsch, J and Peuker, UA and Schmidt, V and Thommes, M},
title = {Penetration depth and effective sample size characterization of UV/Vis radiation into pharmaceutical tablets.},
journal = {Journal of pharmaceutical sciences},
volume = {},
number = {},
pages = {103889},
doi = {10.1016/j.xphs.2025.103889},
pmid = {40578761},
issn = {1520-6017},
abstract = {The pharmaceutical industry is moving from off-line to real-time release testing (RTRT) to enhance quality while reducing costs. UV/Vis spectroscopy has emerged as a promising tool for RTRT given its simplicity, sensitivity and cost-effectiveness. Nevertheless, the effective sample size must be characterized in relation to the penetration depth to justify its representativeness and suitability for RTRT. In this study, bilayer tablets were produced using a hydraulic tablet press. The lower layer contained titanium dioxide and microcrystalline cellulose (MCC), while the upper layer consisted of MCC, lactose or a combination with theophylline. The thickness of the upper layer was stepwise increased. Spectra from 224 to 820 nm were recorded with an orthogonally aligned UV/Vis probe. Thereby, the experimental penetration depth reached up to 0.4 mm, while the Kubelka-Munk model yielded a theoretical maximum penetration depth of 1.38 mm. Based on these values, the effective sample sizes were determined. Considering a parabolic penetration profile, the maximum volume was 2.01 mm[3]. The results indicated a wavelength and particle size dependency. Micro-CT analysis confirmed the even distribution of the API in the tablets proving the sufficiency of the UV/Vis sample size. Consequently, UV/Vis spectroscopy is a reliable alternative for RTRT in tableting.},
}

@article {pmid40578508,
year = {2025},
author = {Metin, S and Altan, H and Tercan, E and Dedeoglu, BG and Gurdal, H},
title = {DUSP1 protein's impact on breast cancer: Anticancer response and sensitivity to cisplatin.},
journal = {Biochimica et biophysica acta. Gene regulatory mechanisms},
volume = {},
number = {},
pages = {195103},
doi = {10.1016/j.bbagrm.2025.195103},
pmid = {40578508},
issn = {1876-4320},
abstract = {Dual-Specificity Phosphatase 1 (DUSP1) modulates the activity of members of the Mitogen-Activated Protein Kinase (MAPK) family, including p38, JNK, and ERK1/2, which affects various cellular functions in cancer. Moreover, DUSP1 is known to influence the outcomes of cancer chemotherapy. This study aimed to reduce DUSP1 protein expression using CRISPR/Cas9 and siRNA and assess its effects on cell proliferation, migration, and tumor growth potential in triple-negative breast cancer (TNBC) cells. We examined the expression levels of p38, JNK, and ERK1/2, along with their phosphorylated forms, and investigated DUSP1's influence to cisplatin sensitivity. Our findings revealed that the downregulation of DUSP1 expression inhibited the proliferation, migration, and tumor growth potential of TNBC cells. Additionally, BCI, an inhibitor of DUSP1/6, demonstrated anti-proliferative effects on these cells. Decreasing the expression of DUSP1 increased the phosphorylation ratio of p38 and JNK, but not ERK1/2. Moreover, the anticancer response induced by cisplatin was enhanced by reducing DUSP1 expression or by treating the cells with BCI. Notably, cisplatin treatment increased p38 phosphorylation, which was significantly augmented by reduced DUSP1 expression. We also demonstrated that the DUSP1 inhibition-induced anticancer response in these cells predominantly relied on p38 activity. These findings contribute to a better understanding of the role of DUSP1 in breast cancer and offer insights into potential therapeutic strategies targeting DUSP1 to enhance the efficacy of cisplatin treatment. Our study highlights that decreased DUSP1 protein expression and activity mediates an anticancer response and increases the sensitivity of MDA-MB231 cells to cisplatin by regulating p38.},
}

@article {pmid40578406,
year = {2025},
author = {Cao, Y and Chen, Z and Yin, Y and Kang, X and Zhang, Y and Xu, Z and Yang, X and Yang, B and He, Q and Yan, H and Luo, P},
title = {Autophagy-dependent hepatocyte apoptosis mediates gilteritinib-induced hepatotoxicity.},
journal = {Toxicology letters},
volume = {410},
number = {},
pages = {189-196},
doi = {10.1016/j.toxlet.2025.06.018},
pmid = {40578406},
issn = {1879-3169},
abstract = {Gilteritinib, a dual FLT3/AXL inhibitor, is clinically effective for relapsed/refractory FLT3-mutated acute myeloid leukemia (AML) but is limited by severe hepatotoxicity. This study investigates the molecular mechanisms underlying gilteritinib-induced liver injury, focusing on the interplay between autophagy and apoptosis. In vitro and in vivo models, including human hepatocyte HL-7702 cells and C57BL/6 J mice, were employed. Gilteritinib treatment significantly upregulated autophagy markers (LC3-II) and induced autophagosome formation, as confirmed by western blot, TEM, and mCherry-GFP-LC3 reporter assays. Concurrently, apoptosis markers (cleaved-PARP, cleaved-Caspase3, Annexin V/PI staining) increased dose- and time-dependently. Pharmacological inhibition of autophagy with autophagy inhibitor 3-methyladenine (3-MA, 5 mM) or gene silence of Atg7 attenuated apoptosis, mitochondrial membrane potential loss, and ROS overproduction, while autophagy induction by Torin1 (100 nM) exacerbated hepatocyte death. In vivo, gilteritinib-treated mice exhibited elevated serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) levels, alongside histopathological damage, all of which were mitigated in Atg7-deficient mice. These findings demonstrate that gilteritinib triggers excessive autophagy, which drives hepatocyte apoptosis and liver injury. Targeting autophagy pathways, represents a potential therapeutic strategy to alleviate gilteritinib-induced hepatotoxicity, enabling safer clinical use of this vital AML therapy. This study elucidates a critical autophagy-apoptosis axis in drug-induced liver injury and provides actionable insights for managing adverse effects of targeted cancer therapies.},
}

@article {pmid40578388,
year = {2025},
author = {Lin, Z and Jiang, X and Dai, C and Jia, F},
title = {Towards real time efficient and robust ECoG decoding for mobile brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade917},
pmid = {40578388},
issn = {1741-2552},
abstract = {OBJECTIVE: Decoding locomotion-related brain activities from electrocorticographic (ECoG) signals is essential in brain-computer interfaces (BCIs). Most previous ECoG decoders are computationally demanding and sensitive to noises/outliers. Mobile and robust BCIs are particularly important for physically disabled patients to restore motor ability in outdoor scenarios, where the processing pipeline should be implemented efficiently using constrained computation resources. In this work, we aim to explore the optimal pipeline for mobile BCI decoding.

APPROACH: We comprehensively evaluated the trade-off between the decoding precision, computational efficiency and robustness of diverse decoding algorithms on a combined ECoG dataset of 12 subjects conducting individual finger movement, including partial-least-square and their N-way variants, Bayesian ridge regression, least absolute shrinkage and selection operator, support vector regression, neural networks with diverse architectures, and random forest (RF). We further explored the feature optimization technique for selected models by using their inherent model explainability. We also compared the decoding performance of updatable algorithms when the data is split into multiple batches and used sequentially.

MAIN RESULTS: The RF model, not valued by previous studies, can achieve the best trade-off between precision and efficiency, achieving an average Pearson's correlation coefficient (r) of 0.466 with only 0.5K FLOPs per inference and a model size of 900KiB. We also demonstrate the inherent superior robustness of RF model on corrupted ECoG electrodes, with a > 2× decoding precision on noisy signals compared with all state-of-the-art deep neural networks. The optimized RF processing pipeline was deployed on a STM32-based embedded platform with only a 15.2 ms computation delay.

SIGNIFICANCE: In this study, we systematically explored the performance and efficiency of ECoG decoding algorithms in finger movement analysis. The proposed decoding pipeline is implemented on a compact embedded platform to achieve low-latency, power-efficient real-time decoding. This research accelerates the translation of mobile BCI into real-life practices.},
}

@article {pmid40578216,
year = {2025},
author = {Zhang, H and Wang, H and An, J and Zheng, S and Wu, D},
title = {A lightweight spiking neural network for EEG-based motor imagery classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107741},
doi = {10.1016/j.neunet.2025.107741},
pmid = {40578216},
issn = {1879-2782},
abstract = {Spiking neural networks (SNNs) aim to simulate the human brain neural network, using sparse spike event streams for effective and energy-efficient spatio-temporal signal processing. This paper proposes a lightweight SNN model for electroencephalogram (EEG) based motor imagery (MI) classification, a classical brain-computer interface paradigm. The model has three desirable characteristics: (1) it has a brain-inspired architecture; (2) it is energy efficient; and, (3) it is dataset agnostic. Within-subject and cross-subject experiments on three public datasets demonstrated the superiority of our SNN model over four classical convolutional neural network based models in EEG based MI classification.},
}

@article {pmid40576544,
year = {2025},
author = {Wu, Y and Bao, K and Liang, J and Li, Z and Shi, Y and Tang, R and Xu, K and Wei, M and Chen, Z and Jian, J and Luo, Y and Tang, Y and Deng, Q and Dai, H and Sun, C and Zhang, W and Lin, H and Zhang, K and Li, L},
title = {Photonic Interfaces: an Innovative Wearable Sensing Solution for Continuous Monitoring of Human Motion and Physiological Signals.},
journal = {Small methods},
volume = {},
number = {},
pages = {e2500727},
doi = {10.1002/smtd.202500727},
pmid = {40576544},
issn = {2366-9608},
support = {10300000H062401/001//Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China/ ; 2024SDXHDX0005//"Pioneer" and "Leading Goose" Key Research and Development Program of Zhejiang Province/ ; 12104375//National Natural Science Foundation of China/ ; 62175202//National Natural Science Foundation of China/ ; 2024C03150//Key Research and Development Program of Zhejiang Province/ ; 2023GD003/110500Y0022303//Key Project of Westlake Institute for Optoelectronics/ ; },
abstract = {Flexible integrated photonic sensors are gaining prominence in intelligent wearable sensing due to their compact size, exceptional sensitivity, rapid response, robust immunity to electromagnetic interference, and the capability to enable parallel sensing through optical multiplexing. However, integrating these sensors for practical applications, such as monitoring human motions and physiological activities together, remains a significant challenge. Herein, it is presented an innovative fully packaged integrated photonic wearable sensor, which features a delicately designed flexible necklace-shaped microring resonator (MRR), along with a pair of grating couplers (GCs) coupled to a fiber array (FA). The necklace-shaped MRR is engineered to minimize waveguide sidewall-induced scattering loss, with a measured intrinsic quality factor (Qint) of 1.68 × 10[5], ensuring highly sensitive and precise signal monitoring. GCs and FA enhance the seamless wearability of devices while maintaining superior sensitivity to monitor various human motions and physiological signs. These are further classified signals using machine learning algorithms, achieving an accuracy rate of 97%. This integrated photonic wearable sensor shows promise for human-machine interfaces, touch-responsive wearable monitors, and artificial skin, especially in environments susceptible to electromagnetic interference, such as intensive care units (ICUs) and spacecraft. This work significantly advances the field of smart wearable technology.},
}

@article {pmid40575493,
year = {2025},
author = {Mehta, D},
title = {Brain-Computer Interface tool use and the Contemplation Conundrum: a blueprint of mental action, agency, and control.},
journal = {Oxford open neuroscience},
volume = {4},
number = {},
pages = {kvaf002},
pmid = {40575493},
issn = {2753-149X},
abstract = {This paper approaches the role of intentional action in brain-computer interface (BCI) tool use to allow for an ethical discourse regarding the development and usage of neurotechnology. The exploration of mental actions and user control in BCI tool use brings us closer to understanding the philosophical underpinnings of intentions and agency for BCI-mediated actions. The author presents that under some theories of intentional action, certain BCI-mediated overt movements qualify as both voluntary and unintentional. This plausibly magnifies the ethical considerations surrounding BCI tool use. This problem is referred by the author as the contemplation conundrum. Thus, the paper proposes research scope for the neural correlates of intention formation and the neural correlates of imagination aimed at clarifying implementational control and safeguarding privacy of thought in BCI tool use.},
}

@article {pmid40574626,
year = {2025},
author = {Kaszás, A and Meszéna, D and Fiáth, R and Slézia, A and Ulbert, I and Katona, G},
title = {Capturing the Electrical Activity of all Cortical Neurons: Are Solutions Within Reach?.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e06225},
doi = {10.1002/advs.202506225},
pmid = {40574626},
issn = {2198-3844},
support = {TKP2021-EGA-42//Thematic Programme of Excellence/ ; NAP2022-I-2/2022//Hungarian Brain Research Program/ ; RRF-2.3.1-21-2022-00015//Pharmaceutical Research and Development Laboratory Project/ ; HUN-REN-HAZAHIVO-2023//Hungarian Research Network/ ; KSZF-174/2023//Hungarian Research Network/ ; 2019-2.1.7-ERA-NET-2021-00023//ERA-NET/ ; //Bolyai János Scholarship of the Hungarian Academy of Sciences/ ; 150574//National Research, Development and Innovation Office/ ; PD143582//National Research, Development and Innovation Office/ ; },
abstract = {Recent advancements in high-density implantable intracortical electrode technology have significantly improved neural interfaces for both research and clinical applications. However, a significant challenge persists: scaling up these devices to achieve recording of nearly all single-unit activity across large brain volumes. This critical review explores recent progress in neural electrode design, focusing on the challenges of achieving scalable solutions for this ambitious goal. The physical and technical constraints of both rigid and flexible probes are addressed, highlighting the limitations imposed by shank stiffness, mechanical tissue damage, and foreign body response. It is identified that the physics of inserting the electrodes into the brain tissue poses a fundamental constraint, which inherently restricts achievable electrode density. Biohybrid strategies, integrating biological and synthetic components, have shown promise, but they have yet to overcome the major challenges necessary to achieve a scalable functional interface. It is concluded that, given the current limitations of available techniques, there is a pressing need to explore fundamentally novel approaches to realize the vision of recording the electrical activity of every cortical neuron within the brain.},
}

@article {pmid40573719,
year = {2025},
author = {Safarov, F and Kutlimuratov, A and Khojamuratova, U and Abdusalomov, A and Cho, YI},
title = {Enhanced AlexNet with Gabor and Local Binary Pattern Features for Improved Facial Emotion Recognition.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {12},
pages = {},
doi = {10.3390/s25123832},
pmid = {40573719},
issn = {1424-8220},
support = {20022362//Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy in 2024/ ; 2410003714//Establishment of standardization basis for BCI and AI Interoperability/ ; },
mesh = {Humans ; *Emotions/physiology ; *Facial Recognition/physiology ; *Facial Expression ; Algorithms ; Deep Learning ; *Pattern Recognition, Automated/methods ; Face/physiology ; *Automated Facial Recognition/methods ; Neural Networks, Computer ; Convolutional Neural Networks ; },
abstract = {Facial emotion recognition (FER) is vital for improving human-machine interactions, serving as the foundation for AI systems that integrate cognitive and emotional intelligence. This helps bridge the gap between mechanical processes and human emotions, enhancing machine engagement with humans. Considering the constraints of low hardware specifications often encountered in real-world applications, this study leverages recent advances in deep learning to propose an enhanced model for FER. The model effectively utilizes texture information from faces through Gabor and Local Binary Pattern (LBP) feature extraction techniques. By integrating these features into a specially modified AlexNet architecture, our approach not only classifies facial emotions more accurately but also demonstrates significant improvements in performance and adaptability under various operational conditions. To validate the effectiveness of our proposed model, we conducted evaluations using the FER2013 and RAF-DB benchmark datasets, where it achieved impressive accuracies of 98.10% and 93.34% for the two datasets, with standard deviations of 1.63% and 3.62%, respectively. On the FER-2013 dataset, the model attained a precision of 98.2%, a recall of 97.9%, and an F1-score of 98.0%. Meanwhile, for the other dataset, it achieved a precision of 93.54%, a recall of 93.12%, and an F1-score of 93.34%. These results underscore the model's robustness and its capability to deliver high-precision emotion recognition, making it an ideal solution for deployment in environments where hardware limitations are a critical concern.},
}

Humans
*Emotions/physiology
*Facial Recognition/physiology
*Facial Expression
Algorithms
Deep Learning
*Pattern Recognition, Automated/methods
Face/physiology
*Automated Facial Recognition/methods
Neural Networks, Computer
Convolutional Neural Networks

@article {pmid40573479,
year = {2025},
author = {Sasatake, Y and Matsushita, K},
title = {P300 ERP System Utilizing Wireless Visual Stimulus Presentation Devices.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {12},
pages = {},
doi = {10.3390/s25123592},
pmid = {40573479},
issn = {1424-8220},
support = {JPMJSP2125//JST SPRING/ ; none//THERS/ ; },
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Wireless Technology/instrumentation ; Brain-Computer Interfaces ; Male ; Adult ; *Photic Stimulation/methods ; Electroencephalography/methods ; Female ; Young Adult ; },
abstract = {The P300 event-related potential, evoked by attending to specific sensory stimuli, is utilized in non-invasive brain-computer interface (BCI) systems and is considered the only interface through which individuals with complete paralysis can operate devices based on their intention. Conventionally, visual stimuli used to elicit P300 have been presented using displays; however, placing a display directly in front of the user obstructs the field of view and prevents the user from perceiving their surrounding environment. Moreover, every time the user changes posture, the display must be repositioned accordingly, increasing the burden on caregivers. To address these issues, we propose a novel system that employs wirelessly controllable LED visual stimulus presentation devices distributed throughout the surrounding environment, rather than relying on traditional displays. The primary challenge in the proposed system is the communication delay associated with wireless control, which introduces errors in the timing of stimulus presentation-an essential factor for accurate P300 analysis. Therefore, it is necessary to evaluate how such delays affect P300 detection accuracy. The second challenge lies in the variability of visual stimulus strength due to differences in viewing distance caused by the spatial distribution of stimulus devices. This also requires the validation of its impact on P300 detection. In Experiment 1, we evaluated system performance in terms of wireless communication delay and confirmed an average delay of 352.1 ± 30.9 ms. In Experiment 2, we conducted P300 elicitation experiments using the wireless visual stimulus presentation device under conditions that allowed the precise measurement of stimulus presentation timing. We compared P300 waveforms across three conditions: (1) using the exact measured stimulus timing, (2) using the stimulus timing with a fixed compensation of 350 ms for the wireless delay, and (3) using the stimulus timing with both the 350 ms fixed delay compensation and an additional pseudo-random error value generated based on a normal distribution. The results demonstrated the effectiveness of the proposed delay compensation method in preserving P300 waveform integrity. In Experiment 3, a system performance verification test was conducted on 21 participants using a wireless visual presentation device. As a result, statistically significant differences (p < 0.01) in amplitude between target and non-target stimuli, along with medium or greater effect sizes (Cohen's d: 0.49-0.61), were observed under all conditions with an averaging count of 10 or more. Notably, the P300 detection accuracy reached 85% with 40 averaging trials and 100% with 100 trials. These findings demonstrate that the system can function as a P300 speller and be utilized as an interface equivalent to conventional display-based methods.},
}

Humans
*Event-Related Potentials, P300/physiology
*Wireless Technology/instrumentation
Brain-Computer Interfaces
Male
Adult
*Photic Stimulation/methods
Electroencephalography/methods
Female
Young Adult

@article {pmid40571414,
year = {2025},
author = {Yang, L and Li, M and Yang, L and Wang, Z and Shang, Z},
title = {Hippocampal LFP responses during pigeon homing flight in outdoors.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0185-25.2025},
pmid = {40571414},
issn = {1529-2401},
abstract = {The hippocampal formation (HF) plays a key role in avian spatial navigation. Previous studies suggest that the HF may serve different functions at various stages in pigeons' long-distance outdoor homing flight. However, it remains unclear whether the HF exhibits specific neural responses during these stages. In this study, we employed a wearable bimodal data recording system to simultaneously capture flight trajectories and hippocampal local field potential (LFP) signals of pigeons (either sex) during outdoor homing navigation. Our results revealed significant differences in hippocampal neural responses across the initial decision-making (DM) and en route navigation (ER) stages. Specifically, elevated LFP power in theta (4-12 Hz) and beta (12-30 Hz) bands was detected during the DM stage compared to the ER stage, while the high gamma (60-120 Hz) band exhibited the opposite pattern. In addition, we examined typical theta-beta phase-amplitude coupling (PAC) during the ER stage. Additionally, stage-specific hippocampal responses remained consistent across release sites. Notably, the difference in hippocampal responses across stages diminished along with the accumulation of homing experience. These results offer new insights into the role of the avian HF in homing flight navigation and suggest parallels between avian and mammalian hippocampal mechanisms in spatial learning.Significance Statement It remains unclear whether the hippocampal formation (HF) exhibits specific neural responses during various stages in the long-distance outdoor navigation of pigeons. By recording hippocampal local field potentials (LFPs) and positional data during natural outdoor flights, we reveal distinct neural response patterns that differentiate between initial decision-making and sustained navigation stages. We detected band-specific power and coupling responses between different navigation stages, consistent across multiple release sites. Additionally, we found that the LFP responses differences across stages gradually diminish along with the accumulation of the homing experience. Our study offers new insights into the role of the avian HF in outdoor homing flight.},
}

@article {pmid40567082,
year = {2025},
author = {Lachkar, S and Ibrahimi, A and Boualaoui, I and Sayegh, HE and Nouini, Y},
title = {Botulinum toxin A in idiopathic overactive bladder: a narrative review of 5410 cases.},
journal = {The Canadian journal of urology},
volume = {32},
number = {3},
pages = {145-165},
pmid = {40567082},
issn = {1488-5581},
mesh = {Humans ; *Urinary Bladder, Overactive/drug therapy ; *Botulinum Toxins, Type A/therapeutic use/adverse effects ; *Neuromuscular Agents/therapeutic use/adverse effects ; Treatment Outcome ; },
abstract = {INTRODUCTION: When conservative treatments fail, botulinum toxin A (BoNT-A) is an option for refractory idiopathic overactive bladder (OAB). This review evaluates the efficacy, safety, and predictive factors for BoNT-A in this situation.

MATERIALS AND METHODS: A literature search up to January 2025 was performed using PubMed, Google Scholar, and Embase to assess efficacy, safety, and predictors of adverse events (AE) related to BoNT-A. The risk of bias was assessed using the Risk of Bias 2 (RoB 2) tool for randomized studies and the Critical Appraisal Skills Programme (CASP) checklist for cohort studies. The quality of the review was evaluated based on the Oxford criteria, following the Strengthening the Assessment of Narrative Review Articles (SANRA) guidelines, and by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews.

RESULTS: 31 studies were included, involving 5410 patients. BoNT-A improves OAB symptoms even after reinjections. Higher doses do not enhance efficacy but increase AE. AE includes high post-void residual (PVR), clean intermittent self-catheterization (CISC), and Urinary Tract Infection (UTI). Predictors of CISC include age, male gender, hysterectomy, ≥3 vaginal deliveries, mixed incontinence, prior mid-urethral sling (MUS), high PVR, low Pressure at Pdet at First Micturition (PIP1) in women, low Bladder Compliance Index (BCI) in men, and high Bladder Outlet Obstruction Index (BOOI). Diabetes and heart failure increase PVR. UTIs are more frequent in women and men with benign prostatic hyperplasia, with CISC increasing the risk fivefold. Severe complications are rare. Predictors of poor response include male gender, high BOOI, low urinary flow, and diabetes.

DISCUSSION: BoNT-A is effective for OAB, especially for incontinence. AE is dose-dependent and limits treatment adherence. Their link with poor response remains unclear.

CONCLUSION: BoNT-A effectively treats refractory idiopathic OAB, improving symptoms and quality of life with repeated injections.},
}

Humans
*Urinary Bladder, Overactive/drug therapy
*Botulinum Toxins, Type A/therapeutic use/adverse effects
*Neuromuscular Agents/therapeutic use/adverse effects
Treatment Outcome

@article {pmid40566931,
year = {2025},
author = {Lin, K and Chen, J and Pan, J and Wang, R and Wu, S and Wen, W and Li, Y and Wang, L and Yuan, F},
title = {Electro-Acupuncture to Treat Disorder of Consciousness (AcuDoc): Study Protocol for a Randomized Sham-Controlled Trial.},
journal = {Brain and behavior},
volume = {15},
number = {6},
pages = {e70637},
pmid = {40566931},
issn = {2162-3279},
support = {//Health Commission of Guangzhou City/ ; //NATCM's Project of High-level Construction of Key TCM Disciplines/ ; //Guangzhou Municipal Science and Technology Bureau/ ; //2023A04J0473/ ; },
mesh = {Humans ; *Electroacupuncture/methods ; Adult ; *Brain Injuries, Traumatic/complications/therapy/physiopathology ; *Consciousness Disorders/therapy/etiology/physiopathology ; Female ; Male ; Young Adult ; Middle Aged ; Randomized Controlled Trials as Topic ; Electroencephalography ; },
abstract = {BACKGROUND: Treatment of disorders of consciousness (DOC) remains a clinical challenge. Electroacupuncture (EA) was shown to have the potential to promote the recovery of consciousness. This trial aims to explore the therapeutic effects and mechanisms of EA in patients with DOC due to traumatic brain injury (TBI) through a multimodal approach.

METHODS: A total of 50 adult patients with DOC due to TBI and 25 healthy subjects will be enrolled in the study. Patients enrolled in the study will be assigned to the EA group or the sham-EA group through stratified randomization. All patients receive behavioral assessments (CRS-R and brain-computer interface), neurophysiological evaluations (EEG, somatosensory evoked potentials, brainstem auditory evoked potentials), and neuroimaging evaluations (rs-fMRI, amide proton transfer, intravoxel incoherent motion, neurite orientation dispersion and density imaging) before and after the 14-day EA or sham-EA treatment. Each healthy subject will receive a set of neurophysiological and neuroimaging examinations but no treatments. The practitioner administering EA and sham-EA is the only one aware of the grouping results. In the sham-EA group, sham-acupoints, sham-acupuncture, and sham-wire are utilized. The primary outcome measurement is the change in CRS-R score after 14 days of treatment compared with the baseline CRS-R score.

DISCUSSION: The AcuDoc trial will be the first randomized sham-controlled study to investigate the clinical benefits of EA in patients with DOC. This trial will elucidate the role of EA in the treatment of DOC due to TBI and provide evidence of its therapeutic mechanisms.},
}

Humans
*Electroacupuncture/methods
Adult
*Brain Injuries, Traumatic/complications/therapy/physiopathology
*Consciousness Disorders/therapy/etiology/physiopathology
Female
Male
Young Adult
Middle Aged
Randomized Controlled Trials as Topic
Electroencephalography

@article {pmid40566772,
year = {2025},
author = {Zhang, T and Chen, J and Lu, Y and Xu, D and Yan, S and Ouyang, Z},
title = {[The analysis of invention patents in the field of artificial intelligent medical devices].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {504-511},
doi = {10.7507/1001-5515.202407044},
pmid = {40566772},
issn = {1001-5515},
mesh = {*Artificial Intelligence ; *Patents as Topic ; Humans ; *Inventions ; China ; Brain-Computer Interfaces ; Telemedicine ; *Equipment and Supplies ; Robotics ; Algorithms ; },
abstract = {The emergence of new-generation artificial intelligence technology has brought numerous innovations to the healthcare field, including telemedicine and intelligent care. However, the artificial intelligent medical device sector still faces significant challenges, such as data privacy protection and algorithm reliability. This study, based on invention patent analysis, revealed the technological innovation trends in the field of artificial intelligent medical devices from aspects such as patent application time trends, hot topics, regional distribution, and innovation players. The results showed that global invention patent applications had remained active, with technological innovations primarily focused on medical image processing, physiological signal processing, surgical robots, brain-computer interfaces, and intelligent physiological parameter monitoring technologies. The United States and China led the world in the number of invention patent applications. Major international medical device giants, such as Philips, Siemens, General Electric, and Medtronic, were at the forefront of global technological innovation, with significant advantages in patent application volumes and international market presence. Chinese universities and research institutes, such as Zhejiang University, Tianjin University, and the Shenzhen Institute of Advanced Technology, had demonstrated notable technological innovation, with a relatively high number of patent applications. However, their overseas market expansion remained limited. This study provides a comprehensive overview of the technological innovation trends in the artificial intelligent medical device field and offers valuable information support for industry development from an informatics perspective.},
}

*Artificial Intelligence
*Patents as Topic
Humans
*Inventions
China
Brain-Computer Interfaces
Telemedicine
*Equipment and Supplies
Robotics
Algorithms

@article {pmid40566769,
year = {2025},
author = {Wu, H and Chen, S and Jia, J},
title = {[Research progress on brain mechanism of brain-computer interface technology in the upper limb motor function rehabilitation in stroke patients].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {480-487},
doi = {10.7507/1001-5515.202404015},
pmid = {40566769},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; *Upper Extremity/physiopathology ; *Brain/physiopathology ; Electroencephalography ; Stroke/physiopathology ; },
abstract = {Stroke causes abnormality of brain physiological function and limb motor function. Brain-computer interface (BCI) connects the patient's active consciousness to an external device, so as to enhance limb motor function. Previous studies have preliminarily confirmed the efficacy of BCI rehabilitation training in improving upper limb motor function after stroke, but the brain mechanism behind it is still unclear. This paper aims to review on the brain mechanism of upper limb motor dysfunction in stroke patients and the improvement of brain function in those receiving BCI training, aiming to further explore the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function after stroke. The results of this study show that in the fields of imaging and electrophysiology, abnormal activity and connectivity have been found in stroke patients. And BCI training for stroke patients can improve their upper limb motor function by increasing the activity and connectivity of one hemisphere of the brain and restoring the balance between the bilateral hemispheres of the brain. This article summarizes the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function in stroke in both imaging and electrophysiology, and provides a reference for the clinical application and scientific research of BCI in stroke rehabilitation in the future.},
}

Humans
*Brain-Computer Interfaces
*Stroke Rehabilitation
*Upper Extremity/physiopathology
*Brain/physiopathology
Electroencephalography
Stroke/physiopathology

@article {pmid40566768,
year = {2025},
author = {Liu, X and Yang, B and Gan, A and Zhang, J},
title = {[Study on speech imagery electroencephalography decoding of Chinese words based on the CAM-Net model].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {473-479},
doi = {10.7507/1001-5515.202503048},
pmid = {40566768},
issn = {1001-5515},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Speech/physiology ; Algorithms ; Male ; Adult ; Imagination ; },
abstract = {Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech impairments. This study designed a Chinese speech imagery paradigm using three clinically relevant words-"Help me", "Sit up" and "Turn over"-and collected electroencephalography (EEG) data from 15 healthy subjects. Based on the data, a Channel Attention Multi-Scale Convolutional Neural Network (CAM-Net) decoding algorithm was proposed, which combined multi-scale temporal convolutions with asymmetric spatial convolutions to extract multidimensional EEG features, and incorporated a channel attention mechanism along with a bidirectional long short-term memory network to perform channel weighting and capture temporal dependencies. Experimental results showed that CAM-Net achieved a classification accuracy of 48.54% in the three-class task, outperforming baseline models such as EEGNet and Deep ConvNet, and reached a highest accuracy of 64.17% in the binary classification between "Sit up" and "Turn over". This work provides a promising approach for future Chinese speech imagery BCI research and applications.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
*Neural Networks, Computer
*Speech/physiology
Algorithms
Male
Adult
Imagination

@article {pmid40566767,
year = {2025},
author = {Li, X and Cao, X and Wang, J and Zhu, W and Huang, Y and Wan, F and Hu, Y},
title = {[Performance evaluation of a wearable steady-state visual evoked potential based brain-computer interface in real-life scenario].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {464-472},
doi = {10.7507/1001-5515.202310069},
pmid = {40566767},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; *Wearable Electronic Devices ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male ; },
abstract = {Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.},
}

*Brain-Computer Interfaces
Humans
*Evoked Potentials, Visual/physiology
*Electroencephalography
*Wearable Electronic Devices
Algorithms
Signal Processing, Computer-Assisted
Adult
Male

@article {pmid40566766,
year = {2025},
author = {Zhu, Y and Ji, Z and Li, S and Wang, H and Fu, Y and Wang, H},
title = {[A portable steady-state visual evoked potential brain-computer interface system for smart healthcare].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {455-463},
doi = {10.7507/1001-5515.202412051},
pmid = {40566766},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; Signal Processing, Computer-Assisted ; Software ; Adult ; Male ; },
abstract = {This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.},
}

*Brain-Computer Interfaces
Humans
*Evoked Potentials, Visual/physiology
*Electroencephalography
Signal Processing, Computer-Assisted
Software
Adult
Male

@article {pmid40566765,
year = {2025},
author = {Chai, X and Wang, N and Song, J and Yang, Y},
title = {[Detection of motor intention in patients with consciousness disorder based on electroencephalogram and functional near infrared spectroscopy combined with motor brain-computer interface paradigm].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {447-454},
doi = {10.7507/1001-5515.202502027},
pmid = {40566765},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; *Electroencephalography/methods ; *Consciousness Disorders/physiopathology/diagnosis ; Male ; Movement ; Adult ; Female ; Intention ; Persistent Vegetative State/physiopathology/diagnosis ; },
abstract = {Clinical grading diagnosis of disorder of consciousness (DOC) patients relies on behavioral assessment, which has certain limitations. Combining multi-modal technologies and brain-computer interface (BCI) paradigms can assist in identifying patients with minimally conscious state (MCS) and vegetative state (VS). This study collected electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals under motor BCI paradigms from 14 DOC patients, who were divided into two groups based on clinical scores: 7 in the MCS group and 7 in the VS group. We calculated event-related desynchronization (ERD) and motor decoding accuracy to analyze the effectiveness of motor BCI paradigms in detecting consciousness states. The results showed that the classification accuracies for left-hand and right-hand movement tasks using EEG were 93.28% and 76.19% for the MCS and VS groups, respectively; the classification precisions using fNIRS were 53.72% and 49.11% for these groups. When combining EEG and fNIRS features, the classification accuracies for left-hand and right-hand movement tasks in the MCS and VS groups were 95.56% and 87.38%, respectively. Although there was no statistically significant difference in motor decoding accuracy between the two groups, significant differences in ERD were observed between different consciousness states during left-hand movement tasks (P < 0.001). This study demonstrates that motor BCI paradigms can assist in assessing the level of consciousness, with EEG being more sensitive for evaluating residual motor intention intensity. Moreover, the ERD feature of motor intention intensity is more sensitive than BCI classification accuracy.},
}

Humans
*Brain-Computer Interfaces
Spectroscopy, Near-Infrared/methods
*Electroencephalography/methods
*Consciousness Disorders/physiopathology/diagnosis
Male
Movement
Adult
Female
Intention
Persistent Vegetative State/physiopathology/diagnosis

@article {pmid40566764,
year = {2025},
author = {Pan, J and Zhang, Z and Zhang, Y and Wang, F and Xiao, J},
title = {[Brain-computer interface technology and its applications for patients with disorders of consciousness].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {438-446},
doi = {10.7507/1001-5515.202410061},
pmid = {40566764},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Consciousness Disorders/diagnosis/rehabilitation/physiopathology ; Electroencephalography ; Brain/physiopathology ; Consciousness ; },
abstract = {With the continuous advancement of neuroimaging technologies, clinical research has discovered the phenomenon of cognitive-motor dissociation in patients with disorders of consciousness (DoC). This groundbreaking finding has provided new impetus for the development and application of brain-computer interface (BCI) in clinic. Currently, BCI has been widely applied in DoC patients as an important tool for assessing and assisting behaviorally unresponsive individuals. This paper reviews the current applications of BCI in DoC patients, focusing four main aspects including consciousness detection, auxiliary diagnosis, prognosis assessment, and rehabilitation treatment. It also provides an in-depth analysis of representative key techniques and experimental outcomes in each aspect, which include BCI paradigm designs, brain signal decoding method, and feedback mechanisms. Furthermore, the paper offers recommendations for BCI design tailored to DoC patients and discusses future directions for research and clinical practice in this field.},
}

Humans
*Brain-Computer Interfaces
*Consciousness Disorders/diagnosis/rehabilitation/physiopathology
Electroencephalography
Brain/physiopathology
Consciousness

@article {pmid40566763,
year = {2025},
author = {Pan, H and Ding, P and Wang, F and Li, T and Zhao, L and Nan, W and Gong, A and Fu, Y},
title = {[Evaluation methods for the rehabilitation efficacy of bidirectional closed-loop motor imagery brain-computer interface active rehabilitation training systems].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {431-437},
doi = {10.7507/1001-5515.202407097},
pmid = {40566763},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Imagery, Psychotherapy/methods ; },
abstract = {The bidirectional closed-loop motor imagery brain-computer interface (MI-BCI) is an emerging method for active rehabilitation training of motor dysfunction, extensively tested in both laboratory and clinical settings. However, no standardized method for evaluating its rehabilitation efficacy has been established, and relevant literature remains limited. To facilitate the clinical translation of bidirectional closed-loop MI-BCI, this article first introduced its fundamental principles, reviewed the rehabilitation training cycle and methods for evaluating rehabilitation efficacy, and summarized approaches for evaluating system usability, user satisfaction and usage. Finally, the challenges associated with evaluating the rehabilitation efficacy of bidirectional closed-loop MI-BCI were discussed, aiming to promote its broader adoption and standardization in clinical practice.},
}

*Brain-Computer Interfaces
Humans
*Imagination/physiology
*Imagery, Psychotherapy/methods

@article {pmid40566538,
year = {2025},
author = {Pilipović, K and Janković, T and Rajič Bumber, J and Belančić, A and Mršić-Pelčić, J},
title = {Traumatic Brain Injury: Novel Experimental Approaches and Treatment Possibilities.},
journal = {Life (Basel, Switzerland)},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/life15060884},
pmid = {40566538},
issn = {2075-1729},
support = {UIP-2017-05-9517//Croatian Science Foundation/ ; uniri-iskusni-biomed-23-56//University of Rijeka/ ; uniri-mladi-biomed-23-38//University of Rijeka/ ; uniri-iskusni-biomed-23-82//University of Rijeka/ ; },
abstract = {Traumatic brain injury (TBI) remains a critical global health issue with limited effective treatments. Traditional care of TBI patients focuses on stabilization and symptom management without regenerating damaged brain tissue. In this review, we analyze the current state of treatment of TBI, with focus on novel therapeutic approaches aimed at reducing secondary brain injury and promoting recovery. There are few innovative strategies that break away from the traditional, biological target-focused treatment approaches. Precision medicine includes personalized treatments based on biomarkers, genetics, advanced imaging, and artificial intelligence tools for prognosis and monitoring. Stem cell therapies are used to repair tissue, regulate immune responses, and support neural regeneration, with ongoing development in gene-enhanced approaches. Nanomedicine uses nanomaterials for targeted drug delivery, neuroprotection, and diagnostics by crossing the blood-brain barrier. Brain-machine interfaces enable brain-device communication to restore lost motor or neurological functions, while virtual rehabilitation and neuromodulation use virtual and augmented reality as well as brain stimulation techniques to improve rehabilitation outcomes. While these approaches show great potential, most are still in development and require more clinical testing to confirm safety and effectiveness. The future of TBI therapy looks promising, with innovative strategies likely to transform care.},
}

@article {pmid40564460,
year = {2025},
author = {Liu, Z and Fan, K and Gu, Q and Ruan, Y},
title = {Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
doi = {10.3390/bioengineering12060645},
pmid = {40564460},
issn = {2306-5354},
abstract = {The study of electroencephalogram (EEG) signals is crucial for understanding brain function and has extensive applications in clinical diagnosis, neuroscience, and brain-computer interface technology. This paper addresses the challenge of recognizing motor imagery EEG signals with few channels, which is essential for portable and real-time applications. A novel framework is proposed that applies a continuous wavelet transform to convert time-domain EEG signals into two-dimensional time-frequency representations. These images are then concatenated into channel-dependent multilayer EEG time-frequency representations (CDML-EEG-TFR), incorporating multidimensional information of time, frequency, and channels, allowing for a more comprehensive and enriched brain representation under the constraint of few channels. By adopting a deep convolutional neural network with EfficientNet as the backbone and utilizing pre-trained weights from natural image datasets for transfer learning, the framework can simultaneously learn temporal, spatial, and channel features embedded in the CDML-EEG-TFR. Moreover, the transfer learning strategy effectively addresses the issue of data sparsity in the context of a few channels. Our approach enhances the classification accuracy of motor imagery EEG signals in few-channel scenarios. Experimental results on the BCI Competition IV 2b dataset show a significant improvement in classification accuracy, reaching 80.21%. This study highlights the potential of CDML-EEG-TFR and the EfficientNet-based transfer learning strategy in few-channel EEG signal classification, laying a foundation for practical applications and further research in medical and sports fields.},
}

@article {pmid40564444,
year = {2025},
author = {Garcia-Palencia, O and Fernandez, J and Shim, V and Kasabov, NK and Wang, A and The Alzheimer's Disease Neuroimaging Initiative, },
title = {Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
doi = {10.3390/bioengineering12060628},
pmid = {40564444},
issn = {2306-5354},
support = {Project 22-UOA-120, 23-UOA-055-CSG//Health Research Council of New Zealand and Royal Society Catalyst/ ; 23-UOA-055-CSG//University of Auckland/ ; },
abstract = {Artificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to their limited temporal memory and high computational demands. Spiking neural networks (SNNs), inspired by the brain's biological processes, offer a promising alternative. SNNs use discrete spikes for event-driven communication, making them energy-efficient and well suited for the real-time processing of dynamic brain data. Among SNN architectures, NeuCube stands out as a powerful framework for analysing spatiotemporal neuroimaging data. It employs a 3D brain-like structure to model neural activity, enabling personalised modelling, disease classification, and biomarker discovery. This paper explores the advantages of SNNs and NeuCube for multimodal neuroimaging analysis, including their ability to handle complex spatiotemporal patterns, adapt to evolving data, and provide interpretable insights. We discuss applications in disease diagnosis, brain-computer interfaces, and predictive modelling, as well as challenges such as training complexity, data encoding, and hardware limitations. Finally, we highlight future directions, including hybrid ANN-SNN models, neuromorphic hardware, and personalised medicine. Our contributions in this work are as follows: (i) we give a comprehensive review of an SNN applied to neuroimaging analysis; (ii) we present current software and hardware platforms, which have been studied in neuroscience; (iii) we provide a detailed comparison of performance and timing of SNN software simulators with a curated ADNI and other datasets; (iv) we provide a roadmap to select a hardware/software platform based on specific cases; and (v) finally, we highlight a project where NeuCube has been successfully used in neuroscience. The paper concludes with discussions of challenges and future perspectives.},
}

@article {pmid40564430,
year = {2025},
author = {Darvishi, H and Mohammadi, A and Maghami, MH and Sadeghi, M and Sawan, M},
title = {EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain-Computer Interface Performance.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
doi = {10.3390/bioengineering12060614},
pmid = {40564430},
issn = {2306-5354},
abstract = {Brain-computer interfaces (BCIs) translate electroencephalography (EEG) signals into control commands, offering potential solutions for motor-impaired individuals. While traditional BCI studies often focus solely on amplitude variations or inter-channel connectivity, movement-related brain activity is inherently dynamic, involving interactions across regions and frequency bands. We propose that combining amplitude-based (filter bank common spatial patterns, FBCSP) and phase-based connectivity features (phase-locking value, PLV) improves decoding accuracy. EEG signals from ten healthy subjects were recorded during arm movements, with electromyography (EMG) as ground truth. After preprocessing (resampling, normalization, bandpass filtering), FBCSP and multi-lag PLV features were fused, and the ReliefF algorithm selected the most informative subset. A feedforward neural network achieved average metrics of: Pearson correlation 0.829 ± 0.077, R-squared value 0.675 ± 0.126, and root mean square error (RMSE) 0.579 ± 0.098 in predicting EMG amplitudes indicative of arm movement angles. Analysis highlighted contributions from both FBCSP and PLV, particularly in the 4-8 Hz and 24-28 Hz bands. This fusion approach, augmented by data-driven feature selection, significantly enhances movement decoding accuracy, advancing robust neuroprosthetic control systems.},
}

@article {pmid40563754,
year = {2025},
author = {Gkintoni, E and Vassilopoulos, SP and Nikolaou, G and Vantarakis, A},
title = {Neurotechnological Approaches to Cognitive Rehabilitation in Mild Cognitive Impairment: A Systematic Review of Neuromodulation, EEG, Virtual Reality, and Emerging AI Applications.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/brainsci15060582},
pmid = {40563754},
issn = {2076-3425},
abstract = {Background/Objectives: Mild Cognitive Impairment (MCI) represents a clinical syndrome characterized by cognitive decline greater than expected for an individual's age and education level but not severe enough to significantly interfere with daily activities, with variable trajectories that may remain stable, progress to dementia, or occasionally revert to normal cognition. This systematic review examines neurotechnological approaches to cognitive rehabilitation in MCI populations, including neuromodulation, electroencephalography (EEG), virtual reality (VR), cognitive training, physical exercise, and artificial intelligence (AI) applications. Methods: A systematic review following PRISMA guidelines was conducted on 34 empirical studies published between 2014 and 2024. Studies were identified through comprehensive database searches and included if they employed neurotechnological interventions targeting cognitive outcomes in individuals with MCI. Results: Evidence indicates promising outcomes across multiple intervention types. Neuromodulation techniques showed beneficial effects on memory and executive function. EEG analyses identified characteristic neurophysiological markers of MCI with potential for early detection and monitoring. Virtual reality enhanced assessment sensitivity and rehabilitation engagement through ecologically valid environments. Cognitive training demonstrated the most excellent efficacy with multi-domain, adaptive approaches. Physical exercise interventions yielded improvements through multiple neurobiological pathways. Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. Conclusions: Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. Neurophysiological monitoring provides valuable biomarkers for diagnosis and treatment response. Future research should focus on more extensive clinical trials, standardized protocols, and accessible implementation models to translate these technological advances into clinical practice.},
}

@article {pmid40563743,
year = {2025},
author = {Mróz, K and Jonak, K},
title = {Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/brainsci15060571},
pmid = {40563743},
issn = {2076-3425},
support = {FD-20/II-3/999//Lublin University of Technology/ ; },
abstract = {Background: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. ML enables large-scale data processing, offering novel opportunities for diagnosing and treating mental disorders. However, challenges such as data variability, noise, and model interpretability remain significant. This study reviews the current limitations of EEG-based anxiety detection and explores the potential of advanced AI models, including transformers and VAE-D2GAN, to improve diagnostic accuracy and real-time monitoring. Methods: The paper presents the application of ML algorithms, with a focus on convolutional neural networks (CNN) and recurrent neural networks (RNN), in identifying biomarkers of anxiety disorders and predicting therapy responses. Additionally, it discusses the role of brain-computer interfaces (BCIs) in assisting individuals with disabilities by enabling device control through brain activity. Results: Experimental EEG research on BCI applications was conducted, focusing on motor imagery-based brain activity. Findings indicate that successive training sessions improve signal classification accuracy, emphasizing the need for personalized and adaptive EEG analysis methods. Challenges in BCI usability and technological constraints in EEG processing are also addressed. Conclusions: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy.},
}

@article {pmid40563723,
year = {2025},
author = {Fodor, MA and Cantürk, A and Heisenberg, G and Volosyak, I},
title = {Streamlining cVEP Paradigms: Effects of a Minimized Electrode Montage on Brain-Computer Interface Performance.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/brainsci15060549},
pmid = {40563723},
issn = {2076-3425},
support = {101118964//This project has received funding from the European Union's research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101118964./ ; },
abstract = {(1) Background: Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals, offering potential applications in assistive technology and neurorehabilitation. Code-modulated visual evoked potential (cVEP)-based BCIs employ code-pattern-based stimulation to evoke neural responses, which can then be classified to infer user intent. While increasing the number of EEG electrodes across the visual cortex enhances classification accuracy, it simultaneously reduces user comfort and increases setup complexity, duration, and hardware costs. (2) Methods: This online BCI study, involving thirty-eight able-bodied participants, investigated how reducing the electrode count from 16 to 6 affected performance. Three experimental conditions were tested: a baseline 16-electrode configuration, a reduced 6-electrode setup without retraining, and a reduced 6-electrode setup with retraining. (3) Results: Our results indicate that, on average, performance declines with fewer electrodes; nonetheless, retraining restored near-baseline mean Information Transfer Rate (ITR) and accuracy for those participants for whom the system remained functional. The results reveal that for a substantial number of participants, the classification pipeline fails after electrode removal, highlighting individual differences in the cVEP response characteristics or inherent limitations of the classification approach. (4) Conclusions: Ultimately, this suggests that minimal cVEP-BCI electrode setups capable of reliably functioning across all users might only be feasible through other, more flexible classification methods that can account for individual differences. These findings aim to serve as a guideline for what is currently achievable with this common cVEP paradigm and to highlight where future research should focus in order to move closer to a practical and user-friendly system.},
}

@article {pmid40562060,
year = {2025},
author = {Xu, F and Liu, Y and Li, Y and Zhang, C and Han, Z and He, T and Xiao, X and Chao, F and Leng, J and Xu, M},
title = {Research on coding and decoding algorithm of binocular brain-controlled unmanned vehicle.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade829},
pmid = {40562060},
issn = {1741-2552},
abstract = {With the rapid development of Brain-Computer Interface (BCI) technology, Steady-State Visual Evoked Potential (SSVEP) has emerged as an effective method for high-efficiency information transmission. However, traditional single-frequency stimulation methods face limitations in command set scalability and visual comfort. To address these issues, we propose a novel binocular SSVEP stimulation paradigm for brain-controlled unmanned vehicles. This system uses a checkerboard and phase encoding for stimulus presentation, encoding a single target with two frequencies to expand the command set. The frequencies are set between 30-35 Hz to enhance visual comfort. By leveraging polarized light technology, each eye receives distinct frequencies, suppressing intermodulation components and reducing the stimulated area for each eye. We also introduce an improved Filter Bank Dual-frequency Task-Discriminant Component Analysis (FBD-TDCA) algorithm. Experimental results show that, in a 15-command simulation, only six frequencies successfully encoded all commands, achieving comparable performance to traditional single-frequency paradigms. A 12-target brain-controlled unmanned vehicle online simulation with 12 participants further validated the proposed paradigm and algorithm. In the binocular stimulation paradigm, the average Information Transfer Rate (ITR) reached 154.67±19.69 bits/min in online experiments, with offline training yielding an ITR of 170.7±31.2 bits/min. This novel stimulation paradigm not only supports large-scale target sets for BCI systems but also improves visual comfort, offering stability and feasibility for practical brain-controlled applications. .},
}

@article {pmid40561510,
year = {2025},
author = {Almanna, MA and Elkaim, LM and Alvi, MA and Levett, JJ and Li, B and Mamdani, M and Al-Omran, M and Alotaibi, NM},
title = {Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study.},
journal = {JMIR formative research},
volume = {9},
number = {},
pages = {e60859},
doi = {10.2196/60859},
pmid = {40561510},
issn = {2561-326X},
mesh = {Humans ; *Brain-Computer Interfaces/psychology ; *Social Media/statistics & numerical data ; Natural Language Processing ; *Public Opinion ; Male ; Emotions ; Female ; Adult ; },
abstract = {BACKGROUND: Given the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education.

OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods.

METHODS: A mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users' demographics by matching predefined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.

RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; τ=0.266; P<.001). Most posts were objective (50,847/65,340, 77.81%), with a smaller proportion being subjective (14,393/65,340, 22.02%). Biographic analysis showed that the "broadcasting" group contributed the most to BCI discussions (17,803/58,030, 30.67%), while the "scientific" group, contributing 27.58% (n=16,005), had the highest overall engagement metrics. The emotional analysis identified anticipation (score = 10,802/52,618, 20.52%), trust (score=9244/52,618, 17.56%), and fear (score=7344/52,618, 13.95%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.

CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy making, and communication strategies.},
}

Humans
*Brain-Computer Interfaces/psychology
*Social Media/statistics & numerical data
Natural Language Processing
*Public Opinion
Male
Emotions
Female
Adult

@article {pmid40561478,
year = {2025},
author = {Chen, CS and Chang, SH and Liu, CW and Pan, TM},
title = {Exploring the Potential of Electroencephalography Signal-Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis.},
journal = {JMIR medical informatics},
volume = {13},
number = {},
pages = {e72027},
doi = {10.2196/72027},
pmid = {40561478},
issn = {2291-9694},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Brain/physiology/diagnostic imaging ; *Image Processing, Computer-Assisted/methods ; Adult ; },
abstract = {BACKGROUND: Electroencephalography (EEG) has been widely used to measure brain activity, but its potential to generate accurate images from neural signals remains a challenge. Most EEG-decoding research has focused on tasks such as motor imagery, emotion recognition, and brain wave classification, which involve EEG signal analysis and classification. Some studies have explored the correlation between EEG and images, primarily focusing on EEG-image pair classification or transformation. However, EEG-based image generation remains underexplored.

OBJECTIVE: The primary goal of this study was to extend EEG-based classification to image generation, addressing the limitations of previous methods and unlocking the full potential of EEG for image synthesis. To achieve more meaningful EEG-to-image generation, we developed a novel framework, Neural-Cognitive Multimodal EEG-Informed Image (NECOMIMI), which was specifically designed to generate images directly from EEG signals.

METHODS: We developed a 2-stage NECOMIMI method, which integrated the novel Neural Encoding Representation Vectorizer (NERV) EEG encoder that we designed with a diffusion-based generative model. The Category-Based Assessment Table (CAT) score was introduced to evaluate the semantic quality of EEG-generated images. In addition, the ThingsEEG dataset was used to validate and benchmark the CAT score, providing a standardized measure for assessing EEG-to-image generation performance.

RESULTS: The NERV EEG encoder achieved state-of-the-art performance in several zero-shot classification tasks, with an average accuracy of 94.8% (SD 1.7%) in the 2-way task and 86.8% (SD 3.4%) in the 4-way task, outperforming models such as Natural Image Contrast EEG, Multimodal Similarity-Keeping Contrastive Learning, and Adaptive Thinking Mapper ShallowNet. This highlighted its superiority as a feature extraction tool for EEG signals. In a 1-stage image generation framework, EEG embeddings often resulted in abstract or generalized images such as landscapes instead of specific objects. Our proposed 2-stage NECOMIMI architecture effectively extracted semantic information from noisy EEG signals, showing its ability to capture and represent underlying concepts derived from brain wave activity. We further conducted a perturbation study to test whether the model overly depended on visual cortex EEG signals for scene-based image generation. The perturbation of visual cortex EEG channels led to a notable increase in Fréchet inception distance scores, suggesting that our model relied heavily on posterior brain signals to generate semantically coherent images.

CONCLUSIONS: NECOMIMI demonstrated the potential of EEG-to-image generation, revealing the challenges of translating noisy EEG data into accurate visual representations. The novel NERV EEG encoder for multimodal contrastive learning reached state-of-the-art performance both on n-way zero-shot and EEG-informed image generation. The introduction of the CAT score provided a new evaluation metric, paving the way for future research to refine generative models. In addition, this study highlighted the significant clinical potential of EEG-to-image generation, particularly in enhancing brain-machine interface systems and improving quality of life for individuals with motor impairments.},
}

*Electroencephalography/methods
Humans
*Signal Processing, Computer-Assisted
*Brain/physiology/diagnostic imaging
*Image Processing, Computer-Assisted/methods
Adult

@article {pmid40554057,
year = {2025},
author = {Park, S and Ha, J and Kim, L},
title = {Improving single-trial detection of error-related potentials by considering the effect of heartbeat-evoked potentials in a motor imagery-based brain-computer interface.},
journal = {Computers in biology and medicine},
volume = {195},
number = {},
pages = {110563},
doi = {10.1016/j.compbiomed.2025.110563},
pmid = {40554057},
issn = {1879-0534},
abstract = {OBJECTIVE: This study aimed to determine the effect of heartbeat-evoked potentials (HEPs) on changes in the error-related potential (ErrP) epoch and classification performance in single trials, specifically distinguishing between correct and error conditions in a three-class motor imagery-based brain-computer interface.

METHODS: Eleven individuals participated in this study, with 10 participants assigned to the offline group and 10 to the real-time group. The experiment consisted of 360 motor imagery trials, involving both correct and erroneous feedback. The ErrP trial was categorized into three conditions based on whether the heartbeat was within the ErrP epoch time window or not: (1) including heartbeat trials (ErrPIHB), (2) excluding heartbeat trials (ErrPEHB), and (3) total trials (ErrPT).

RESULTS: A small negativity was observed at approximately 200 ms, followed by a subsequent positivity at approximately 300 ms. The prominent amplitudes at approximately 200 and 300 ms in the ErrPEHB condition notably differed from those in the ErrPT and ErrPIHB conditions, showing the highest classification accuracy. In the offline experiment dataset of 10 participants, the ErrPEHB condition demonstrated the highest classification accuracy (0.89). This was higher by 0.07 and 0.11 compared to the ErrPT (0.82) and ErrPIHB (0.78) conditions, respectively. For real-time analysis, the novel ErrP paradigm proposed in this study achieved a classification accuracy of 0.89 for 10 participants, a 0.05 increase compared with that of the conventional ErrP paradigm.

CONCLUSION AND SIGNIFICANCE: These findings can contribute to obtaining pure or clear ErrP epochs associated with the target response and significantly improve classification performance.},
}

@article {pmid40553977,
year = {2025},
author = {Jiang, H and Qi, H and Tang, A and Hu, S and Lai, J},
title = {Start the Engine of Neuroregeneration: A Mechanistic and Strategic Overview of Direct Astrocyte-to-Neuron Reprogramming.},
journal = {Ageing research reviews},
volume = {},
number = {},
pages = {102808},
doi = {10.1016/j.arr.2025.102808},
pmid = {40553977},
issn = {1872-9649},
abstract = {The decline of adult neurogenesis and neuronal function during aging underlies the onset and progression of neurodegenerative diseases such as Alzheimer's disease. Conventional therapies, including neurotransmitter modulators and antibodies targeting pathogenic proteins, offer only symptomatic improvement. As the most abundant glial cells in the brain, astrocytes outnumber neurons nearly fivefold. However, their proliferative and transdifferentiation potential renders them ideal candidates for in situ neuronal replacement. Direct astrocyte-to-neuron reprogramming offers a promising regenerative approach to restore damaged neural circuits. Herein, we propose a "car start-up" model to conceptualize this process, emphasizing the need to inhibit non-neuronal fate pathways (release the handbrake), suppress transcriptional repressors (release the footbrake), and activate neuron-specific gene expression (step on the gas). Additionally, overcoming metabolic barriers in the cytoplasm is essential for successful lineage conversion. Viral or non-viral vectors deliver reprogramming factors, while small molecules serve as metabolic and epigenetic fuel to boost efficiency. In summary, we review the current evidence supporting direct astrocyte-to-neuron reprogramming as a viable regenerative strategy in the aging brain. We also highlight the conceptual "car start-up" model as a useful framework to dissect the molecular logic of lineage conversion and emphasize its promising therapeutic potential for combating neurodegenerative diseases.},
}

@article {pmid40553738,
year = {2025},
author = {Zhang, HG and Wang, JF and Jialin, A and Zhao, XY and Wang, C and Deng, W},
title = {Relationship between multimorbidity burden and depressive symptoms in older Chinese adults: A prospective 10-year cohort study.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {119714},
doi = {10.1016/j.jad.2025.119714},
pmid = {40553738},
issn = {1573-2517},
abstract = {BACKGROUND: Recent research indicates that multimorbidity clusters due to common mechanisms and risk factors, leading to different effects on the development of depressive symptoms (DS) in older populations. This study innovatively examined the association of both the number and specific patterns of multimorbidity with DS.

METHODS: A total of 1988 participants aged 60 years and older were selected from the China Health and Retirement Longitudinal Study (CHARLS) and monitored for DS between June 2011 and September 2020. Twelve chronic conditions were assessed through self-reports. DS was evaluated using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Latent class analysis (LCA) was used to identify multimorbidity patterns, and Cox proportional hazards regression models examined the associations of specific diseases, multimorbidity count and multimorbidity patterns with DS.

RESULTS: During the 9.17-year follow-up period, 986 cases of DS were identified. Hypertension (adjusted hazard ratio [HR] = 1.21, 95 % confidence interval [CI] = 1.05-1.39), stroke (HR = 1.77, 95%CI = 1.20-2.63), stomach or other digestive disease (HR = 1.28, 95%CI = 1.11-1.48), arthritis or rheumatism (HR = 1.41, 95%CI = 1.24-1.60), chronic lung diseases (HR = 1.25, 95%CI = 1.03-1.52) and kidney disease (HR = 1.38, 95%CI = 1.07-1.78) were significantly associated with increased DS risk. Each additional chronic condition increased the DS hazard by 13 % (adjusted HR = 1.13, 95 % CI = 1.08-1.18). Four multimorbidity patterns were identified by LCA, with the digestion/arthritis pattern (HR = 1.47, 95 % CI = 1.22-1.77) and respiratory pattern (HR = 1.47, 95 % CI = 1.07-2.04) showing higher DS risk compared to the relatively healthy group.

CONCLUSION: The number and patterns of multimorbidity are significantly associated with heightened DS risk in older populations. Older adults in complex health conditions, particularly those with digestion/arthritis and respiratory multimorbidity patterns, should receive closer mental health monitoring.},
}

@article {pmid40551292,
year = {2025},
author = {Chu, J and Yao, J and Li, Z and Li, J and Zhang, Y and Liu, C and He, H and Li, B and Wei, H},
title = {Brain tissue electrical conductivity as a promising biomarker for dementia assessment using MRI.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {21},
number = {6},
pages = {e70270},
pmid = {40551292},
issn = {1552-5279},
support = {2024YFC2421100//National Key Research and Development Program of China/ ; //National Natural Science Foundation of China/ ; //62471296, 82271441, 62071299, 82372036, 82001342/ ; 23TS1400200//Shanghai Science and Technology Development Funds/ ; STAR 20220103 YG2023LC02//SJTU Trans-med Awards Research/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Biomarkers ; *Brain/diagnostic imaging/metabolism/physiopathology ; *Dementia/diagnostic imaging/diagnosis/metabolism ; Male ; Female ; Amyloid beta-Peptides/metabolism ; *Electric Conductivity ; tau Proteins/metabolism ; Aged ; Cognitive Dysfunction ; Positron-Emission Tomography ; },
abstract = {INTRODUCTION: Dementia, particularly Alzheimer's disease, involves cognitive decline linked to amyloid beta (Aβ) and tau protein aggregation. Magnetic resonance imaging (MRI)-based brain tissue conductivity, which increases in dementia, may serve as a non-invasive biomarker for protein aggregation. We investigate the relationship between MRI-based brain electrical conductivity, protein aggregation, cognition, and gene expression.

METHODS: Brain conductivity maps were reconstructed and correlated with PET protein signals, cognitive performance, and plasma protein levels. The diagnostic potential of conductivity for dementia was assessed, and transcriptomic analysis using the Allen Human Brain Atlas elucidated the underlying biological processes.

RESULTS: Increased brain conductivity was associated with Aβ and tau aggregation in specific brain regions, cognitive decline, and plasma protein levels. Conductivity also improved dementia discrimination performance, and higher gene expression related to ion transport, cellular development, and signaling pathways was observed.

DISCUSSION: Brain electrical conductivity shows promise as a biomarker for dementia, correlating with protein aggregation and relevant cellular processes.

HIGHLIGHTS: Brain tissue conductivity correlates with Aβ and tau aggregation in dementia. Brain tissue conductivity correlates with cognitive scores and GMV. CSF conductivity correlates with plasma protein levels. Combining conductivity with GMV improves dementia diagnosis accuracy. Gene expression in ion processes, cell development, and signaling links to conductivity.},
}

Humans
*Magnetic Resonance Imaging/methods
Biomarkers
*Brain/diagnostic imaging/metabolism/physiopathology
*Dementia/diagnostic imaging/diagnosis/metabolism
Male
Female
Amyloid beta-Peptides/metabolism
*Electric Conductivity
tau Proteins/metabolism
Aged
Cognitive Dysfunction
Positron-Emission Tomography

@article {pmid40550006,
year = {2025},
author = {Liu, Y and Fan, P and Pan, Y and Ping, J},
title = {Flexible Microinterventional Sensors for Advanced Biosignal Monitoring.},
journal = {Chemical reviews},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.chemrev.5c00115},
pmid = {40550006},
issn = {1520-6890},
abstract = {Flexible microinterventional sensors represent a transformative technology that enables the minimal intervention required to access and monitor complex biosignals (e.g., bioelectrical, biophysical, and biochemical signals) originating from deep tissues, thereby providing accurate data for diagnostics, robotics, prosthetics, brain-computer interfaces, and therapeutic systems. However, fully unlocking their potential hinges on establishing a nondisruptive, intimate, and nonrestrictive interface with the tissue surface, facilitating efficient integration between the microinterventional sensor and the target tissue. In this comprehensive review, we highlight the critical tissue characteristics in both physiologically and pathologically relevant contexts that are pivotal for the design of microinterventional sensors. We also summarize recent advancements in flexible substrate materials and conductive materials, which are tailored to facilitate effective information interaction between bioelectronic components and biological tissues. Furthermore, we classify various electrode architectures─spanning 1D, 2D, and 3D─designed to accommodate the mechanics of soft tissues and enable nonrestrictive interfaces in diverse sensing scenarios. Additionally, we outline critical challenges for next-generation microinterventional sensors and propose integrating advanced materials, innovative fabrication, and embedded intelligence to drive breakthroughs in biosignal sensing. Ultimately, we aim to both provide foundational understanding and highlight emerging strategies in biosignal capture, leveraging recent advancements in these critical components.},
}

@article {pmid40549688,
year = {2025},
author = {Meijs, S and Andreis, FR and Kjærgaard, B and Janjua, TAM and Jensen, W},
title = {Chronic Cranial Window Technique for Repeated Cortical Recordings During Anesthesia in Pigs.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {220},
pages = {},
doi = {10.3791/67931},
pmid = {40549688},
issn = {1940-087X},
mesh = {Animals ; Swine ; *Electrocorticography/methods/instrumentation ; *Anesthesia/methods ; *Somatosensory Cortex/physiology ; Dura Mater/surgery ; Electrodes, Implanted ; },
abstract = {Cortical recordings are essential for extracting neuronal signals to inform various applications, including brain-computer interfaces and disease diagnostics. Each application places specific requirements on the recording technique, and invasive solutions are often selected for long-term recordings. However, invasive recording methods are challenged by device failure and adverse tissue responses, which compromise long-term signal quality. To improve the reliability and quality of chronic cortical recordings while minimizing risks related to device failure and tissue reactions, we developed a cranial window technique. In this protocol, we report methods to implant and access a cranial window in juvenile landrace pigs, which facilitates temporary electrocorticography (ECoG) array placement on the dura mater. We further describe how cortical signals can be recorded using the cranial window technique. Cranial window access can be repeated several times, but a minimum of 2 weeks between implant and access surgeries is advised to facilitate recovery and tissue healing. The cranial window approach successfully minimized common electrode failure modes and tissue responses, resulting in stable and reliable cortical recordings over time. We recorded event-related potentials (ERPs) from the primary somatosensory cortex as an example. The method provided highly reliable recordings, which also allowed the assessment of the effect of an intervention (high-frequency stimulation) on the ERPs. The absence of significant device failures and the reduced number of electrodes used (two electrodes, 43 recording sessions, 16 animals) suggest an improved research economy. While minor surgical access is required for electrode placement, the method offers advantages such as reduced infection risk and improved animal welfare. This study presents a scalable, reliable, and reproducible method for chronic cortical recordings, with potential applications in various fields of neuroscience, including pain research and neurological disease diagnosis. Future adaptations may extend its use to other species and recording modalities, such as intracortical recordings and imaging techniques.},
}

Animals
Swine
*Electrocorticography/methods/instrumentation
*Anesthesia/methods
*Somatosensory Cortex/physiology
Dura Mater/surgery
Electrodes, Implanted

@article {pmid40549518,
year = {2025},
author = {Yu, F and Rao, Z and Chen, N and Liu, L and Jiang, M},
title = {ArmBCIsys: Robot Arm BCI System With Time-Frequency Network for Multiobject Grasping.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3579332},
pmid = {40549518},
issn = {2162-2388},
abstract = {Brain-computer interface (BCI) offers a direct communication and control channel between the human brain and external devices, presenting new pathways for individuals with physical disabilities to operate robotic arms for complex tasks. However, achieving multiobject grasping tasks under low signal-to-noise ratio (SNR) consumer-grade EEG signals is a significant challenge due to the lack of robust decoding algorithms and precise visual tracking methods. This article proposes, ArmBCIsys, an integrated robotic arm system that combines a novel dual-branch frequency-enhanced network (DBFENet) to robustly decode EEG signals under noisy conditions with the high-precision vision-guided grasping module. The proposed DBFENet designs the scaling temporal convolution block (STCB) to extract multiscale spatiotemporal features from the time domain, while the designed DropScale projected Transformer (DSPT) utilizes discrete cosine transform (DCT) to capture key frequency-domain features, significantly improving decoding robustness. We fine-tune the masked-attention mask Transformer (Mask2Former) model on the Jacquard dataset and incorporate the multiframe centroid-intersection over union (IoU) tracking algorithm to build visual grasp segmenter (VisGraspSeg), enabling reliable segmentation and dynamic tracking for diverse daily objects. Experimental validations on both self-built code-modulated visual evoked potential (c-VEP) dataset (1344 samples) and two public c-VEP datasets demonstrate that DBFENet achieves the state-of-the-art recognition performance, and the system integrates the DBFENet and proposed vision-guided module and ensures stable multiobject selecting and automatic object grasping in dynamic environments, extending promising applications in healthcare robotics, assistive technology, and industrial automation. The self-built dataset has been made publicly accessible at https://github.com/wtu1020/ ArmBCIsys-Self-built-cVEP-Dataset.},
}

@article {pmid40548156,
year = {2025},
author = {Tzimourta, KD},
title = {Human-Centered Design and Development in Digital Health: Approaches, Challenges, and Emerging Trends.},
journal = {Cureus},
volume = {17},
number = {6},
pages = {e85897},
pmid = {40548156},
issn = {2168-8184},
abstract = {Human-centered design (HCD) has emerged as a critical approach for developing digital health technologies that are usable, acceptable, and effective within complex healthcare environments. Rooted in systems theory, ergonomics, and information systems research, HCD prioritizes the needs, capabilities, and limitations of diverse user groups - including patients, clinicians, and caregivers - throughout the design and implementation process. This review synthesizes current applications of HCD in four key domains: brain-computer interfaces (BCIs), augmented and virtual reality (AR/VR), artificial intelligence (AI)-based clinical decision support systems AI-CDSS, and mobile health (mHealth) technologies. It explores design frameworks, usability strategies, and models of human-technology collaboration that contribute to successful adoption and sustained use. Ethical and legal considerations - such as data privacy, informed consent, and algorithmic fairness - are also addressed, particularly in contexts involving biometric and neurophysiological data. While HCD practices have shown substantial potential to improve digital health outcomes, their implementation remains uneven across technologies and settings. Emerging trends, including adaptive personalization, explainable AI, and participatory co-design, are identified as promising directions for the development of more inclusive, trustworthy, and sustainable digital health innovations.},
}

@article {pmid40546334,
year = {2025},
author = {Cruz, MV and Jamal, S and Sethuraman, SC},
title = {A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.},
journal = {Journal of medical signals and sensors},
volume = {15},
number = {},
pages = {16},
pmid = {40546334},
issn = {2228-7477},
abstract = {The brain-computer interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in health care. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in health care and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.},
}

@article {pmid40545006,
year = {2025},
author = {Feng, J and Jia, W and Li, Z},
title = {Electroencephalography: A Valuable Tool for Assessing Motor Impairment and Recovery Post-Stroke.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110518},
doi = {10.1016/j.jneumeth.2025.110518},
pmid = {40545006},
issn = {1872-678X},
abstract = {Stroke is a leading cause of adult disability, and restoring motor function post-stroke is critical to improving the well-being and quality of life of affected individuals. Accurate and timely assessment of motor function is essential for developing effective rehabilitation strategies and predicting recovery outcomes. Electroencephalography (EEG) offers a non-invasive, real-time monitoring of brain activity, offering personalized insights into motor impairment and recovery. Its simplicity and bedside applicability make EEG a valuable tool and a potential biomarker for brain function. In recent years, the integration of EEG with the brain-computer interface technology and neuromodulation techniques has revolutionized personalized rehabilitation therapy, offering new hope for patients with motor dysfunction following stroke. This review synthesizes evidence on EEG-derived biomarkers and their integration with brain-computer interface and neuromodulation techniques, proposing a framework for personalized rehabilitation strategies in stroke recovery.},
}

@article {pmid40544658,
year = {2025},
author = {Mathon, B and Navarro, V and Pons, T and Lecas, S and Roussel, D and Charpier, S and Carpentier, A},
title = {Ultrasound-induced blood-brain barrier opening and selenium-nanoparticle injection lower seizure activity: A mouse model of temporal lobe epilepsy.},
journal = {Ultrasonics},
volume = {155},
number = {},
pages = {107734},
doi = {10.1016/j.ultras.2025.107734},
pmid = {40544658},
issn = {1874-9968},
abstract = {BACKGROUND: Given the limitations of current treatment options for drug-resistant mesial temporal lobe epilepsy (MTLE), the development of novel, nonablative and minimally invasive surgical techniques is essential.

OBJECTIVE AND METHODS: In this study, low-intensity pulsed ultrasound (LIPU)- and microbubble-induced (henceforth LIPU) blood-brain barrier (BBB) opening combined with selenium-nanoparticle (SeNP) intravenous injection in a mouse model of mesial temporal lobe optimized the latter's bioavailability in the brain epileptic tissue of the kainic acid (KA) mouse model of MTLE. We aimed to assess the safety and antiepileptic potential of LIPU-enhanced SeNP delivery against KA-induced seizures using long-term intracranial electroencephalogram video recordings and evaluating neuroinflammation, astrogliosis, neuronal apoptosis and neurogenesis in the hippocampal tissues of mice.

RESULTS: First, we established that SeNP intravenous injection combined with LIPU-induced BBB disruption was the most effective method to achieve high and sustained selenium levels in the brain. The safety of this treatment was demonstrated after three treatment sessions, 1-week apart, with no adverse effects observed. Our results further showed a significantly lower frequency of epileptic seizures (-90 %, P = 0.001) in KA mice treated with LIPU + SeNPs compared to sham-treated controls. Short- and long-term histological changes were seen after that combined regimen, including less aberrant neurogenesis in the hippocampus hilum, less neuronal death throughout the hippocampus and less hippocampal microglial activation, which might collectively contribute to the observed antiseizure effect.

CONCLUSION: SeNP injection combined with LIPU-induced BBB disruption demonstrated potential as a promising approach to reduce seizure activity in MTLE; however, statistical comparison did not conclusively establish superiority over SeNPs alone. Further investigations are necessary to consider translational studies in humans.},
}

@article {pmid40542951,
year = {2025},
author = {Chen, J and Sun, G and Zhang, Y and Chen, W and Zheng, X and Zhang, S and Hao, Y},
title = {Interactively Integrating Reach and Grasp Information in Macaque Premotor Cortex.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40542951},
issn = {1995-8218},
abstract = {Reach-to-grasp movements require integrating information on both object location and grip type, but how these elements are planned and to what extent they interact remains unclear. We designed a new experimental paradigm in which monkeys sequentially received reach and grasp cues with delays, requiring them to retain and integrate both cues to grasp the goal object with appropriate hand gestures. Neural activity in the dorsal premotor cortex (PMd) revealed that reach and grasp were similarly represented yet not independent. Upon receiving the second cue, the PMd continued encoding the first, but over half of the neurons displayed incongruent modulations: enhanced, attenuated, or even reversed. Population-level analysis showed significant changes in encoding structure, forming distinct neural patterns. Leveraging canonical correlation analysis, we identified a shared subspace preserving the initial cue's encoding, contributed by both congruent and incongruent neurons. Together, these findings reveal a novel perspective on the interactive planning of reach and grasp within the PMd, providing insights into potential applications for brain-machine interfaces.},
}

@article {pmid40541755,
year = {2025},
author = {Yang, A and Tian, J and Wang, W and Zhou, L and Zhou, K},
title = {Shared and distinct neural signatures of feature and spatial attention.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121296},
doi = {10.1016/j.neuroimage.2025.121296},
pmid = {40541755},
issn = {1095-9572},
abstract = {The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent activation during various visual attention tasks. However, these studies have been limited by small sample sizes and methodological constraints inherent in univariate analysis. Here, we utilized a between-subject whole-brain machine learning approach with a large sample size (N=235) to investigate the neural signatures of FA (FAS) and SA (SAS). Both FAS and SAS showed cross-task predictive capabilities, though inter-task prediction was weaker than intra-task prediction, suggesting both shared and distinct mechanisms. Specifically, the frontoparietal network exhibited the highest predictive performance for FA, while the visual network excelled in predicting SA, highlighting their respective prominence in the two attention processes. Moreover, both signatures demonstrated distributed representations across large-scale brain networks, as each cluster within the signatures was sufficient for predicting FA and SA, but none of them were deemed necessary for either FA or SA. Our study challenges traditional network-centric models of attention, emphasizing distributed brain functioning in attention, and provides comprehensive evidence for shared and distinct neural mechanisms underlying FA and SA.},
}

@article {pmid40502202,
year = {2025},
author = {Srinivasan, A and Wairagkar, M and Iacobacci, C and Hou, X and Card, NS and Jacques, BG and Pritchard, AL and Bechefsky, PH and Hochberg, LR and AuYong, N and Pandarinath, C and Brandman, DM and Stavisky, SD},
title = {Encoding of speech modes and loudness in ventral precentral gyrus.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40502202},
issn = {2692-8205},
abstract = {The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with an accuracy of 94% and 89% for two participants respectively, and corresponding neural preparatory activity could be detected hundreds of milliseconds before speech onset. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.},
}

@article {pmid40541523,
year = {2025},
author = {Cao, S and Yin, Y and Li, W and Liu, Z and Chen, Z},
title = {Time-varying formation control for heterogeneous multi-agent systems in the presence of actuator faults and deception attacks.},
journal = {ISA transactions},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.isatra.2025.06.004},
pmid = {40541523},
issn = {1879-2022},
abstract = {This paper explores the control of time-varying formations in a class of heterogeneous multi-agent systems. The key innovation lies in the simultaneous consideration of hybrid actuator faults and deception attacks. To achieve the control objective, a novel distributed double-layer control scheme, comprising a network layer and a physical layer, is proposed. In the network layer, a distributed observer with secure output feedback control is developed to mitigate severe deception attacks, ensuring that the mean square observer error remains within an acceptable range. In the physical layer, fault compensators are designed to address both additive and multiplicative faults. As a result, the followers achieve time-varying formation control, and closed-loop stability analysis is conducted using the Lyapunov method. Finally, to verify the validity of the theoretical findings, numerical simulations are subsequently conducted.},
}

@article {pmid40538971,
year = {2025},
author = {Miao, Y and Li, K and Zhao, W and Zhang, Y},
title = {EA-EEG: a novel model for efficient motor imagery EEG classification with whitening and multi-scale feature integration.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {94},
pmid = {40538971},
issn = {1871-4080},
abstract = {Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience and brain-computer interfaces (BCI) due to its high temporal resolution. In motor imagery EEG (MI-EEG) tasks, EEG signals reflect movement-related brain activity, making them ideal for BCI control. However, the non-stationary nature of MI-EEG signals poses significant challenges for classification, as frequency characteristics vary across tasks and individuals. Traditional preprocessing methods, such as bandpass filtering and standardization, may struggle to adapt to these variations, potentially limiting classification performance. To address this issue, this study introduces EA-EEG, an improved MI-EEG classification model that incorporates whitening as a preprocessing step to reduce channel correlation and enhance the model feature extraction ability. EA-EEG further leverages a multi-scale pooling strategy, combining convolutional networks and root mean square pooling to extract key spatial and temporal features, and applies prototype-based classification to improve MI-EEG classification performance. Experiments on the BCI4-2A and BCI4-2B datasets demonstrate that EA-EEG achieves state-of-the-art performance, with 85.33% accuracy (Kappa = 0.804) on BCI4-2A and 88.05% accuracy (Kappa = 0.761) on BCI4-2B, surpassing existing approaches. These results confirm EA-EEG's effectiveness in handling non-stationary MI-EEG signals, demonstrating its potential for robust BCI applications, including rehabilitation, prosthetic control, and cognitive monitoring.},
}

@article {pmid40538970,
year = {2025},
author = {Lin, C and Lu, H and Pan, C and Ma, S and Zhang, Z and Tian, R},
title = {MBRSTCformer: a knowledge embedded local-global spatiotemporal transformer for emotion recognition.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {95},
pmid = {40538970},
issn = {1871-4080},
abstract = {Emotion recognition is an essential prerequisite for realizing generalized BCI, which possesses an extensive range of applications in real life. EEG-based emotion recognition has become mainstream due to its real-time mapping of brain emotional activities, so a robust EEG-based emotion recognition model is of great interest. However, most existing deep learning emotion recognition methods treat the EEG signal as a whole feature extraction, which will destroy its local stimulation differences and fail to extract local features of the brain region well. Inspired by the cognitive mechanisms of the brain, we propose the multi-brain regions spatiotemporal collaboration transformer (MBRSTCfromer) framework for EEG-based emotion recognition. First, inspired by the prior knowledge, we propose the Multi-Brain Regions Collaboration Network. The EEG data are processed separately after being divided by brain regions, and stimulation scores are presented to quantify the stimulation produced by different brain regions and feedback on the stimulation degree to the MBRSTCfromer. Second, we propose a Cascade Pyramid Spatial Fusion Temporal Convolution Network for multi-brain regions EEG features fusion. Finally, we conduct comprehensive experiments on two mainstream emotion recognition datasets to validate the effectiveness of our proposed MBRSTCfromer framework. We achieved 98.63 % , 98.15 % , and 98.58 % accuracy on the three dimensions (arousal, valence, and dominance) on the DEAP dataset; and 97.66 % , 97.07 % , and 97.97 % on the DREAMER dataset.},
}

@article {pmid40536865,
year = {2025},
author = {Li, Y and Su, D and Yang, X and Wang, X and Zhao, H and Zhang, J},
title = {From Frequency to Temporal: Three Simple Steps Achieve Lightweight High-Performance Motor Imagery Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3579528},
pmid = {40536865},
issn = {1558-2531},
abstract = {OBJECTIVE: To address the challenges of high data noise and substantial model computational complexity in Electroencephalography (EEG)-based motor imagery decoding, this study aims to develop a decoding method with both high accuracy and low computational cost.

METHODS: First, frequency domain analysis was performed to reveal the frequency modeling patterns of deep learning models. Utilizing prior knowledge from brain science regarding the key frequency bands for motor imagery, we adjusted the convolution kernels and pooling sizes of EEGNet to focus on effective frequency bands. Subsequently, a residual network was introduced to preserve high-frequency detailed features. Finally, temporal convolution modules were used to deeply capture temporal dependencies, significantly enhancing feature discriminability.

RESULTS: Experiments were conducted on the BCI Competition IV 2a and 2b datasets. Our method achieved average classification accuracies of 86.23% and 86.75% respectively, surpassing advanced models like EEG-Conformer and EEG-TransNet. Meanwhile, the Multiply-accumulate operations (MACs) were 27.16M, a reduction of over 50% compared to the comparison models, and the Forward/Backward Pass Size was 14.33MB.

CONCLUSION: By integrating prior knowledge from brain science with deep learning techniques-specifically frequency domain analysis, residual networks, and temporal convolutions-it is possible to effectively improve the accuracy of EEG motor imagery decoding while substantially reducing model computational complexity.

SIGNIFICANCE: This paper employs the simplest and most fundamental techniques in its design, highlighting the critical role of brain science knowledge in model development. The proposed method demonstrates broad application potential.},
}

@article {pmid40536747,
year = {2025},
author = {Fei, SW and Chen, JL and Hu, YB},
title = {A novel time-frequency feature extraction method of EEG signals utilizing fractional synchrosqueezing wavelet transform.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {40536747},
issn = {2662-4737},
abstract = {In order to improve the accuracy of Electroencephalogram (EEG) classification, Fractional Synchrosqueezing Wavelet Transform (FSSWT) is proposed to effectively overcome the contradiction between energy concentration and frequency separation in traditional time-frequency analysis methods. Firstly, the principle of FSSWT is introduced, and the time-frequency transformation equation for FSSWT applied to multi-frequency signals is established. The examples of synthetic signal and EEG signal show that the proposed method can suppress the mode aliasing of MI-EEG significantly while maintaining high resolution characteristics, and the energy concentration and related intermediate indexes perform well. The experimental results show that the proposed FSSWT-EEGDNN-ResNet model achieves an average classification accuracy of 95.17% under the condition of the MI-EEG signals processed by FSSWT of eight subjects, demonstrating the effectiveness of FSSWT in EEG signal feature extraction and classification.},
}