@article {pmid40722299,
year = {2025},
author = {Zhang, Z and Lu, G},
title = {Multimodal Knowledge Distillation for Emotion Recognition.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
doi = {10.3390/brainsci15070707},
pmid = {40722299},
issn = {2076-3425},
abstract = {Multimodal emotion recognition has emerged as a prominent field in affective computing, offering superior performance compared to single-modality methods. Among various physiological signals, EEG signals and EOG data are highly valued for their complementary strengths in emotion recognition. However, the practical application of EEG-based approaches is often hindered by high costs and operational complexity, making EOG a more feasible alternative in real-world scenarios. To address this limitation, this study introduces a novel framework for multimodal knowledge distillation, designed to improve the practicality of emotion decoding while maintaining high accuracy, with the framework including a multimodal fusion module to extract and integrate interactive and heterogeneous features, and a unimodal student model structurally aligned with the multimodal teacher model for better knowledge alignment. The framework combines EEG and EOG signals into a unified model and distills the fused multimodal features into a simplified EOG-only model. To facilitate efficient knowledge transfer, the approach incorporates a dynamic feedback mechanism that adjusts the guidance provided by the multimodal model to the unimodal model during the distillation process based on performance metrics. The proposed method was comprehensively evaluated on two datasets based on EEG and EOG signals. The accuracy of the valence and arousal of the proposed model in the DEAP dataset are 70.38% and 60.41%, respectively. The accuracy of valence and arousal in the BJTU-Emotion dataset are 61.31% and 60.31%, respectively. The proposed method achieves state-of-the-art classification performance compared to the baseline method, with statistically significant improvements confirmed by paired t-tests (p < 0.05), and the framework effectively transfers knowledge from multimodal models to unimodal EOG models, enhancing the practicality of emotion recognition while maintaining high accuracy, thus expanding the applicability of emotion recognition in real-world scenarios.},
}

@article {pmid40722278,
year = {2025},
author = {Yazıcı, M and Ulutaş, M and Okuyan, M},
title = {Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
doi = {10.3390/brainsci15070685},
pmid = {40722278},
issn = {2076-3425},
abstract = {The electroencephalogram (EEG) is one of the most popular neurophysiological methods in neuroscience. Scalp EEG measurements are obtained using various numbers of channels for both clinical and research applications. This pilot study explores the effect of EEG channel count on motor imagery classification using source analysis in brain-computer interface (BCI) applications. Different channel configurations are employed to evaluate classification performance. This study focuses on mu band signals, which are sensitive to motor imagery-related EEG changes. Common spatial patterns are utilized as a spatiotemporal filter to extract signal components relevant to the right hand and right foot extremities. Classification accuracies are obtained using configurations with 19, 30, 61, and 118 electrodes to determine the optimal number of electrodes in motor imagery studies. Experiments are conducted on the BCI Competition III Dataset Iva. The 19-channel configuration yields lower classification accuracy when compared to the others. The results from 118 channels are better than those from 19 channels but not as good as those from 30 and 61 channels. The best results are achieved when 61 channels are utilized. The average accuracy values are 83.63% with 19 channels, increasing to 84.70% with 30 channels, 84.73% with 61 channels, and decreasing to 83.95% when 118 channels are used.},
}

@article {pmid40721281,
year = {2025},
author = {Ognard, J and Douri, D and El Hajj, G and Ghozy, S and Rohleder, M and Gentric, JC and Kadirvel, R and Kallmes, DF and Brinjikji, W},
title = {Future is Ven(o)us: A 5-year narrative update on the venous route for therapeutics in Neurointervention.},
journal = {AJNR. American journal of neuroradiology},
volume = {},
number = {},
pages = {},
doi = {10.3174/ajnr.A8942},
pmid = {40721281},
issn = {1936-959X},
abstract = {Over the past five years, transvenous (TV) techniques have rapidly expanded the neurointerventional landscape, offering new diagnostic and therapeutic strategies for a range of cerebrovascular conditions. This narrative review synthesizes contemporary evidence and technical advances across multiple venous applications, including TV embolization for arteriovenous malformations and dural fistulas, treatment of cerebrospinal fluid-venous fistulas, and venous sinus stenting for pulsatile tinnitus, intracranial hypertension, and skull-base leaks. Recent data underscore high efficacy rates and favorable safety profiles in carefully selected patients, often matching or surpassing traditional arterial approaches. Innovations such as fetal vein of Galen embolization, vein-targeted brain-computer interface implantation, and endovascular cerebrospinal fluid shunting exemplify the therapeutic versatility of venous access. However, procedural challenges, such as venous anatomy, access, and embolic control, require meticulous planning and advanced skillsets. Trials like TATAM and DIVE-IIN are and will shape evidence-based indications for TV therapy. With expanding indications and growing operator expertise, the venous route is evolving from a niche adjunct into a cornerstone of neurovascular care.ABBREVIATIONS: bAVM(s)＝ brain arteriovenous malformation(s); CVF(s)＝ cerebrospinal fluid-venous fistula(s); CVT＝ cerebral venous thrombosis; DAVF(s)＝ dural arteriovenous fistula(s); EVT＝ endovascular therapy; EVOH＝ ethylene-vinyl alcohol copolymer; IIH＝ idiopathic intracranial hypertension; JR-NET3＝ Japanese Registry of NeuroEndovascular Therapy; PT＝ pulsatile tinnitus; RPCT＝ retrograde pressure-cooker technique; SIH＝ spontaneous intracranial hypotension; sCSFL＝ spontaneous cerebrospinal fluid leak; SSWA＝ sigmoid sinus wallabnormality/abnormalities; TV＝ transvenous; TVE＝ transvenous embolization; VSS＝ venous sinus stenting.},
}

@article {pmid40720979,
year = {2025},
author = {Silva, AB and Liu, JR and Anderson, VR and Kurtz-Miott, CM and Hallinan, IP and Littlejohn, KT and Brosler, S and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF},
title = {Implications of shared motor and perceptual activations on the sensorimotor cortex for neuroprosthetic decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf50e},
pmid = {40720979},
issn = {1741-2552},
abstract = {OBJECTIVE: Neuroprostheses can restore communicative ability to people with paralysis by decoding intended speech motor movements from the sensorimotor cortex (SMC). However, overlapping neural populations in the SMC are also engaged in visual and auditory perceptual processing. The nature of these shared motor and perceptual activations and their potential to interfere with decoding are particularly relevant questions for speech neuroprostheses, as reading and listening are essential daily functions.

APPROACH: In two participants with vocal-tract paralysis and anarthria (ClinicalTrials.gov; NCT03698149), we developed an online electrocorticography (ECoG) based speech-decoding system that maintained accuracy and specificity to intended speech, even during common daily tasks like reading and listening. Offline, we studied the spectrotemporal characteristics and spatial distribution of reading, listening, and attempted-speech responses across our participants' ECoG arrays.

MAIN RESULTS: Across participants, the speech-decoding system had zero false-positive activations during 63.2 minutes of attempted speech and perceptual tasks, maintaining accuracy and specificity to volitional speech attempts. Offline, though we observed shared neural populations that responded to attempted speech, listening, and reading, we found they leveraged different neural representations with differentiable spectrotemporal responses. Shared populations localized to the middle precentral gyrus and may have a distinct role in speech-motor planning.

SIGNIFICANCE: Potential neuroprosthesis users strongly desire reliable systems that will retain specificity to volitional speech attempts during daily use. These results demonstrate a decoding framework for speech neuroprostheses that maintains this specificity and further our understanding of shared perceptual and motor activity on the SMC.},
}

@article {pmid40720264,
year = {2025},
author = {Cobilean, V and Mavikumbure, HS and Drake, D and Stuart, M and Manic, M},
title = {Investigating Membership Inference Attacks against CNN Models for BCI Systems.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3593443},
pmid = {40720264},
issn = {2168-2208},
abstract = {As Deep Learning (DL) algorithms become more widely adopted in healthcare applications, there is a greater emphasis on understanding and addressing potential privacy risks associated with these models. The purpose of this study is to investigate the privacy vulnerabilities of the Convolutional Neural Network (CNN) classifiers for Electroencephalogram (EEG) data in the Brain-Computer Interfaces (BCIs). Specifically, it focuses on the Membership Inference Attack (MIA), which seeks to determine if data from an individual were used in model training. The novelty of this work lies in its empirical analysis of MIA, specifically by addressing two key challenges that are less common in other domains: 1) datasets that are heterogeneous and 2) spatial-temporal design choices. Motivated by these challenges, we investigate the susceptibility to MIA based on: 1) specifics of the training dataset (number of participants, demographics), and 2) specifics of the CNN (such as architecture, regularization). Our experiments revealed that an adversary with limited knowledge of the model and its training process can compromise the privacy of training participants, noting that the same attack is not effective against deep learning models trained on image and tabular datasets. Some of our findings are: 1) training on diverse participant datasets improves the privacy of the most participants but increases risks of memorization and vulnerabilities for underrepresented groups; 2) regularization is less effective in defending against the MIA for EEG data CNN classifiers when compared to other types of input data; 3) depth and width of model architecture has no impact on membership attack effectiveness. We hope that the presented insights will assist future researchers develop more privacy-aware deep learning based BCI systems.},
}

@article {pmid40720262,
year = {2025},
author = {Wang, K and Liu, Y and Tian, F and Yi, W and Zhang, Y and Jung, TP and Xu, M and Ming, D},
title = {Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain-Computer Interfaces.},
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.2025.3592988},
pmid = {40720262},
issn = {1558-0210},
abstract = {Neurofeedback training (NFT) has been widely used in motor rehabilitation. However, NFT combined with motor imagery-based brain-computer interface (MI-BCI) faces challenges such as mental fatigue and non-personalized training strategies. Therefore, we proposed an adaptive NFT based on a VR game that simulates real-life motor tasks to improve training efficiency. We conducted a detailed comparative analysis of the efficiency of the VR-based NFT and traditional Graz-based NFT. Forty-eight healthy subjects were randomly assigned to five groups and underwent various NFT protocols. Among them, the subjects in the four experimental groups were required to perform the NFT three times over five days, including virtual or real scenarios, as well as unilateral or bilateral hands training. We evaluated training effects by analyzing EEG features and classification performance, while online recognition duration served as the primary measure for assessing the adaptive NFT strategy. EEG analysis showed that VR-based NFT significantly enhanced the Event-related desynchronization (ERD) activations in the sensorimotor cortices over five days. The VR-based NFT group achieved a classification accuracy of 81.85%, representing a 10.14% improvement from baseline, which exceeded the 6.43% increase observed in the Graz-based NFT group. Furthermore, implementing the adaptive NFT strategy reduced the mean task duration by over 30% compared to the fixed-time training protocol. The results demonstrated that the adaptive MI-BCI-based NFT in a VR game achieves superior training outcomes while reducing training duration. These findings suggest the promising potential for applying MI-BCI NFT with VR games in motor rehabilitation following a stroke.},
}

@article {pmid40719991,
year = {2025},
author = {Luo, X and Dong, J and Li, T},
title = {The Role of CCL11-CCR3 Induced Mitochondrial Dysfunction and Oxidative Stress in Cognitive Impairment in Early-onset Schizophrenia: Insights from Preclinical Studies.},
journal = {Inflammation},
volume = {},
number = {},
pages = {},
pmid = {40719991},
issn = {1573-2576},
support = {81920108018//National Nature Science Foundation of China Key Project/ ; },
abstract = {Abnormal cytokine expression has been implicated as a potential contributor to neurodegeneration. This study aimed to investigate the plasma cytokine profiles in patients with early-onset schizophrenia (SCZ) and to explore the molecular mechanisms underlying the role of the key cytokine CCL11 in contributing to cognitive impairment. Plasma concentrations of 44 cytokines were quantified in individuals with SCZ. The effects of CCL11 on mitochondrial function were examined in vitro using primary hippocampal neurons. An in vivo model was subsequently developed by administering CCL11 into the lateral ventricle. The impact of the CCL11-CCR3 signaling pathway on mitochondrial function, oxidative stress, and cognitive function within the hippocampus was assessed using a combination of behavioral testing, molecular biology experiments, transcriptomic analysis, and non-targeted metabolomics. In individuals with SCZ, CCL11 and IL-13 levels were notably higher than in controls. In vitro, CCL11 exposure caused mitochondrial dysfunction and increased reactive oxygen species in hippocampal neurons. In vivo, CCL11-treated mice showed cognitive deficits, mitochondrial fission, and neuroinflammation in the hippocampus. Comprehensive integration of transcriptomic and metabolomic data revealed that CCL11 significantly disrupted the Glucokinase/Glucose-6-phosphate metabolism pathway, coinciding with elevated metabolites indicative of oxidative damage. Finally, downregulation of the CCR3 receptor in the hippocampus mitigated CCL11-induced oxidative stress, mitochondrial dysfunction, and cognitive impairment. CCL11 causes cytotoxicity in neurons by increasing oxidative stress and mitochondrial dysfunction. In a mouse model, knockout of the CCR3 receptor alleviates CCL11-induced cognitive impairment, mitochondrial dysfunction, and oxidative stress.},
}

@article {pmid40666891,
year = {2025},
author = {Wang, Y and Cheng, L and Li, D and Lu, Y and Hopkins, WD and Sherwood, CC and Xu, T and Liu, C and Paxinos, G and Jiang, T and Chu, C and Fan, L},
title = {Evolutionary Convergence of the Arcuate Fasciculus in Marmosets and Humans.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40666891},
issn = {2692-8205},
abstract = {The marmoset is a highly vocal platyrrhine monkey that shares key anatomical and functional features with humans, offering insights into the evolution of brain connectivity. Although similarities in vocalization features with humans have been reported, it remains unclear whether marmosets possess an arcuate fasciculus (af) homolog. This study delineated white matter tracts in marmosets, establishing homologies with those observed in other primates, including macaques, chimpanzees, and humans. The presence of an af homolog in marmosets was confirmed by tracer and ultra-high-resolution diffusion magnetic resonance imaging datasets. We compared cortical connectivity patterns across these species and found the af in marmosets terminates in the ventral frontal cortex, with greater similarity to humans than macaques. Furthermore, we linked af connectivity with vocalization-related brain activation in both marmosets and humans. Collectively, our findings suggest that a dorsal pathway, which emerged early in marmoset evolution, has evolved convergently with humans, despite their distant phylogenetic kinship.},
}

@article {pmid40661574,
year = {2025},
author = {Hou, X and Iacobacci, C and Card, NS and Wairagkar, M and Singer-Clark, T and Kunz, EM and Fan, C and Kamdar, F and Hahn, N and Hochberg, LR and Henderson, JM and Willett, FR and Brandman, DM and Stavisky, SD},
title = {Error encoding in human speech motor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40661574},
issn = {2692-8205},
abstract = {Humans monitor their actions, including detecting errors during speech production. This self-monitoring capability also enables speech neuroprosthesis users to recognize mistakes in decoded output upon receiving visual or auditory feedback. However, it remains unknown whether neural activity related to error detection is present in the speech motor cortex. In this study, we demonstrate the existence of neural error signals in speech motor cortex firing rates during intracortical brain-to-text speech neuroprosthesis use. This activity could be decoded to enable the neuroprosthesis to identify its own errors with up to 86% accuracy. Additionally, we observed distinct neural patterns associated with specific types of mistakes, such as phonemic or semantic differences between the person's intended and displayed words. These findings reveal how feedback errors are represented within the speech motor cortex, and suggest strategies for leveraging these additional cognitive signals to improve neuroprostheses.},
}

@article {pmid40719383,
year = {2025},
author = {Greenbaum, D},
title = {Enhancing the Warfighter: Ethical, Legal, and Strategic Implications of Brain-Machine Interface-Enabled Military Exoskeletons.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-26},
doi = {10.1080/21507740.2025.2530952},
pmid = {40719383},
issn = {2150-7759},
abstract = {The integration of brain-machine interfaces (BMIs) with military exoskeletons represents a significant development in human-machine interaction, raising complex ethical, legal, and strategic challenges. Unlike conventional human enhancement technologies, BMI-exoskeleton systems translate neural intent directly into mechanical movement, generating new concerns regarding agency, accountability, long-term health outcomes, and the governance of neuroadaptive changes. This paper offers a structured interdisciplinary analysis, developing taxonomies of current technologies, tracing the historical trajectory of military exoskeleton development, and critically assessing the emerging convergence between exoskeletal augmentation and neural interface systems. We argue that BMI-exoskeletons constitute a distinct category of augmentation that blurs traditional boundaries between operator and tool, requiring governance frameworks attentive to both operational effectiveness and the ethical implications for individual service members, military institutions, and broader society. Drawing on research in engineering, neuroscience, military studies, and bioethics, we outline a comprehensive ethical-legal framework designed to guide the entire lifecycle of human enhancement-from recruitment and informed consent processes through active service, operational deployment, and post-discharge reintegration. Particular attention is given to autonomy, cybersecurity vulnerabilities, distributive justice, gender equity, and the risks associated with de-enhancement and neuroplastic adaptation. Recognizing the preliminary and rapidly evolving nature of empirical evidence in this domain, we emphasize the need for anticipatory, adaptive policy approaches that safeguard the dignity, rights, and long-term welfare of enhanced warfighters while ensuring that technological innovation proceeds with responsible, ethically-informed oversight.},
}

@article {pmid40719065,
year = {2025},
author = {Xu, G and Wang, Z and Xu, K and Zhu, J and Zhang, J and Wang, Y and Hao, Y},
title = {Decoding Handwriting Trajectories from Intracortical Brain Signals for Brain-to-Text Communication.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e05492},
doi = {10.1002/advs.202505492},
pmid = {40719065},
issn = {2198-3844},
abstract = {The potential to decode handwriting trajectories from brain signals has yet to be fully explored in clinical brain-computer interfaces (BCIs). Here, intracortical neural signals are recorded from a paralyzed individual during attempted handwriting of complex characters. An innovative decoding framework is introduced to address both shape and temporal distortions between neural activity and movement, effectively resolving the misalignment issue commonly encountered in clinical BCIs due to the lack of accurate movement labels. The results demonstrated the reconstruction of highly accurate and human-recognizable handwriting trajectories, significantly outperforming conventional methods. Furthermore, the new framework enabled effective multi-day data fusion, leading to additional improvements in trajectory quality. By employing a dynamic time warping approach to translate trajectories into text, a recognition rate up to 91.1% is achieved within a 1000-character database. Additionally, the framework is applied to reconstruct single-trial trajectories of English letters using a previously published dataset, achieving similarly high recognition rates. Collectively, these findings present a novel BCI decoding scheme capable of accurately reconstructing handwriting trajectories, demonstrating its applicability to both alphabetic and logographic brain-to-text translation. This approach has the potential to revolutionize communication for individuals with motor impairments by enabling accurate brain-to-text translation across diverse languages.},
}

@article {pmid40718756,
year = {2025},
author = {Kim, R and Liu, Y and Zhang, J and Xie, C and Luan, L},
title = {Towards Precise Synthetic Neural Codes: High-dimensional Stimulation with Flexible Electrodes.},
journal = {Npj flexible electronics},
volume = {9},
number = {1},
pages = {},
doi = {10.1038/s41528-025-00447-y},
pmid = {40718756},
issn = {2397-4621},
abstract = {Neural representations arise from the spatiotemporally structured activity of neuron populations, inherently residing in high-dimensional spaces. Writing specific information into the central nervous system requires precisely manipulating neural states within this framework. However, current neuromodulation methods lack the precision to fully address this complexity, presenting a significant challenge for advancing effective bidirectional interfaces. In this perspective, we advocate for high-dimensional stimulation as a systematic approach capable of approximating the high dimensionality of natural neural code for brain-machine interface applications. We outline key technological requirements on resolution, coverage, and safety, review recent advances in critical application areas, and highlight the promise of flexible electrode technology in enabling a transformative leap towards precise synthetic neural codes.},
}

@article {pmid40718596,
year = {2025},
author = {Chang, L and Yang, B and Zhang, J and Li, T and Feng, J and Xu, W},
title = {DSTA-Net: dynamic spatio-temporal feature augmentation network for motor imagery classification.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {118},
doi = {10.1007/s11571-025-10296-0},
pmid = {40718596},
issn = {1871-4080},
abstract = {Accurate decoding and strong feature interpretability of Motor Imagery (MI) are expected to drive MI applications in stroke rehabilitation. However, the inherent nonstationarity and high intra-class variability of MI-EEG pose significant challenges in extracting reliable spatio-temporal features. We proposed the Dynamic Spatio-Temporal Feature Augmentation Network (DSTA-Net), which combines DSTA and the Spatio-Temporal Convolution (STC) modules. In DSTA module, multi-scale temporal convolutional kernels tailored to the α and β frequency bands of MI neurophysiological characteristics, while raw EEG serve as a baseline feature layer to retain original information. Next, Grouped Spatial Convolutions extract multi-level spatial features, combined with weight constraints to prevent overfitting. Spatial convolution kernels map EEG channel information into a new spatial domain, enabling further feature extraction through dimensional transformation. And STC module further extracts features and conducts classification. We evaluated DSTA-Net on three public datasets and applied it to a self-collected stroke dataset. In tenfold cross-validation, DSTA-Net achieved average accuracy improvements of 6.29% (p < 0.01), 3.05% (p < 0.01), 5.26% (p < 0.01), and 2.25% over the ShallowConvNet on the BCI-IV-2a, OpenBMI, CASIA, and stroke dataset, respectively. In hold-out validation, DSTA-Net achieved average accuracy improvements of 3.99% (p < 0.01) and 4.2% (p < 0.01) over the ShallowConvNet on the OpenBMI and CASIA datasets, respectively. Finally, we applied DeepLIFT, Common Spatial Pattern, and t-SNE to analyze the contributions of individual EEG channels, extract spatial patterns, and visualize features. The superiority of DSTA-Net offers new insights for further research and application in MI. The code is available in https://github.com/CL-Cloud-BCI/DSTANet-code.},
}

@article {pmid40718569,
year = {2025},
author = {Lopez Blanco, C and Tyler, WJ},
title = {The vagus nerve: a cornerstone for mental health and performance optimization in recreation and elite sports.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1639866},
doi = {10.3389/fpsyg.2025.1639866},
pmid = {40718569},
issn = {1664-1078},
abstract = {Decades of physiological and psychological research into human performance and wellness have established a critical role for vagus nerve signaling in peak physical and cognitive performance. We outline models and perspectives that have emerged through neuroscience and psychophysiology studies to elucidate how the vagus nerve governs human performance through its influence on central nervous system functions and autonomic nervous system activity. These functions include the monitoring and regulation of cardio-respiratory activity, emotional responses, inflammation and physical recovery, cognitive control, stress resilience, and team cohesion. We briefly review some useful interventions such as transcutaneous auricular vagus nerve stimulation, heart-rate variability biofeedback, and controlled breathing as accessible tools for enhancing vagal tone, improving executive functioning under pressure, and mitigating fatigue and burnout. We describe how these approaches and their biological underpinnings are rooted by psychological models like the Yerkes-Dodson law and Polyvagal theory to contextualize their effects on athletic performance. These perspectives suppor recent shifts in sports science toward integrating vagal-centered approaches as scalable, evidence-based strategies that can enhance human performance and wellness.},
}

@article {pmid40717726,
year = {2025},
author = {Otarbay, Z and Kyzyrkanov, A},
title = {SVM-enhanced attention mechanisms for motor imagery EEG classification in brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1622847},
doi = {10.3389/fnins.2025.1622847},
pmid = {40717726},
issn = {1662-4548},
abstract = {Brain-Computer Interfaces (BCIs) leverage brain signals to facilitate communication and control, particularly benefiting individuals with motor impairments. Motor imagery (MI)-based BCIs, utilizing non-invasive electroencephalography (EEG), face challenges due to high signal variability, noise, and class overlap. Deep learning architectures, such as CNNs and LSTMs, have improved EEG classification but still struggle to fully capture discriminative features for overlapping motor imagery classes. This study introduces a hybrid deep neural architecture that integrates Convolutional Neural Networks, Long Short-Term Memory networks, and a novel SVM-enhanced attention mechanism. The proposed method embeds the margin maximization objective of Support Vector Machines directly into the self-attention computation to improve interclass separability during feature learning. We evaluate our model on four benchmark datasets: Physionet, Weibo, BCI Competition IV 2a, and 2b, using a Leave-One-Subject-Out (LOSO) protocol to ensure robustness and generalizability. Results demonstrate consistent improvements in classification accuracy, F1-score, and sensitivity compared to conventional attention mechanisms and baseline CNN-LSTM models. Additionally, the model significantly reduces computational cost, supporting real-time BCI applications. Our findings highlight the potential of SVM-enhanced attention to improve EEG decoding performance by enforcing feature relevance and geometric class separability simultaneously.},
}

@article {pmid40715543,
year = {2025},
author = {Xu, H and Huang, Q and Song, P and Chen, Y and Li, Q and Zhai, Y and Du, X and Ye, H and Bao, X and Mehmood, I and Tanigawa, H and Niu, W and Tu, Z and Chen, P and Zhang, T and Zhang, L and Zhao, X and Zhang, L and Wen, W and Cao, L and Yu, X},
title = {EEG neural indicator of temporal integration in the human auditory brain with clinical implications.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1109},
doi = {10.1038/s42003-025-08540-8},
pmid = {40715543},
issn = {2399-3642},
support = {32171044//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100827//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32271078//National Natural Science Foundation of China (National Science Foundation of China)/ ; LGF22H170006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Temporal integration, the process by which the auditory system combines sound information over a certain period to form a coherent auditory experience, is essential for auditory perception, yet its neural mechanisms remain underexplored. We use a "transitional click train" paradigm, which concatenates two click trains with slightly differing inter-click intervals (ICIs), to investigate temporal integration in the human brain. Using a 64-channel electroencephalogram (EEG), we recorded responses from healthy participants exposed to regular and irregular transitional click trains and conducted change detection tasks. Regular transitional click trains elicited significant change responses in the human brain, indicative of temporal integration, whereas irregular trains did not. These neural responses were modulated by length, contrast, and regularity of ICIs. Behavioral data mirrored EEG findings, showing enhanced detection for regular conditions compared to irregular conditions and pure tones. Furthermore, variations in change responses were associated with decision-making processes. Temporal continuity was critical, as introducing gaps between click trains diminished both behavioral and neural responses. In clinical assessments, 22 coma patients exhibited diminished or absent change responses, effectively distinguishing them from healthy individuals. Our findings identify distinct neural markers of temporal integration and highlight the potential of transitional click trains for clinical diagnostics.},
}

@article {pmid40715225,
year = {2025},
author = {Das, A and Singh, S and Kim, J and Ahanger, TA and Pise, AA},
title = {Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {27161},
pmid = {40715225},
issn = {2045-2322},
support = {No.RS-2022-00155857//Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University). Also supported part by Woosong university research fund 2024./ ; No.RS-2022-00155857//Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University). Also supported part by Woosong university research fund 2024./ ; },
abstract = {Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within BCI systems by leveraging advanced machine learning and deep learning techniques. The accurate classification of electroencephalogram (EEG) data is crucial for enhancing BCI performance. The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. The research evaluates the performance of five traditional machine learning classifiers- K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)-using the "PhysioNet EEG Motor Movement/Imagery Dataset". This dataset encompasses EEG data from various motor tasks, including both actual and imagined movements. Among the traditional classifiers, Random Forest achieved the highest accuracy of 91%, underscoring its efficacy in motor imagery classification within BCI systems. In addition to conventional approaches, the study also explores deep learning techniques, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks yielding accuracies of 88.18% and 16.13%, respectively. However, the proposed hybrid model, which synergistically combines CNN and LSTM, significantly surpasses both traditional machine learning and individual deep learning methods, achieving an exceptional accuracy of 96.06%. This substantial improvement highlights the potential of hybrid deep learning models to advance the state of the art in BCI systems, offering a more robust and precise approach to motor imagery classification.},
}

@article {pmid40714477,
year = {2025},
author = {Wang, J and Zhao, S and Luo, Z and Zhou, Y and Li, S and Pan, G},
title = {EEGMamba: An EEG foundation model with Mamba.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107816},
doi = {10.1016/j.neunet.2025.107816},
pmid = {40714477},
issn = {1879-2782},
abstract = {Electroencephalography (EEG) captures brain activity and has been widely used in clinic and brain-computer interfaces (BCIs). Classic EEG decoding methods rely on supervised learning, limiting their performance and generalizability. Inspired by the revolutionary impact of large models in other fields, researchers are now investigating EEG foundation models. Recently, state space models (SSMs), such as Mamba, have demonstrated strong sequence modeling capabilities, which may be suitable to model the spatiotemporal dependencies of EEG signals. However, the application of Mamba for EEG representation learning remains largely unexplored. In this paper, we investigate the potential of Mamba for learning generic EEG representations and propose a novel EEG foundation model, EEGMamba. Specifically, we employ Mamba encoder as the backbone of EEGMamba to model the spatiotemporal dependencies among EEG patches. Meanwhile, we use patch-based masked EEG reconstruction to learn generic EEG representations. EEGMamba is pre-trained on a large and diverse EEG corpus (16,724 h) from five datasets. We evaluate EEGMamba on up to six downstream BCI tasks using six public datasets. EEGMamba achieves the state-of-the-art performance across all the tasks, demonstrating its strong capability and generalizability.},
}

@article {pmid40714230,
year = {2025},
author = {Gao, C and Wu, X and Ma, L and Li, D and Wang, Y and Guo, C and Li, W and Wang, H and Chu, C and Madsen, KH and Fan, L},
title = {Iterative Prior-Guided Parcellation (iPGP) for Capturing Inter-Subject and Inter-Nuclei Variability in Thalamic Mapping.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121399},
doi = {10.1016/j.neuroimage.2025.121399},
pmid = {40714230},
issn = {1095-9572},
abstract = {The thalamus, a critical relay station in the brain, consists of multiple nuclei that play essential roles in various brain circuits. Identifying these nuclei is crucial for understanding how thalamic structures influence cognitive functions. However, genetic and environmental factors introduce substantial variability in thalamic parcellation patterns, posing both challenges and opportunities for individualized mapping of thalamic function. This study proposes an iterative prior-guided parcellation (iPGP) framework to construct individualized thalamic parcellations. The iPGP method utilizes the Morel histological atlas as prior guidance, incorporates spatially constrained local diffusion characteristics as features, and employs an iterative framework to optimize an individual-specific parcellation model. As a result, iPGP automatically adapts to individual thalamic contrast variations, producing personalized and anatomically consistent parcellations. Through test-retest assessments, iPGP demonstrated a high degree of intra-subject reproducibility. By evaluating inter-subject and inter-nuclei variability, iPGP exhibited strong adaptability across different age groups while capturing subject-specific and region-specific variability. Furthermore, thalamic parcellations generated by iPGP showed significant associations with adolescent age and adult behavioral-cognitive scores. Our findings suggest that iPGP effectively captures inter-subject and inter-nuclei variability in thalamic parcellation, highlighting its potential for advancing thalamic mapping in exploring brain function.},
}

@article {pmid40712594,
year = {2025},
author = {Huang, J and Yang, P and Xiong, B and Lv, Y and Wang, Q and Wan, B and Zhang, Z},
title = {Mixup-based data augmentation for enhancing few-shot SSVEP detection performance.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf467},
pmid = {40712594},
issn = {1741-2552},
abstract = {Few-shot steady-state visual evoked potential (SSVEP) detection remains a major challenge in brain-computer interface (BCI) systems, as limited calibration data often leads to degraded performance. This study aims to enhance few-shot SSVEP detection through an effective data augmentation strategy. Approach: We propose a mixup-based data augmentation method that generates synthetic trials by linearly interpolating between real SSVEP signals extracted using a sliding window strategy. The interpolation weight is optimized by maximizing the similarity between the mixed signal and both the template and reference signals. The augmented data is then used to train spatial filters for improved SSVEP detection. Main results: The proposed method was evaluated on two benchmark SSVEP datasets using task-related component analysis (TRCA) and INS-SF as spatial filters. Results demonstrate that the mixup-based augmentation significantly improves detection accuracy under few-shot conditions, outperforming existing augmentation and baseline methods. Significance: The mixup-based method offers an effective and practical solution for enhancing SSVEP decoding with limited data, reducing calibration time, and improving BCI systems' usability in real-world scenarios.},
}

@article {pmid40712572,
year = {2025},
author = {Wang, J and Liu, Y and Ma, Y and Feng, Y and Lin, L and Ping, A and Tian, F and Zhang, X and Berman, AJL and Bollmann, S and Polimeni, JR and Roe, AW},
title = {In vivo 7 Tesla MRI of non-human primate intracortical microvascular architecture.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.05.028},
pmid = {40712572},
issn = {1097-4199},
abstract = {Intracortical arterioles are key locations for blood flow regulation and oxygen supply in the brain and are critical to brain health and disease. However, imaging such small (<100-μm-sized) vessels in humans is challenging. Here, using non-human primates as a model, we developed a capability for imaging microvasculature in vivo with a clinical 7 T MRI scanner. Using simulations, we identified parameters for imaging intracortical vessels with slow flow and combined this with high-resolution imaging (64 × 64 μm[2] in-plane). Across large swaths of occipital, parietal, and temporal cortex, arrays of intracortical arterioles and venules were observed in gyral crowns and deep within sulcal folds. Systematic arteriole-venule patterns revealed potential architecture of input-output flow relationships. Even single vessels could be followed across cortical laminae. As a first step toward imaging microvasculature in humans, this method introduces a new technology and animal model for understanding relationships between functional and vascular architectures.},
}

@article {pmid40712216,
year = {2025},
author = {Li, S and Xu, R and Wang, X and Cichocki, A and Jin, J},
title = {Dual branch neural network with dynamic learning mechanism for P300-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107876},
doi = {10.1016/j.neunet.2025.107876},
pmid = {40712216},
issn = {1879-2782},
abstract = {Brain-computer interface (BCI) system offers an alternative or supplementary means of interaction for individuals with disabilities. P300 speller is a commonly utilized BCI system due to its high stability, and reliability and without intensive user training. Nevertheless, the inherent class imbalance within P300 datasets predisposes the system to overfit, potentially impacting the classification performances. Existing class rebalancing methods mainly rely on resampling or adjusting the class weight with a fixed value, thus it is still tricky to ensure that the output is evenly balanced. To mitigate the above class imbalance issue, this study proposes a dual branch learning (DBL) method that concurrently considers feature representation and class imbalance. This approach involves the ingestion of two distinct sample types-uniformly sampled and reverse-sampled data-into the feature extraction and classification modules during the training phase. Furthermore, a dynamic learning mechanism is implemented to incrementally emphasize minority class samples (specifically the P300 component) as training progresses. The effectiveness of the proposed DBL method is proved using both publicly accessible and self-collected datasets in a subject-dependent scheme. The proposed DBL method can achieve an accuracy of 97.37 % and 88.72 % in the above datasets. Besides, it provides superior and more reliable results compared with several deep learning and rebalancing methods. These findings highlight the promising potential of the proposed DBL framework in P300-based BCI.},
}

@article {pmid40710265,
year = {2025},
author = {Ma, S and Situ, Z and Peng, X and Li, Z and Huang, Y},
title = {Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {7},
pages = {},
pmid = {40710265},
issn = {2313-7673},
support = {2024ZD0715801//The National Science and Technology Major Project of China/ ; },
abstract = {Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical application. This study focuses on rehabilitation training scenarios, aiming to capture the motor intentions of patients with partial or complete motor impairments (such as stroke survivors) and provide feedforward control commands for exoskeletons. This study developed an EEG acquisition protocol specifically for use with lower-limb rehabilitation motor imagery (MI). It systematically explored preprocessing techniques, feature extraction strategies, and multi-classification algorithms for multi-task MI-EEG signals. A novel 3D EEG convolutional neural network (3D EEG-CNN) that integrates time/frequency features is proposed. Evaluations on a self-collected dataset demonstrated that the proposed model achieved a peak classification accuracy of 66.32%, substantially outperforming conventional approaches and demonstrating notable progress in the multi-class classification of lower-limb motor imagery tasks.},
}

@article {pmid40709513,
year = {2025},
author = {Guo, W and Wang, H and Deng, W and Dong, Z and Liu, Y and Luo, S and Yu, J and Huang, X and Chen, Y and Ye, J and Song, J and Jiang, Y and Li, D and Wang, W and Sun, X and Kuang, W and Qiu, C and Cheng, N and Li, W and Zhang, W and Liu, Y and Tang, Z and Du, X and Greenshaw, AJ and Zhang, L and Li, T},
title = {Impact of early detection and management of emotional distress on length of stay in non-psychiatric inpatients: A retrospective hospital-based cohort study.},
journal = {Chinese medical journal},
volume = {},
number = {},
pages = {},
pmid = {40709513},
issn = {2542-5641},
abstract = {BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients.

METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS (n = 178,883) and non-BS-GPS (n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score ≥11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively.

RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge.

CONCLUSION: Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.},
}

@article {pmid40708811,
year = {2025},
author = {Tsytsarev, V and Volnova, A and Rojas, L and Sanabria, P and Ignashchenkova, A and Ortiz-Rivera, J and Alves, J and Inyushin, M},
title = {Vectorial principles of sensorimotor decoding.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1612626},
pmid = {40708811},
issn = {1662-5161},
abstract = {This review explores the vectorial principles underlying sensorimotor decoding across diverse biological systems. From the encoding of light wavelength in retinal cones to direction-specific motor cortex activity in primates, neural representations frequently rely on population vector coding-a scheme, in which neurons with directional or modality-specific preferences integrate their activity to encode stimuli or motor commands. Early studies on color vision and motor control introduced concepts of vector summation and neuronal tuning, evolving toward more precise models such as the von Mises distribution. Research in invertebrates, including leeches and snails, reveals that even simple nervous systems utilize population vector principles for reflexes and coordinated movements. Furthermore, analysis of joint limb motion suggests biomechanical optimization aligned with Fibonacci proportions, facilitating efficient neural and mechanical control. The review highlights that motor units and neurons often display multimodal or overlapping tuning fields, reinforcing the need for population-based decoding strategies. These findings suggest a unifying vectorial framework for sensory and motor coding, with implications for periprosthetic and brain-machine interface.},
}

@article {pmid40708808,
year = {2025},
author = {Chowdhury, AT and Hassanein, A and Al Shibli, AN and Khanafer, Y and AbuHaweeleh, MN and Pedersen, S and Chowdhury, MEH},
title = {Neural signals, machine learning, and the future of inner speech recognition.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1637174},
pmid = {40708808},
issn = {1662-5161},
abstract = {Inner speech recognition (ISR) is an emerging field with significant potential for applications in brain-computer interfaces (BCIs) and assistive technologies. This review focuses on the critical role of machine learning (ML) in decoding inner speech, exploring how various ML techniques improve the analysis and classification of neural signals. We analyze both traditional methods such as support vector machines (SVMs) and random forests, as well as advanced deep learning approaches like convolutional neural networks (CNNs), which are particularly effective at capturing the dynamic and non-linear patterns of inner speech-related brain activity. Also, the review covers the challenges of acquiring high-quality neural signals and discusses essential preprocessing methods for enhancing signal quality. Additionally, we outline and synthesize existing approaches for improving ISR through ML, that can lead to many potential implications in several domains, including assistive communication, brain-computer interfaces, and cognitive monitoring. The limitations of current technologies were also discussed, along with insights into future advancements and potential applications of machine learning in inner speech recognition (ISR). Building on prior literature, this work synthesizes and organizes existing ISR methodologies within a structured mathematical framework, reviews cognitive models of inner speech, and presents a detailed comparative analysis of existing ML approaches, thereby offering new insights into advancing the field.},
}

@article {pmid40707971,
year = {2025},
author = {Ji, X and Lu, X and Xu, Y and Zhang, W and Yang, H and Yin, C and Wang, H and Ren, C and Ji, Y and Li, Y and Huang, G and Shen, Y},
title = {Effects and neural mechanisms of a brain-computer interface-controlled soft robotic glove on upper limb function in patients with subacute stroke: a randomized controlled fNIRS study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {171},
pmid = {40707971},
issn = {1743-0003},
support = {No.Q202414//Youth Project of the Wuxi Municipal Health Commission/ ; No.2022YFC2009700//National Key Research & Development Program of China/ ; No.BE2023023-2//the Key Project of Jiangsu Province's Key Research and Development Program/ ; No.BE2023034//the Competitive Project of Jiangsu Province's Key Research and Development Program/ ; No.JBGS202414//Jiangsu Province Hospital clinical diagnosis and treatment of technological innovation "Open bidding for selecting the best candidates" project/ ; 2025-K10//Open Research Fund of State Key Laboratory of Digital Medical Engineering/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Spectroscopy, Near-Infrared ; *Robotics/instrumentation ; Aged ; *Stroke/physiopathology/complications ; *Paresis/rehabilitation/physiopathology/etiology ; Adult ; },
abstract = {BACKGROUND AND PURPOSE: The brain-computer interface-based soft robotic glove (BCI-SRG) holds promise for upper limb rehabilitation in subacute stroke patients, yet its efficacy and neural mechanisms are unclear. This study aimed to investigate the therapeutic effects and neural mechanisms of BCI-SRGs by functional near-infrared spectroscopy (fNIRS).

METHODS: Forty subacute stroke patients with left-sided hemiparesis were randomized into the BCI-SRG (n = 20) and soft robotic glove (SRG) (n = 20) groups. Both groups received 20 sessions of intervention over 4 weeks in addition to conventional rehabilitation. The BCI-SRG group was trained using a soft robotic glove controlled by a brain‒computer interface (BCI), whereas the SRG group used the same soft robotic glove without BCI control. The clinical outcomes included the Action Research Arm Test (ARAT), the Fugl-Meyer Assessment Upper Limb (FMA-UL), and Modified Barthel Index (MBI) scores. In addition, fNIRS was used to explore potential clinical brain mechanisms. All assessments were performed before treatment and after 4 weeks of treatment.

RESULTS: A total of 39 participants completed the intervention and clinical assessments (BCI-SRG: n = 20; SRG: n = 19). Compared with the SRG group, the BCI-SRG group showed greater improvements in the ARAT (Z = - 2.139, P = 0.032) and FMA-UL (Z = - 2.588, P = 0.010), with no notable difference in the MBI (Z = - 1.843, P = 0.065). fNIRS data were available for 35 participants (BCI-SRG: n = 17; SRG: n = 18). Within-group comparisons revealed significant postintervention increases in cortical activation in the bilateral sensorimotor cortex (SMC) and medial prefrontal cortex (MPFC) in the BCI-SRG group, whereas no significant changes were observed in the SRG group. Between-group comparisons further revealed significantly greater changes in HbO concentrations in the BCI-SRG group than in the SRG group across the same cortical regions. Moreover, changes in prefrontal activation (post-pre) were positively correlated with improvements in ARAT scores, with significant correlations observed in the left dorsal lateral prefrontal cortex (LDLPFC) (Ch9, r = 0.592, P = 0.012; Ch25, r = 0.488, P = 0.047) and right dorsal lateral prefrontal cortex (RDLPFC) (Ch19, r = 0.671, P = 0.003).

CONCLUSIONS: BCI-SRG training significantly enhances upper limb function and facilitates bilateral motor and sensory cortical reorganization. PFC activation is correlated with functional improvements, suggesting a potential mechanism underlying the benefits of rehabilitation in stroke patients.

TRIAL REGISTRATION: This trial was registered under the Chinese Clinical Trial Registry (ChiCTR2400082786) and was retrospectively registered on April 8, 2024.},
}

Humans
*Brain-Computer Interfaces
Male
Female
Middle Aged
*Stroke Rehabilitation/methods/instrumentation
*Upper Extremity/physiopathology
Spectroscopy, Near-Infrared
*Robotics/instrumentation
Aged
*Stroke/physiopathology/complications
*Paresis/rehabilitation/physiopathology/etiology
Adult

@article {pmid40707673,
year = {2025},
author = {Zhang, X and Li, M and Chen, Y and Liu, J and Zhang, J and Shao, C and Deng, B and Zhang, J and Wang, T and Cao, J and Xu, X and He, Q and Yang, B and Shao, X and Ying, M},
title = {Deubiquitinase USP6 stabilizes oncogenic RUNX1 fusion proteins to promote the leukemic potential and malignant progression.},
journal = {Leukemia},
volume = {},
number = {},
pages = {},
pmid = {40707673},
issn = {1476-5551},
support = {No. 82273942//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {RUNX1-rearranged leukemia is one of the most common subtypes of leukemia associated with genetic abnormalities. Although the majority of patients respond to chemotherapy, relapse and long-term adverse effects remain significant challenges. RUNX1 fusions, resulting from chromosomal rearrangements, are pivotal oncogenic drivers, with over 70 distinct variants identified. Therefore, elucidating their regulatory mechanisms may help to develop novel therapeutic strategies. Herein, we identify a universal deubiquitinase, USP6, that stabilizes RUNX1 fusion proteins with different partners. Importantly, USP6 is specifically upregulated in RUNX1-rearranged leukemia and strongly correlates with poor patient outcomes. Mechanistically, USP6 stabilizes RUNX1 fusions to facilitate the formation of phase separation, leading to robust transcriptional activation of the fusions. Depletion of USP6 dramatically inhibits proliferation and induces differentiation of RUNX1-rearranged leukemic cells. The marketed drug auranofin is identified as a potential USP6 inhibitor, which induces degradation of different RUNX1 fusions, further triggering myeloid differentiation and arresting xenograft tumor growth. Notably, auranofin exhibits selective therapeutic efficacy in patient-derived leukemia blasts from RUNX1-rearranged cases. Together, we not only uncover a new biological function of USP6 in regulating the transcriptional activity of RUNX1 fusions but also validate USP6 as a promising drug target and auranofin as a candidate therapy for RUNX1-rearranged leukemia.},
}

@article {pmid40707358,
year = {2025},
author = {Odermatt, IA and Schulthess-Lutz, M and Mihelj, E and Howell, P and Heimhofer, C and McMackin, R and Ruddy, K and Freund, P and Kikkert, S and Wenderoth, N},
title = {TMS-based neurofeedback training of mental finger individuation induces neuroplastic changes in the sensorimotor system.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.2189-24.2025},
pmid = {40707358},
issn = {1529-2401},
abstract = {Neurofeedback (NF) training based on motor imagery is increasingly used in neurorehabilitation with the aim to improve motor functions. However, the neuroplastic changes underpinning these improvements are poorly understood. Here, we used mental 'finger individuation', i.e., the selective facilitation of single finger representations without producing overt movements, as a model to study neuroplasticity induced by NF. To enhance mental finger individuation, we used transcranial magnetic stimulation (TMS)-based NF training. During motor imagery of individual finger movements, healthy female and male human participants were provided visual feedback on the size of motor evoked potentials, reflecting their finger-specific corticospinal excitability. We found that TMS-NF improved the mental activation of finger-specific representations. First, intracortical inhibitory circuits in the primary motor cortex were tuned after training such that inhibition was selectively reduced for the finger that was mentally activated. Second, motor imagery finger representations in areas of the sensorimotor system assessed with functional MRI became more distinct after training. Together, our results indicate that the neural underpinnings of finger individuation, a well-known model system for neuroplasticity, can be modified using TMS-NF guided motor imagery training. These findings demonstrate that TMS-NF induces neuroplasticity in the sensorimotor system, highlighting the promise of TMS-NF on the recovery of fine motor function.Significance statement The activation of sensorimotor representations through motor imagery can be used to control brain-computer interfaces (BCIs) as assistive devices or training interventions. Here, we investigated how improvements in BCI control may change sensorimotor representations activated through motor imagery. We used BCI-neurofeedback based on TMS that allows for finger-specific feedback on corticospinal excitability. Therefore, this training can be used to practice and improve mental finger individuation, providing a model to study neuroplasticity. We demonstrate that motor imagery representations became more finger-specific after training, as evident in the tuning of intracortical inhibition and more distinct fMRI activation patterns in the sensorimotor system. These findings show that BCI training induces neuroplasticity in the sensorimotor system and shapes sensorimotor representations activated through motor imagery.},
}

@article {pmid40706724,
year = {2025},
author = {Kim, YS and Kim, CU and Han, H and Kim, MY and Choi, SI and Im, CH},
title = {Performance enhancement of steady-state visual evoked field-based brain-computer interfaces using spatial distribution of synchronization index in MEG channel space.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121391},
doi = {10.1016/j.neuroimage.2025.121391},
pmid = {40706724},
issn = {1095-9572},
abstract = {The development of helmet-type magnetoencephalography (MEG) systems that do not require liquid helium (e.g., OPM-MEG) has sparked growing interest in steady-state visual evoked field (SSVEF)-based brain-computer interfaces (BCIs). Unlike electroencephalography (EEG), MEG records less distorted signals with a high spatial resolution, covering the entire head without requiring cumbersome electrode attachment. However, conventional algorithms, such as the filter bank-driven multivariate synchronization index (FBMSI), are prone to misclassification in ambiguous cases where the differences between synchronization indices (S indices) are minimal. Additionally, these algorithms fail to fully exploit high spatial resolution and whole-head coverage of MEG. To address these limitations, this study proposes a novel, calibration-free SSVEF classification algorithm termed Spatial Distribution Analysis (SDA). The SDA algorithm utilizes the center of gravity of the S index distribution in the MEG channel space to enhance classification accuracy. Experimental evaluations with 20 participants using a helmet-type SQUID MEG system demonstrated that the proposed SDA algorithm achieved significantly higher classification accuracy and information transfer rate (ITR) across all window sizes. Notably, the largest improvements of 5.76% in accuracy and 4.87 bits/min in ITR were reported for a window size of 2.5 s. Furthermore, the generalizability of the SDA algorithm was validated on an OPM-MEG dataset, showing performance improvements across all window sizes. The SDA algorithm also mitigated misclassification due to adjacent stimuli and showed short time delay of 0.0907 s, enough to be used for real-time BCIs. These findings highlight the potential of SDA algorithm to enhance the overall performance of SSVEF-based BCI.},
}

@article {pmid40705590,
year = {2025},
author = {Ko, BK and Lee, SH and Lee, SW},
title = {Imagined Speech Detection Using Multi-Receptive CNN for Asynchronous BCI Communication and Neurorehabilitation.},
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.2025.3592312},
pmid = {40705590},
issn = {1558-0210},
abstract = {Imagined speech-based brain-computer interface (BCI) facilitates brain signal-driven intuitive communication which holds great promise as an effective speech rehabilitation tool, enabling real-time, hands-free interaction for individuals with speech and motor impairments. While speech-based assistant systems rely on wake-word detection (e.g., "Hey Siri"), BCI-based communication system must capture imagined onset from EEG signals to turn on the 'brain switch' to further convey user's imagined command. Nevertheless, the absence of reliable ground truth for the endogenous paradigm adds to the complexity to train the model to capture exact onset from continuous EEG. To address these issues, we introduce a multi-receptive field convolutional neural network, designed to capture speech and idle states based on behaviorally-aligned EEG features. We propose a voice-based ground truth alignment method with voting strategy that aims to synchronize imagined speech with overt speech onset and offset, providing a structured approach for capturing speech events in asynchronous BCI systems. Furthermore, spectral and phonological analyses revealed that beta and alpha bands, as well as syllable count, appear to influence speech state discriminability. Evaluations on imagined and overt speech tasks, including pseudo-online experiments, demonstrate the potential to enhance asynchronous BCI systems, supporting real-time communication for both healthy and impaired individuals.},
}

@article {pmid40703721,
year = {2025},
author = {Alkhoury, L and O'Sullivan, J and Scanavini, G and Dou, J and Arora, J and Hamill, L and Patchell, A and Radanovic, A and Watson, WD and Lalor, EC and Schiff, ND and Hill, NJ and Shah, SA},
title = {Leveraging meaning-induced neural dynamics to detect covert cognition via EEG during natural language listening-a case series.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1616963},
pmid = {40703721},
issn = {1664-1078},
abstract = {At least a quarter of adult patients with severe brain injury in a disorder of consciousness may have cognitive abilities that are hidden due to motor impairment. In this case series, we developed a tool that extracted acoustic and semantic processing biomarkers from electroencephalography recorded while participants listened to a story. We tested our method on two male adolescent survivors of severe brain injury and showed evidence of acoustic and semantic processing. Our method identifies cognitive processing while obviating demands on attention, memory, and executive function. This lays a foundation for graded assessments of cognition recovery across the spectrum of covert cognition.},
}

@article {pmid40703668,
year = {2025},
author = {Wang, F and Luo, Z and Lv, W and Zhu, X},
title = {DTCNet: finger flexion decoding with three-dimensional ECoG data.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1627819},
pmid = {40703668},
issn = {1662-5188},
abstract = {ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal quality, particularly in the field of neural control. ECoG enables more accurate decoding of brain activity compared to traditional EEG. By obtaining cortical ECoG signals directly from the cerebral cortex, complex motor commands, such as finger movement trajectories, can be decoded more efficiently. However, existing studies still face significant challenges in accurately decoding finger movement trajectories. Specifically, current models tend to confuse the movement information of different fingers and fail to fully exploit the dependencies within time series when predicting long sequences, resulting in limited decoding performance. To address these challenges, this paper proposes a novel decoding method that transforms 2D ECoG data samples into 3D spatio-temporal spectrograms with time-stamped features via wavelet transform. The method further enables accurate decoding of finger bending by using a 1D convolutional network composed of Dilated-Transposed convolution, which together extract channel band features and temporal variations in tandem. The proposed method achieved the best performance among three subjects in BCI Competition IV. Compared with existing studies, our method made the correlation coefficient between the predicted multi-finger motion trajectory and the actual multi-finger motion trajectory exceed 80% for the first time, with the highest correlation coefficient reaching 82%. This approach provides new insights and solutions for high-precision decoding of brain-machine signals, particularly in precise command control tasks, and advances the application of BCI systems in real-world neuroprosthetic control.},
}

@article {pmid40703402,
year = {2025},
author = {Borra, D and Ma, M and Martinez-Martin, E and Xia, L},
title = {Editorial: Methods in brain-computer interfaces: 2023.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1647584},
pmid = {40703402},
issn = {1662-5161},
}

@article {pmid40703200,
year = {2025},
author = {Li, K and Zhang, J and Yu, B and Ward, MP and Liu, M and Liu, Y and Wang, Z and Chen, Z and Li, W and Wang, N and Zhao, Y and Yang, X and Yang, F and Wang, P and Zhang, Z},
title = {Meteorological, Socioeconomic, and Environmental Factors Influencing Human Brucellosis Occurrence in Yunnan, China, 2006-2021: A Bayesian Spatiotemporal Modeling Study.},
journal = {Transboundary and emerging diseases},
volume = {2025},
number = {},
pages = {8872434},
pmid = {40703200},
issn = {1865-1682},
mesh = {Humans ; China/epidemiology ; *Brucellosis/epidemiology ; Bayes Theorem ; Socioeconomic Factors ; Spatio-Temporal Analysis ; Risk Factors ; Meteorological Concepts ; Environment ; },
abstract = {Background: Brucellosis epidemics in Yunnan Province in southern China have increased and caused more impact in recent years. However, the epidemiological characteristics and driving factors for brucellosis have not been clearly described. The aim of this study was to analyze the spatiotemporal distribution and potential factors for human brucellosis (HB) in Yunnan Province, 2006-2021. Methods: HB data were obtained from the China National Notifiable Infectious Diseases Reporting Information System. Global spatial autocorrelation and spatial scanning statistics were used to analyze the spatial patterns of brucellosis. Zero-inflated negative binomial (ZINB) Bayesian spatiotemporal models were applied to the analysis of potential risk factors, including environmental, meteorological, and socioeconomic factors. Findings: Between 2006 and 2021, a total of 2794 brucellosis cases were reported. The central and western regions were the most severely affected. GDP showed a positive correlation with brucellosis risk when in the range 0-30.9 billion RMB, peaking with a relative risk (RR) of 13.64 (95% Bayesian credible interval [BCI]: 4.10, 49.10) at around 2.3 billion RMB. Conversely, a negative correlation was observed for GDP between 101 and 135 billion RMB, with the RR dropping to 0.14 (95% BCI: 0.01, 0.89) at 135 billion RMB. Brucellosis cases increased by 4.90% (95% BCI: 1.82%, 7.95%) per 1°C increase in temperature, while a 1° increase in slope reduced cases by 17.06% (95% BCI: 4.01%, 28.81%). Interpretation: Our findings suggest that socioeconomic factors play the greatest role in the occurrence of brucellosis in both northern and southern China; however, the effects of the environmental factors may be different between these areas. Differences in factors affecting each region need to be fully considered, and brucellosis prevention and control need to be adapted to these differences.},
}

Humans
China/epidemiology
*Brucellosis/epidemiology
Bayes Theorem
Socioeconomic Factors
Spatio-Temporal Analysis
Risk Factors
Meteorological Concepts
Environment

@article {pmid40702984,
year = {2025},
author = {Yu, Y and Wang, RM and Dong, Y and Jia, XZ and Wu, ZY},
title = {Neuroimaging correlates of genetics in patients with Wilson's disease.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf186},
pmid = {40702984},
issn = {1460-2199},
support = {81125009//National Natural Science Foundation of China/ ; 81701126//National Natural Science Foundation of China/ ; 188020-193810101/089//Research Foundation for Distinguished Scholars of Zhejiang University/ ; },
mesh = {Humans ; *Hepatolenticular Degeneration/genetics/diagnostic imaging/pathology/physiopathology ; Male ; Female ; Adult ; *Brain/pathology/diagnostic imaging/physiopathology ; Young Adult ; *Mutation/genetics ; Magnetic Resonance Imaging ; Neuroimaging ; Copper-Transporting ATPases/genetics ; Adolescent ; Middle Aged ; Atrophy ; },
abstract = {Wilson's disease is an inherited disorder of copper metabolism. Despite significant advancements in neuroimaging studies, prior research into the pathological mechanism of Wilson's disease has ignored the crucial impact of mutation on the disease. This study examined brain imaging in relation to mutation in patients with Wilson's disease. A total of 57 Wilson's disease patients and 25 healthy controls were recruited in the current research. Patients were classified as having either the p.R778L or the p.P992L mutation (N = 43) or other mutations (N = 14). Utilizing the amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and voxel-based morphology, the brain function and structure of Wilson's disease were explored. Compared to healthy controls, Wilson's disease patients with the p.R778L or p.P992L mutation showed greater atrophy in the bilateral putamen, caudate, globus pallidus, thalamus, amygdala, insula, and hippocampus. And these patients showed altered spontaneous neural activity in many more brain regions than healthy controls in three frequency bands. Significant correlation was found between altered brain volume and Unified Wilson's Disease Rating Scale neurological subscale scores. These findings reveal the functional and structural characteristics of Wilson's disease and emphasize the importance of exploring the neuroimaging correlation of genetic mutations in Wilson's disease.},
}

Humans
*Hepatolenticular Degeneration/genetics/diagnostic imaging/pathology/physiopathology
Male
Female
Adult
*Brain/pathology/diagnostic imaging/physiopathology
Young Adult
*Mutation/genetics
Magnetic Resonance Imaging
Neuroimaging
Copper-Transporting ATPases/genetics
Adolescent
Middle Aged
Atrophy

@article {pmid40702747,
year = {2025},
author = {Yang, A and Lv, X and Wang, H and Wang, X},
title = {Psychedelics, Spirituality, and Fundamentalism: A Brain Network Approach to Cognitive Flexibility and Rigidity.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00509},
pmid = {40702747},
issn = {1948-7193},
abstract = {This viewpoint reconceptualizes mysticism and fundamentalism as brain network disorders, with psychedelics like psilocybin, lysergic acid diethylamide, and N,N-dimethyltryptamine offering potential to modulate these states. By disrupting rigid neural patterns, psychedelics may foster cognitive flexibility, challenge inflexible belief systems, and offer therapeutic value for extremism and mental health disorders.},
}

@article {pmid40702190,
year = {2025},
author = {Kaifosh, P and Reardon, TR and , },
title = {A generic non-invasive neuromotor interface for human-computer interaction.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40702190},
issn = {1476-4687},
abstract = {Since the advent of computing, humans have sought computer input technologies that are expressive, intuitive and universal. While diverse modalities have been developed, including keyboards, mice and touchscreens, they require interaction with a device that can be limiting, especially in on-the-go scenarios. Gesture-based systems use cameras or inertial sensors to avoid an intermediary device, but tend to perform well only for unobscured movements. By contrast, brain-computer or neuromotor interfaces that directly interface with the body's electrical signalling have been imagined to solve the interface problem[1], but high-bandwidth communication has been demonstrated only using invasive interfaces with bespoke decoders designed for single individuals[2-4]. Here, we describe the development of a generic non-invasive neuromotor interface that enables computer input decoded from surface electromyography (sEMG). We developed a highly sensitive, easily donned sEMG wristband and a scalable infrastructure for collecting training data from thousands of consenting participants. Together, these data enabled us to develop generic sEMG decoding models that generalize across people. Test users demonstrate a closed-loop median performance of gesture decoding of 0.66 target acquisitions per second in a continuous navigation task, 0.88 gesture detections per second in a discrete-gesture task and handwriting at 20.9 words per minute. We demonstrate that the decoding performance of handwriting models can be further improved by 16% by personalizing sEMG decoding models. To our knowledge, this is the first high-bandwidth neuromotor interface with performant out-of-the-box generalization across people.},
}

@article {pmid40701672,
year = {2025},
author = {Kundi, H and Popma, JJ and Granada, JF and Leon, MB and Kodesh, A and Ascione, G and George, I and Latib, A and Thompson, JB and Popma, A and Alu, MC and Cohen, DJ},
title = {Outcomes in Older Patients Undergoing Surgical Aortic Valve Replacement With Concomitant Procedures.},
journal = {Journal of the American College of Cardiology},
volume = {86},
number = {4},
pages = {280-283},
doi = {10.1016/j.jacc.2025.05.021},
pmid = {40701672},
issn = {1558-3597},
}

@article {pmid40700800,
year = {2025},
author = {Peng, J and Jia, S and Zhang, J and Wang, Y and Yu, Z and Liu, JK},
title = {Decoding natural visual scenes via learnable representations of neural spiking sequences.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107863},
doi = {10.1016/j.neunet.2025.107863},
pmid = {40700800},
issn = {1879-2782},
abstract = {Visual input underpins cognitive function by providing the brain with essential environmental information. Neural decoding of visual scenes seeks to reconstruct pixel-level images from neural activity, a vital capability for vision restoration via brain-computer interfaces. However, extracting visual content from time-resolved spiking activity remains a significant challenge. Here, we introduce the Wavelet-Informed Spike Augmentation (WISA) model, which applies multilevel wavelet transforms to spike trains to learn compact representations that can be directly fed into deep reconstruction networks. When tested on recorded retinal spike data responding to natural video stimuli, WISA substantially improves reconstruction accuracy, especially in recovering fine-grained details. These results emphasize the value of temporal spike patterns for high-fidelity visual decoding and demonstrate WISA as a promising model for visual decoding.},
}

@article {pmid40700312,
year = {2025},
author = {Correia, P and Quintão, C and Quaresma, C and Vigário, R},
title = {A Framework for Corticomuscle Control Studies Using a Serious Gaming Approach.},
journal = {Methods and protocols},
volume = {8},
number = {4},
pages = {},
doi = {10.3390/mps8040074},
pmid = {40700312},
issn = {2409-9279},
support = {UI/BD/151321/2021//Fundação para a Ciência e Tecnologia (FCT, Portugal)/ ; },
abstract = {Sophisticated voluntary movements are essential for everyday functioning, making the study of how the brain controls muscle activity a central challenge in neuroscience. Investigating corticomuscular control through non-invasive electrophysiological recordings is particularly complex due to the intricate nature of neuronal signals. To address this challenge, we present a novel experimental methodology designed to study corticomuscular control using electroencephalography (EEG) and electromyography (EMG). Our approach integrates a serious gaming biofeedback system with a specialized experimental protocol for simultaneous EEG-EMG data acquisition, optimized for corticomuscular studies. This work introduces, for the first time, a method for assessing brain-muscle functional connectivity during the execution of a demanding motor task. By identifying neuronal sources linked to muscular activity, this methodology has the potential to advance our understanding of motor control mechanisms. These insights could contribute to improving clinical practices and fostering the development of novel brain-computer interface technologies.},
}

@article {pmid40699544,
year = {2025},
author = {Pan, H and Chen, Z and Xu, N and Wang, B and Hu, Y and Zhou, H and Perry, A and Kong, XZ and Shen, M and Gao, Z},
title = {Dissecting Social Working Memory: Neural and Behavioral Evidence for Externally and Internally Oriented Components.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40699544},
issn = {1995-8218},
abstract = {Social working memory (SWM)-the ability to maintain and manipulate social information in the brain-plays a crucial role in social interactions. However, research on SWM is still in its infancy and is often treated as a unitary construct. In the present study, we propose that SWM can be conceptualized as having two relatively independent components: "externally oriented SWM" (e-SWM) and "internally oriented SWM" (i-SWM). To test this external-internal hypothesis, participants were tasked with memorizing and ranking either facial expressions (e-SWM) or personality traits (i-SWM) associated with images of faces. We then examined the neural correlates of these two SWM components and their functional roles in empathy. The results showed distinct activations as the e-SWM task activated the postcentral and precentral gyri while the i-SWM task activated the precuneus/posterior cingulate cortex and superior frontal gyrus. Distinct multivariate activation patterns were also found within the dorsal medial prefrontal cortex in the two tasks. Moreover, partial least squares analyses combining brain activation and individual differences in empathy showed that e-SWM and i-SWM brain activities were mainly correlated with affective empathy and cognitive empathy, respectively. These findings implicate distinct brain processes as well as functional roles of the two types of SWM, providing support for the internal-external hypothesis of SWM.},
}

@article {pmid40697162,
year = {2025},
author = {Dohle, E and Swanson, E and Jovanovic, L and Yusuf, S and Thompson, L and Horsfall, HL and Muirhead, W and Bashford, L and Brannigan, J},
title = {Toward the Clinical Translation of Implantable Brain-Computer Interfaces for Motor Impairment: Research Trends and Outcome Measures.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e01912},
doi = {10.1002/advs.202501912},
pmid = {40697162},
issn = {2198-3844},
support = {FC001153/WT_/Wellcome Trust/United Kingdom ; FC001153/WT_/Wellcome Trust/United Kingdom ; FC001153/WT_/Wellcome Trust/United Kingdom ; //Rosetrees Trust and Stoneygate Trust/ ; },
abstract = {Implantable brain-computer interfaces (iBCIs) decode neural signals to control external effectors, offering potential to restore function in individuals with severe motor impairments, such as loss of limb function or speech. This systematic review examines the evolution of iBCI research and key bottlenecks to clinical translation, particularly the absence of standardized, clinically meaningful outcome measures. A comprehensive search of MEDLINE, Embase, and CINAHL identifies 112 studies, nearly half (49.1%) published since 2020. Eighty unique iBCI participants were identified, providing the most up-to-date estimate of global users. Research remains concentrated in the United States (83%), with growing contributions from Europe, China, and Australia. Electrocorticography (ECoG)-based devices increasingly emerge alongside micro-electrode arrays. iBCI devices are now being used to control a broader range of effectors, including robotic prosthetics and digital technologies. Although most (69.6%) studies reported outcome measures prospectively, these primarily related to decoding (69.6%) and task performance (62.5%), with only 17.9% assessing clinical outcomes. When cassessed, clinical outcomes were highly heterogeneous due to varied approaches across target populations. iBCIs show potential to restore functional independence at scale. However, challenges remain around cross-subject generalization, scalable implantation, and outcome standardization. Novel measures should be developed collaboratively with engineers, clinicians, and individuals with lived experience of motor impairment.},
}

@article {pmid40696184,
year = {2025},
author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y},
title = {Author Correction: Stress dynamically modulates neuronal autophagy to gate depression onset.},
journal = {Nature},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41586-025-09404-1},
pmid = {40696184},
issn = {1476-4687},
}

@article {pmid40695313,
year = {2025},
author = {de Borman, A and Wittevrongel, B and Van Dyck, B and Van Rooy, K and Carrette, E and Meurs, A and Van Roost, D and Van Hulle, MM},
title = {Speech mode classification from electrocorticography: transfer between electrodes and participants.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf2de},
pmid = {40695313},
issn = {1741-2552},
abstract = {Objective Speech brain-computer interfaces aim to restore communication for individuals who have lost the ability to speak by interpreting their brain activity and decoding the intended speech. As an initial component of these decoders, speech detectors have been developed to distinguish between the intent to speak and silence. However, it is important that these detectors account for real-life scenarios in which users may engage language-related brain areas-such as during reading or listening-without any intention to speak. Approach In this study, we analyze the interplay between different speech modes: speaking, listening, imagining speaking, reading and mouthing. We gathered a large dataset of 29 participants implanted with electrocorticography electrodes and developed a speech mode classifier. We also assessed how well classifiers trained on data from a specific participant transfer to other participants, both in the case of a single- and multi-electrode classifier. Main Results High accuracy was achieved using linear classifiers, for both single-electrode and multi-electrode configurations. Single-electrode classification reached 88.89% accuracy and multi-electrode classification 96.49% accuracy in distinguishing among three classes (speaking, listening, and silence). The best performing electrodes were located on the superior temporal gyrus and sensorimotor cortex. We found that single-electrode classifiers could be transferred across recording sites. For multi-electrode classifiers, we observed that transfer performance was higher for binary classifiers compared to multiclass classifiers, with the optimal source subject of the binary classifiers depending on the speech modes being classified. Significance Accurately detecting speech from brain signals is essential to prevent spurious outputs from a speech brain-computer interface and to advance its use beyond lab settings. To achieve this objective, the transfer between participants is particularly valuable as it can reduce training time, especially in cases where subject training is challenging.},
}

@article {pmid40694675,
year = {2025},
author = {Izac, M and N'Kaoua, B and Pillette, L and Jeunet-Kelway, C},
title = {[Improve athletes' performance with neurofeedback].},
journal = {Biologie aujourd'hui},
volume = {219},
number = {1-2},
pages = {51-58},
doi = {10.1051/jbio/2025001},
pmid = {40694675},
issn = {2105-0686},
mesh = {Humans ; *Neurofeedback/methods/physiology ; *Athletic Performance/physiology/psychology ; *Athletes/psychology ; Electroencephalography ; Cognition/physiology ; },
abstract = {In order to optimise their performance, athletes are looking for innovative, efficient and reliable training approaches. The development of electroencephalography and neurofeedback (NF) offers the opportunity to create innovative cognitive training procedures. Indeed, these technologies allow athletes to benefit from a feedback during mental training sessions and to objectively assess performance and progress. In addition, NF makes it possible to guide the athletes towards optimal cognitive strategies according to their objectives, and has a motivational dimension that pushes them to engage in the sessions. We first introduce the usefulness of NF to improve sports performance. Then, we review the current results concerning its efficiency. Finally, we provide an overview of the literature showing the heterogeneity of the studies published on the subject, focusing mainly on the aspects that could explain the variability of reported data.},
}

Humans
*Neurofeedback/methods/physiology
*Athletic Performance/physiology/psychology
*Athletes/psychology
Electroencephalography
Cognition/physiology

@article {pmid40694476,
year = {2025},
author = {Rangwani, R and Abbasi, A and Gulati, T},
title = {Effective cerebellar neuroprosthetic control after stroke.},
journal = {Cell reports},
volume = {44},
number = {8},
pages = {116030},
doi = {10.1016/j.celrep.2025.116030},
pmid = {40694476},
issn = {2211-1247},
abstract = {Brain-machine interfaces (BMIs) offer a viable option for restoring function in patients with motor disabilities post-stroke. Most BMI systems rely on signals from the motor cortex (M1), which is often compromised after stroke. The cerebellum, a subcortical structure involved in motor control, remains an underexplored source for neuroprosthetic control. Using chronic electrophysiological recordings in a rat stroke model, we show that cerebellar neural activity can effectively drive BMI control, performing comparably to M1-driven control. We observed this even in animals with motor impairments post-stroke. Simultaneous M1-cerebellum recordings during cerebellar BMI control revealed that cerebellar "direct" neurons driving the interface were influenced by both local cerebellar and distant M1 neurons. While cerebellar influence remained stable, M1's interaction with cerebellar direct neurons shifted from longer to shorter timescales after stroke. These findings highlight that cerebellar direct neural control is possible in the stroke brain and reveal changes in M1-cerebellar network dynamics post-stroke.},
}

@article {pmid40694466,
year = {2025},
author = {Zhang, C and Li, G and Wu, X and Gao, X},
title = {A Novel Hybrid Brain-Computer Interface Integrating Motor Imagery and Multiple Visual Stimuli.},
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.2025.3591616},
pmid = {40694466},
issn = {1558-0210},
abstract = {Brain-Computer Interface (BCI) that integrate Motor Imagery (MI) with Steady-State Visual Evoked Potentials (SSVEP) or Overt Spatial Attention (OSA) have demonstrated superior performance compared to MI only BCI. Nonetheless, the exploration of BCI that combine MI with visual tasks remains limited, and the synchronization between MI and visual tasks is often weak. To address this gap, our study introduces a novel BCI paradigm that combines MI with two visual tasks: SSVEP and OSA. In this paradigm, dynamic images depicting left and right arm movements flash at distinct frequencies, serving as visual stimuli positioned on both sides of the screen. Four classification methods are used for testing. The MI+SSVEP+OSA paradigm achieves higher average accuracy than the MI, MI+SSVEP, and MI+OSA paradigms. This validates the effectiveness of our novel paradigm and confirms the feasibility of simultaneously integrating MI with two visual stimuli. Moreover, we observe that the integration of SSVEP offers significant improvements, especially for participants who exhibit limited performance in the MI only paradigm. Additionally, our results indicate comparable performance between the MI+SSVEP and MI+OSA paradigms. Overall, this study offers valuable insights that can guide future research in hybrid BCI development, paving the way for more efficient and user-friendly BCI.},
}

@article {pmid40694230,
year = {2025},
author = {Afkhaminia, F and Shamsollahi, MB and Bahraini, T},
title = {A distributed adaptive network framework for ERP-Based classification of multichannel EEG signals.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {40694230},
issn = {2662-4737},
abstract = {Understanding brain function is one of the most challenging areas in brain signal processing. This study introduces a novel framework for electroencephalography (EEG) signal classification based on distributed adaptive networks using diffusion strategy. Our approach models the brain as a multitask network, where EEG electrodes are considered as nodes of this network. The network parameters are dynamically optimized based on the data from the nodes and inter-node cooperation. The proposed framework, which comprises network modeling and diffusion-based adaptation using the adapt then combine (ATC) algorithm, has been validated on different types of data. Experimental results indicate that the proposed framework outperforms common methods in classifying EEG data based on event-related potential (ERP) pattern identification, particularly in scenarios where machine learning-based models struggle with limited data. Furthermore, its ability to adapt to the non-stationary and dynamic nature of EEG signals and its efficient real-time implementation make this approach ideal for brain-computer interface (BCI), cognitive neuroscience, and clinical applications.},
}

@article {pmid40694026,
year = {2025},
author = {Guérin, V},
title = {Veteran and Brain-Computer Interfaces: The Duty to Care.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/21507740.2025.2530948},
pmid = {40694026},
issn = {2150-7759},
abstract = {Anticipated by science fiction, the enhanced soldier crystallized in the United States at the dawn of the 21st century within the Pentagon's scientific agency, the Defense Advanced Research Projects Agency (DARPA). Fueled by the fear of being overtaken by the enemy, and then by its own technology, this agency's new vision produced a "bifurcation" within anthropotechnics: the modification of humans for war. The soldier is now at the heart of a process of radical innovation, with as yet unknown implications. Emblematic of this enhancement, the use of the brain-computer interfaces (BCIs) will not only expose the soldier to previously unknown psychocognitive and emotional effects, but also offer the enemy potential access to his/her inner self. By giving birth to a new kind of veteran, this hybridization will generate new responsibilities for military commanders and politicians, as well as a new type of care.},
}

@article {pmid40694018,
year = {2025},
author = {Fan, C and Ding, Y and Zhang, H},
title = {A commentary on "Brain-computer interfaces: the innovative to unlocking neurological conditions".},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000003094},
pmid = {40694018},
issn = {1743-9159},
}

@article {pmid40691442,
year = {2025},
author = {Wang, X and Chen, S and Li, J and Gao, Y and Li, S and Li, M and Liu, X and Liu, S and Ming, D},
title = {Enhanced theta oscillations in the left temporoparietal region associated with refractory positive symptoms in schizophrenia.},
journal = {Schizophrenia (Heidelberg, Germany)},
volume = {11},
number = {1},
pages = {104},
pmid = {40691442},
issn = {2754-6993},
abstract = {Positive symptoms are a prominent feature of schizophrenia. Despite antipsychotic treatment, ~30% of patients develop refractory positive symptoms (RPSs). Current research fails to elucidate the potential neurophysiological mechanisms underlying RPSs, thereby hindering the development of additional treatments. This study, which included 37 patients with RPSs and 40 with non-refractory positive symptoms (NRPSs), aimed to explore their underlying neural mechanisms. Outcome measures were relative power spectrum density and interregional synchronization across frequency bands and theta-gamma phase-amplitude coupling (θ-γ PAC). The single-frequency analysis indicated that RPSs exhibited elevated theta power and reduced lateralization in the left temporal lobe and temporo-parietal junction, along with enhanced functional connectivity in the left frontocentral region. The cross-frequency analysis revealed that RPSs exhibited slightly higher θ-γ coupling at the left temporo-parietal junction compared to NRPSs. Correlation analysis revealed significant associations among theta power, the lateralization index, functional connectivity, and the severity of positive symptoms. The aberrant activation of the theta rhythm in the left temporo-parietal region may lead to increased functional asymmetry in the brain, impeding interregional and inter-frequency information transmission and thus significantly impairing the normal processing of auditory information. These findings offer potential insights into the neurophysiological basis of positive symptoms in schizophrenia and may inform future clinical interventions.},
}

@article {pmid40690341,
year = {2025},
author = {Wang, J and Yao, L and Wang, Y},
title = {Enhanced Online Continuous Brain-Control by Deep Learning-based EEG Decoding.},
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.2025.3591254},
pmid = {40690341},
issn = {1558-0210},
abstract = {OBJECTIVE: A growing amount of deep learning models for motor imagery (MI) decoding from electroencephalogram (EEG) have demonstrated their superiority over traditional machine learning approaches in offline dataset analysis. However, current online MI-based brain-computer interfaces (BCIs) still predominantly adopt machine learning decoders while falling short of high BCI performance. Yet, the generalization and advantages of deep learning-based EEG decoding in realistic BCI systems remain far unclear.

METHODS: We conduct a randomized and cross-session online MI-BCI study with 2D center-out tasks in 15 BCI-naive subjects. A newly proposed deep learning model named interactive frequency convolutional neural network (IFNet) is leveraged and rigorously compared with the prevailing benchmark namely filter-bank common spatial pattern (FBCSP) for online MI decoding.

RESULTS: Through extensive online analysis, the deep learning decoder consistently outperforms the classical counterpart across various performance metrics. In particular, IFNet significantly improves the average online task accuracy by 20% and 27% in two sessions compared with FBCSP, respectively. Moreover, a significant cross-session training effect is observed by the IFNet model (P = 0.017) while not for the controlled method (P = 0.337). Further offline evaluations also demonstrate the superior performance of IFNet over state-of-the-art deep learning models. Moreover, we present unique behavioral and neurophysiological insights underlying online brain-machine interaction.

CONCLUSION: We present one of the first studies about online MI-BCIs using deep learning, achieving substantially enhanced online performance for continuous BCI control.

SIGNIFICANCE: This study suggests the good utility of deep learning in MI-BCIs and has implications for clinical applications such as stroke rehabilitation.},
}

@article {pmid40688356,
year = {2025},
author = {Sonntag, J and Yu, L and Wang, X and Schack, T},
title = {Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1617748},
pmid = {40688356},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery-based brain-computer interfaces (BCIs) are a technique for decoding and classifying the intention of motor execution, solely based on imagined (rather than executed) movements. Although deep learning techniques have increased the potential of BCIs, the complexity of decoding unilateral upper limb motor imagery remains challenging. To understand whether neurophysiological features, which are directly related to neural mechanisms of motor imagery, might influence classification accuracy, most studies have largely leveraged traditional machine learning frameworks, leaving deep learning-based techniques underexplored.

METHODS: In this work, three different deep learning models from the literature (EEGNet, FBCNet, NFEEG) and two common spatial pattern-based machine learning classifiers (SVM, LDA) were used to classify imagined right elbow flexion and extension from participants using electroencephalography data. From two recorded resting states (eyes-open, eyes-closed), absolute and relative alpha and beta power of the frontal, fronto-central and central electrodes were used to predict the accuracy of the different classifiers.

RESULTS: The prediction of classifier accuracies by neurophysiological features revealed negative correlations between the relative alpha band and classifier accuracies and positive correlations between the absolute and relative beta band and classifiers accuracies. Most ipsilateral EEG channels yielded significant correlations with classifier accuracies, especially for the machine learning classifier.

DISCUSSION: This pattern contrasts with previous findings from bilateral MI paradigms, where contralateral alpha and beta activity were more influential. These inverted correlations suggest task-specific neurophysiological mechanisms in unilateral MI, emphasizing the role of ipsilateral inhibition and attentional processes.},
}

@article {pmid40685778,
year = {2025},
author = {Niu, X and Jiang, L and Hu, J and Jia, Y and Zhao, S and Ma, Y and Qiu, Z and Lian, Y and Zhu, E and Ni, J},
title = {Femtosecond Laser-Engineered Multifunctional Bio-Metasurface for the Inhibition of Thrombosis and Bacterial Infections.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c05001},
pmid = {40685778},
issn = {1944-8252},
abstract = {Surface engineering is an effective strategy for addressing thrombosis and bacterial infection associated with blood-contacting implants (BCIs). However, most functional surfaces rely on a single mechanism and surface engineering poses substantial processing challenges for chemically inert and difficult-to-process materials such as pyrolytic carbon. Herein, a multifunctional bio-metasurface (LDT surface) synergizing liquid-repellent (L), drag-reduction (D), and turbulence-attenuation (T) strategies is proposed. The LDT surface is achieved through the synergistic interplay of surface texture-mediated flow control and interfacial lubrication effects. The textured LDT surface with microgrooves exhibits a hemodynamic modulation capability, exhibiting an effective turbulence-attenuation effect. The slippery coating on the LDT surface exhibits liquid-repellent and drag-reduction effects, regulating bio (blood and bacteria)-material interfacial interactions. The complex, hierarchical micro-groove, micro-hole, and nano-ripples/gaps/protrusions structures on the surface are fabricated on pyrolytic carbon via temporally shaped femtosecond laser texturing, followed by functional coating. The LDT surface exhibits excellent stability under continuous turbulent flow, with no toxic byproducts generated during processing. The computational fluid dynamics simulation results confirm that the streamwise microgrooves on the wall significantly attenuate turbulence. Compared to the pristine sample surface, the experimental results reveal a 98.2% reduction in platelet adhesion on the LDT surface, with a platelet adhesion rate of only 0.22% and no detected activated platelets, while denatured fibrinogen adhesion decreases by 55.3%. Moreover, the antiadhesion capacities of the LDT surface against Staphylococcus aureus and Escherichia coli improve by 99.4% and 98.4%, respectively, relative to the pristine sample surface, without viable residual bacteria or biofilm formation. The study offers a promising strategy to mitigate BCI-associated thrombosis and bacterial infection on BCIs, particularly those made from difficult-to-machine materials.},
}

@article {pmid40683976,
year = {2025},
author = {Joshi, A and Matharu, PS and Malviya, L and Kumar, M and Jadhav, A},
title = {Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26267},
pmid = {40683976},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Machine Learning ; Wavelet Analysis ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; },
abstract = {Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inherent in EEG data. This paper explores the use of Convolutional Spiking Neural Networks (CSNNs) to enhance EEG signal classification. We apply Discrete Wavelet Transform (DWT) for feature extraction and evaluate CSNN performance on the Physionet EEG dataset, benchmarking it against traditional deep learning and machine learning methods. The findings indicate that CSNNs achieve high accuracy, reaching 98.75% in 10-fold cross-validation, and an impressive F1 score of 98.60%. Notably, this F1-score represents an improvement over previous benchmarks, highlighting the effectiveness of our approach. Along with offering advantages in temporal precision and energy efficiency, CSNNs emerge as a promising solution for next-generation EEG analysis systems.},
}

*Electroencephalography/methods
Humans
*Neural Networks, Computer
Brain-Computer Interfaces
Machine Learning
Wavelet Analysis
Signal Processing, Computer-Assisted
Algorithms
Deep Learning

@article {pmid40683565,
year = {2025},
author = {Wu, Y and Lv, K and Zhao, Y and Yang, G and Hao, X and Zheng, B and Lv, C and An, Z and Zhou, H and Yuan, Q and Song, T},
title = {Prediction model for detrusor underactivity via noninvasive clinical parameters in men with benign prostatic hyperplasia.},
journal = {Urology},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.urology.2025.07.021},
pmid = {40683565},
issn = {1527-9995},
abstract = {OBJECTIVE: To develop a clinical prediction model for detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH).

METHODS: A retrospective review was conducted on 546 individuals with BPH who had undergone urodynamic testing between January 2012 and May 2022. The bladder contractility index (BCI) was assessed using a pressure-flow study (PFS). Patients were categorized into DU (BCI <100, n=196) and non-DU (BCI ≥100, n=350) groups. Univariate logistic regression was initially performed to identify potential DU-related factors, followed by multivariate analysis to determine independent risk factors.

RESULTS: A predictive model for DU in patients with BPH was developed using the coefficient of these independent risk factors. Among the 546 cases, 196 (35.9%) were diagnosed with DU. Older age, smaller prostate volume, lower urgency symptom score, lower incomplete emptying symptom score, higher straining symptom score, and lower maximum flow rate (Qmax) were identified as independent predictors of DU in patients with BPH. The model demonstrated an area under the curve (AUC) of 0.78 (95% CI, 0.74-0.82), with internal validation yielding 0.75 (95% CI, 0.74-0.75).

CONCLUSIONS: We developed a predictive model that effectively estimates the DU probability in patients with BPH without requiring invasive PFS.},
}

@article {pmid40683191,
year = {2025},
author = {Li, Y and Sun, Y and Wan, F and Yuan, Z and Jung, TP and Wang, H},
title = {MetaNIRS: A general decoding framework for fNIRS based motor execution/imagery.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107873},
doi = {10.1016/j.neunet.2025.107873},
pmid = {40683191},
issn = {1879-2782},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a crucial brain activity monitoring tool with remarkable potential applications in brain-computer interfaces (BCI), particularly in rehabilitation therapy for disabilities. However, the performance of fNIRS-based BCI systems remains suboptimal, such as motor execution (ME) and motor imagery (MI) decoding. Inspired by the successful application of the PoolFormer framework in visual tasks, we first proposed a novel long-range dilation multilayer perceptron (LongDilMLP) to utilize the hemodynamic characteristics of fNIRS. Furthermore, the LongDilMLP was integrated with the PoolFormer framework, called as MetaNIRS in this study. The proposed framework MetaNIRS was employed for both ME and MI classification tasks, achieving rigorous validation of its effectiveness and practical applicability. To evaluate the performance of MetaNIRS, two publicly available ME datasets (A and C) and one self-collected MI dataset (B) were employed. The experimental results demonstrated that the average accuracy were 76.00 %, 57.45 %, and 84.14 %, with cross-subject accuracy of 77.24 %, 58.55 %, and 85.52 %, respectively. Moreover, sensitivity experiments of model parameters showed the robustness. Ablation experiments highlighted the significance of each MetaNIRS component and the efficacy of LongDilMLP over traditional MLP. Additionally, visualization techniques enhanced the interpretability of MetaNIRS, indicating the main contribution of the first half signals for classification. Using the first half of signals, the average accuracy only reduced 4.30 %, 1.69 %, and 1.11 %, respectively. These findings suggest that the superior performance of MetaNIRS, which provide an efficient general decoding framework for ME and MI.},
}

@article {pmid40683189,
year = {2025},
author = {Chen, H and Zeng, W and Chen, C and Cai, L and Wang, F and Shi, Y and Wang, L and Zhang, W and Li, Y and Yan, H and Siok, WT and Wang, N},
title = {EEG Emotion Copilot: Optimizing lightweight LLMs for emotional EEG interpretation with assisted medical record generation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107848},
doi = {10.1016/j.neunet.2025.107848},
pmid = {40683189},
issn = {1879-2782},
abstract = {In the fields of affective computing (AC) and brain-computer interface (BCI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches have made notable strides in EEG emotion recognition, particularly in feature extraction and pattern recognition, significant challenges persist in achieving end-to-end emotion computation, including rapid processing, individual adaptation, and seamless user interaction. This paper presents the EEG Emotion Copilot, a system optimizing a lightweight large language model (LLM) with 0.5B parameters operating in a local setting, which first recognizes emotional states directly from EEG signals, subsequently generates personalized diagnostic and treatment suggestions, and finally supports the automation of assisted electronic medical records. Specifically, we demonstrate the critical techniques in the novel data structure of prompt, model pruning and fine-tuning training, and deployment strategies aiming at improving performance and computational efficiency. Extensive experiments show that our optimized lightweight LLM-based copilot achieves an enhanced intuitive interface for participant interaction, superior accuracy of emotion recognition and assisted electronic medical records generation, in comparison to such models with similar scale parameters or large-scale parameters such as 1.5B, 1.8B, 3B and 7B. In summary, through these efforts, the proposed copilot is expected to advance the application of AC in the medical domain, offering innovative solution to mental health monitoring. The codes will be released at https://github.com/NZWANG/EEG_Emotion_Copilot.},
}

@article {pmid40681665,
year = {2025},
author = {Schrag, E and Comaduran Marquez, D and Kirton, A and Kinney-Lang, E},
title = {An investigation into the comfort and neural response of textured visual stimuli in pediatric SSVEP-based BCI.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26168},
pmid = {40681665},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Female ; Male ; Adolescent ; Child ; *Photic Stimulation/methods ; Electroencephalography/methods ; Child, Preschool ; Signal-To-Noise Ratio ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) are widely used due to their reliability and possible training-free setup. Common SSVEP stimuli are high contrast and solidly colored, potentially causing discomfort and visual fatigue, particularly when high stimulation frequencies are employed. To address this, textured stimuli, which may evoke visual responses in higher processing systems, have been proposed as an alternative to conventional flashing stimuli. We evaluate the effectiveness of textured stimuli for SSVEP-based BCIs by examining both user comfort and neural responses across different EEG channel subsets. Neurotypical participants aged 5-18 (n = 35, 57% female) were exposed to traditional and textured stimuli at three frequencies (9, 14, and 33 Hz) and asked to report perceived comfort. While textured stimuli were consistently rated as more comfortable, especially at lower frequencies, signal-to-noise ratio analysis indicated that they did not enhance neural responses compared to conventional stimuli. Classification accuracy was driven primarily by stimulation frequency rather than stimulus type and there was a sharp decline in accuracy at 33 Hz. These findings suggest that while textured stimuli improve user comfort, their utility in enhancing BCI performance remains unclear, warranting further investigation into stimulus design for SSVEP-based BCIs.},
}

Humans
*Brain-Computer Interfaces
*Evoked Potentials, Visual/physiology
Female
Male
Adolescent
Child
*Photic Stimulation/methods
Electroencephalography/methods
Child, Preschool
Signal-To-Noise Ratio

@article {pmid40681115,
year = {2025},
author = {Deepika, D and Rekha, G},
title = {Multi-class mental Task Classification based Brain-Computer Interface using Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network model.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110536},
doi = {10.1016/j.jneumeth.2025.110536},
pmid = {40681115},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) offer a promising avenue for individuals with severe motor disabilities to interact with the world. By decoding brain signals, BCIs can enable users to control devices and communicate thoughts. However, challenges such as noise in EEG signals and limited data availability hinder the development of accurate and reliable BCI systems. Nonetheless, problems persist, including limited data availability, noisy EEG signals, real-time performance limitations, and reduced classification accuracy.

NEW METHOD: To overcome this, the present work proposes an efficient Multi-Class Mental Task Classification based BCI using deep learning techniques. Initially, the obtained EEG data is pre-processed with a Finite Linear Haar wavelet-based Filtering (FLHF) technique to remove disturbances in EEG data. Afterwards, optimal feature extraction utilizes a Hybrid dynamic centre binary pattern and multi-threshold-based ternary pattern (H-DCBP-MTTP) technique to extract characteristics from pre-processed EEG data. Finally, the Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network (IRDCANFIN) model is used to classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using an Improved Remora optimization approach (IROA).

RESULTS: The proposed approach's performance is examined using the BCI laboratory dataset and the EEG Psychiatric Disorders Dataset, which yield accuracy results of 99.3% and 99.56%, respectively. Furthermore, evaluation results show that the proposed approach outperforms existing models.

Compared to existing models, such as DQN with a 1D-CNN-LSTM, GSP-ML, Shallow 1D-CNN, KNN, and SVM, and the proposed approach yields effective results in terms of accuracy, robustness, and computational efficiency.

CONCLUSION: The proposed IRDCANFIN classifier is used to classify multiple classes of mental tasks like baseline, counting, multiplication, letter composing, and rotation.},
}

@article {pmid40681114,
year = {2025},
author = {Zhang, R and Li, Z and Pan, X and Cui, H and Chen, X},
title = {Hybrid BCI for upper limb rehabilitation: integrating MI with peripheral field SSVEP stimulation.},
journal = {Journal of neuroscience methods},
volume = {423},
number = {},
pages = {110537},
doi = {10.1016/j.jneumeth.2025.110537},
pmid = {40681114},
issn = {1872-678X},
abstract = {BACKGROUND: Rehabilitation systems based on brain-computer interfaces (BCIs) hold significant potential for stroke patients. Existing systems, predominantly relying on motor imagery (MI), have room for improvement in both performance and user comfort. This study aims to enhance these aspects by developing a hybrid BCI system integrating MI with steady-state visual evoked potentials (SSVEPs) elicited by peripheral visual field stimulation.

NEW METHODS: The system is coupled with a soft robotic hand for feedback, forming a closed-loop framework. The design incorporates concentric rings with 7° and 10° eccentricities as peripheral stimuli, flashing at frequencies of 34 Hz and 35 Hz for left and right sides, respectively, to evoke SSVEPs. A central video (304 ×304 pixels) of left-hand/right-hand grasping motions guides subjects in performing synchronized MI tasks simply by focusing on it, which could also complete the SSVEP task.

RESULTS: The offline results of 11 subjects showed that the classification result of MI was 70.65 ± 3.38 %, and the SSVEP result was 96.04 ± 3.33 %, and the fusion result reached 96.23 ± 3.21 %, which confirmed the validity of the fusion method. The online experiment of 11 subjects achieved a result of 97.12 ± 2.09 %, validating the feasibility of the system.

The proposed system improves the comfort level while ensuring the performance of the system as compared to the existing systems.

CONCLUSION: The feasibility of the proposed system was verified by offline and online experiments to advance the clinical applications.},
}

@article {pmid40680338,
year = {2025},
author = {Sun, R and Ma, D and Pan, G},
title = {Post-training quantization for efficient ANN-SNN conversion.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107832},
doi = {10.1016/j.neunet.2025.107832},
pmid = {40680338},
issn = {1879-2782},
abstract = {Spiking Neural Networks (SNNs), inspired by the behavior of biological neurons, offer a promising direction for next-generation neural computing. Two primary methodologies have emerged for training deep SNNs: Direct Training, which optimizes SNNs using surrogate gradients, and ANN-to-SNN Conversion, which derives SNNs from Artificial Neural Networks (ANNs). In this work, we focus on the latter and investigate the conversion error that arises during the transformation. We provide a theoretical analysis showing that channel-wise thresholds are more effective than traditional layer-wise thresholds in mitigating this error. To achieve this efficiently, we leverage post-training quantization (PTQ), which enables calibration using only a small dataset without requiring retraining. Compared to conventional direct training and ANN-to-SNN conversion methods, our approach significantly reduces training time while improving accuracy on both static image and neuromorphic datasets.},
}

@article {pmid40679899,
year = {2025},
author = {Kim, YS and Han, H and Kim, CU and Choi, SI and Kim, MY and Im, CH},
title = {Performance enhancement of steady-state visual evoked field-based brain-computer interfaces incorporating MEG source imaging.},
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.2025.3590576},
pmid = {40679899},
issn = {1558-0210},
abstract = {Recent advancements in helmet-type magnetoencephalography (MEG) systems that operate without liquid helium, such as optically pumped magnetometer (OPM)-based MEG, have increased interest in MEG-based brain-computer interfaces (BCIs). Among various BCI paradigms, steady-state visual evoked field (SSVEF)-based BCIs have been actively studied owing to their high information transfer rate (ITR) and low demand for calibration sessions. Although MEG provides excellent spatial resolution and whole-head coverage, conventional algorithms such as the filter bank-driven multivariate synchronization index (FBMSI) do not fully exploit these advantages. To overcome this limitation, this study employed MEG source imaging to utilize information from whole-head MEG recordings fully and developed a novel weighting method called the averaged source location-based weighting (ASLW). ASLW leverages the averaged source locations of SSVEF signals to enhance BCI performance. Experimental results with 20 participants demonstrated that integrating ASLW with the FBMSI algorithm (ASLW-FBMSI) significantly improved both the classification accuracy and ITR across all tested window sizes. Notably, the largest performance gains included a 13.89% accuracy improvement at a 3-s window size and a 13.12 bits/min increase in ITR at a 2.5-s window size. Additionally, the ASLW-FBMSI algorithm exhibited a short processing delay of 0.107 s at a 4-s data length and was successfully validated in online BCI experiments with 20 participants. Although tested in SQUID-MEG in this study, our findings demonstrate the effectiveness of ASLW in significantly enhancing the overall performance of SSVEF-based BCIs.},
}

@article {pmid40678831,
year = {2025},
author = {Wang, J and Chen, H and Wang, X},
title = {Tirzepatide Induces Ferroptosis in Glioblastoma Cell Lines via the SOX2/SLC7A11 Axis: A Potential Therapeutic Strategy for Glioma Treatment.},
journal = {Journal of biochemical and molecular toxicology},
volume = {39},
number = {8},
pages = {e70392},
doi = {10.1002/jbt.70392},
pmid = {40678831},
issn = {1099-0461},
support = {//This study was supported by the Fifth Affiliated Hospital of Zhengzhou University./ ; },
mesh = {*Ferroptosis/drug effects ; Humans ; Cell Line, Tumor ; *Amino Acid Transport System y+/metabolism ; *SOXB1 Transcription Factors/metabolism ; *Glioblastoma/metabolism/drug therapy/pathology ; Cell Proliferation/drug effects ; Lipid Peroxidation/drug effects ; *Neoplasm Proteins/metabolism ; *Brain Neoplasms/metabolism/drug therapy/pathology ; Cell Movement/drug effects ; Tirzepatide ; },
abstract = {Tirzepatide, a dual agonist for glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptors used in type 2 diabetes and obesity management, was investigated for its effects on glioma cells, focusing on its potential to induce ferroptosis. Tirzepatide treatment significantly inhibited glioma cell proliferation and migration, as demonstrated by the CCK-8 and Transwell migration assays. Tirzepatide also induced lipid peroxidation, evidenced by increased ROS levels, elevated MDA production, and reduced SOD activity, while the GSH/GSSG ratio was decreased, reflecting oxidative stress. Ferroptosis was further confirmed by increased Fe[2+] concentrations and alterations in iron metabolism-related genes (Ferritin and TFR1) and lipid metabolism-related genes (ACSL4 and GPX4). Tirzepatide also inhibited the SOX2/SLC7A11 axis, which plays a critical role in resisting ferroptosis. Fer-1, a ferroptosis inhibitor, or SOX2 overexpression, markedly reduced Tirzepatide's effects on proliferation, migration, lipid peroxidation, and ferroptosis, highlighting the critical role of the SOX2/SLC7A11 axis in mediating these effects. These findings indicate that Tirzepatide inhibits glioma cell growth by inducing ferroptosis, presenting a potential therapeutic approach for glioma.},
}

*Ferroptosis/drug effects
Humans
Cell Line, Tumor
*Amino Acid Transport System y+/metabolism
*SOXB1 Transcription Factors/metabolism
*Glioblastoma/metabolism/drug therapy/pathology
Cell Proliferation/drug effects
Lipid Peroxidation/drug effects
*Neoplasm Proteins/metabolism
*Brain Neoplasms/metabolism/drug therapy/pathology
Cell Movement/drug effects
Tirzepatide

@article {pmid40678346,
year = {2025},
author = {Tsay, JJ and Velez, A and Collazo, D and Laniado, I and Bessich, J and Murthy, V and DeMaio, A and Rafeq, S and Kwok, B and Darawshy, F and Pillai, R and Wong, K and Li, Y and Schluger, R and Lukovnikova, A and Roldan, S and Blaisdell, M and Paz, F and Krolikowski, K and Gershner, K and Liu, Y and Gong, J and Borghi, S and Zhou, F and Tsirigos, A and Pass, H and Segal, LN and Sterman, DH},
title = {A Phase I Dose-Escalation Clinical Trial of Bronchoscopic Cryoimmunotherapy in Advanced-Stage NSCLC.},
journal = {JTO clinical and research reports},
volume = {6},
number = {8},
pages = {100849},
pmid = {40678346},
issn = {2666-3643},
abstract = {INTRODUCTION: Outcomes for NSCLC remain suboptimal. Recent data suggest that cryoablation can generate antitumor immune effects. In this first-in-human phase I clinical trial, we investigated the safety and feasibility of bronchoscopic cryoimmunotherapy (BCI) delivered during standard-of-care bronchoscopy and explored associated systemic immune responses.

METHODS: Subjects with known or suspected advanced-stage NSCLC were recruited. BCI was delivered in dose-escalated freeze-thaw cycles to determine maximum dose tolerance. Feasibility assessment was determined with a pre-set goal of achieving successful BCI in more than or equal to 80% of subjects. Safety was assessed by review of BCI-related complications, including grades 2 to 3 bleeding, pneumothorax requiring intervention, and National Cancer Institute Common Terminology Criteria for Adverse Events grade 3 to 5 adverse events. Pre- and post-BCI blood samples were collected to explore changes in the systemic immune profile.

RESULTS: Subjects with predominantly clinical TNM stage 3 or 4 adenocarcinoma or squamous cell carcinoma were enrolled. We reached the maximum dose of 30 seconds with 100% feasibility and no BCI-related adverse events. In peripheral blood analysis, we observed a significant decrease in derived neutrophil-to-lymphocyte ratio in the high-dose BCI group in comparison to the low-dose BCI cohort. We also observed increases in inflammatory cytokines-GM-CSF, IFN-γ, IL-1β, IL-17A, and IL-2-and effector memory T cells post-BCI.

CONCLUSION: BCI is safe and feasible. In addition, we provide preliminary evidence that at higher dose levels there is a systemic immune response consistent with a cytotoxic profile. Further immune analyses will determine the potential of BCI as an adjunctive therapy in combination with immune checkpoint inhibition in NSCLC treatment.},
}

@article {pmid40677333,
year = {2025},
author = {Zheng, ZW and Xu, MH and Fan, LN and Wang, RM and Xu, WQ and Yang, GM and Guo, LY and Liu, C and Dong, Y and Wu, ZY},
title = {Renal Impairment in Wilson's Disease.},
journal = {Kidney international reports},
volume = {10},
number = {7},
pages = {2453-2456},
pmid = {40677333},
issn = {2468-0249},
}

@article {pmid40674496,
year = {2025},
author = {Nair, A},
title = {Unraveling the emergent chorus of the mind: Machine learning reveals how a hidden neural code orchestrates diverse emotion states.},
journal = {Science (New York, N.Y.)},
volume = {389},
number = {6757},
pages = {245},
doi = {10.1126/science.adx7811},
pmid = {40674496},
issn = {1095-9203},
mesh = {*Emotions/physiology ; *Machine Learning ; Humans ; *Brain/physiology ; *Neurons/physiology ; },
abstract = {Machine learning reveals how a hidden neural code orchestrates diverse emotion states.},
}

*Emotions/physiology
*Machine Learning
Humans
*Brain/physiology
*Neurons/physiology

@article {pmid40672704,
year = {2025},
author = {Rekrut, M and Ihl, J and Jungbluth, T and Krüger, A},
title = {How low can you go: evaluating electrode reduction methods for EEG-based speech imagery BCIs.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1578586},
pmid = {40672704},
issn = {2673-6195},
abstract = {Speech imagery brain-computer interfaces (SI-BCIs) aim to decode imagined speech from brain activity and have been successfully established using non-invasive brain measures such as electroencephalography (EEG). However, current EEG-based SI-BCIs predominantly rely on high-resolution systems with 64 or more electrodes, making them cumbersome to set up and impractical for real-world use. In this study, we evaluated several electrode reduction algorithms in combination with various feature extraction and classification methods across three distinct EEG-based speech imagery datasets to identify the optimal number and position of electrodes for SI-BCIs. Our results showed that, across all datasets, the original 64 channels could be reduced by 50% without a significant performance loss in classification accuracy. Furthermore, the relevant areas were not limited to the left hemisphere, widely known to be responsible for speech production and comprehension, but were distributed across the cortex. However, we could not identify a consistent set of optimal electrode positions across datasets, indicating that electrode configurations are highly subject-specific and should be individually tailored. Nonetheless, our findings support the move away from extensive and costly high-resolution systems toward more compact, user-specific setups, facilitating the transition of SI-BCIs from laboratory settings to real-world applications.},
}

@article {pmid40672675,
year = {2025},
author = {Zhang, C and Wang, Y and Wang, X},
title = {Reimagining Neuropsychiatric and Neurological Disorders through the Lens of Brain Network Dynamics: Psychedelics as Catalysts for System-Level Plasticity.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {7},
pages = {2308-2311},
pmid = {40672675},
issn = {2575-9108},
abstract = {Neuropsychiatric disorders reflect disruptions in brain network dynamics along an "order-complexity-chaos" continuum. Psychedelics may therapeutically increase neural entropy, disrupt maladaptive patterns, and promote network reorganization. This system-level framework emphasizes dynamic connectome remodeling over static molecular correction, offering a novel strategy for treating psychiatric and neurological conditions through controlled neural destabilization and reconnection.},
}

@article {pmid40672502,
year = {2025},
author = {Chaichanasittikarn, O and Diaz, L and Thomas, N and Candrea, D and Luo, S and Nathan, K and Tenore, FV and Fifer, MS and Crone, NE and Christie, B and Osborn, LE},
title = {High-gamma electrocorticography activity represents perceived vibration intensity in human somatosensory cortex.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.09.25331186},
pmid = {40672502},
abstract = {Haptic feedback can play a useful role in rehabilitation and brain-computer interface applications by providing users with information about their system or performance. One challenge delivering tactile stimulation is not knowing how the haptic sensation is actually perceived, irrespective of the stimulation amplitude, during real-world use and beyond controlled psychophysical experiments. In a participant with chronically implanted electrocorticography arrays, we observed that perceived intensity of haptic vibration on the fingertips was represented in the high-gamma (HG) frequency band (70-170 Hz) in the somatosensory cortex. The five fingers of the participant's right hand were represented by distinct channels in the implanted array and modulated by the vibration amplitude at the fingertips. Although it reliably varied with the vibration amplitude, we found that HG activity had a stronger relationship with the actual perceived intensity of haptic stimulation (r s = 0.45 , p < 10 [-6]). These results demonstrate that neural signals, specifically HG activity, in the somatosensory cortex can represent qualities of perceived haptic intensity regardless of the stimulation amplitude, which could enable a new way to passively quantify or ensure effective haptic feedback to a user.},
}

@article {pmid40672280,
year = {2025},
author = {Lei, T and Scheid, MR and Glaser, JI and Slutzky, MW},
title = {Active Dissociation of Intracortical Spiking and High Gamma Activity.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.10.663559},
pmid = {40672280},
issn = {2692-8205},
abstract = {Cortical high gamma activity (HGA) is used in many scientific investigations, yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly- predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. We trained subjects to decouple spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them indicated that HGA is not primarily generated by summed local spiking. Instead, HGA correlated with neuronal population co-firing of neurons that were widely distributed across millimeters. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises predominantly from summed postsynaptic potentials triggered by synchronous co-firing of widely distributed neurons.},
}

@article {pmid40668700,
year = {2025},
author = {Robinson, JT},
title = {Making Heads and Tails of the Coming Era of Neural Devices, Could Moore's Law Address the Declining Mental Health Trend.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {50-55},
doi = {10.1109/MPULS.2025.3572593},
pmid = {40668700},
issn = {2154-2317},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Mental Health ; *Transcranial Magnetic Stimulation ; },
abstract = {Despite major advances in medicine and technology, mental health outcomes have declined globally over the past several decades. Fortunately we are in the early phases of exponential growth neurotech similar to Moore's Law. These emerging neural devices may provide a solution to the growing mental health crisis. Clinical data shows promising outcomes from technologies such as transcranial magnetic stimulation (TMS) leading to exponential improvement in performance improvements and cost reductions. As a result, neurotechnology could follow a similar path to personal computing going from a handful of niche markets to ubiquity over the next decade. Indeed, next generation therapeutic brain-computer interfaces (BCIs)-particularly minimally invasive implants-could become mass-market solutions for regulating mental states. The future may be one where neural devices help individuals thrive in an increasingly complex world, not by augmenting human intelligence but by enhancing emotional well-being and preserving the most precious aspects of our humanity.},
}

Humans
*Brain-Computer Interfaces/trends
*Mental Health
*Transcranial Magnetic Stimulation

@article {pmid40668693,
year = {2025},
author = {Banks, J},
title = {Silicon Synapses: The Bold Frontier of Brain-Computer Integration.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {5-9},
doi = {10.1109/MPULS.2025.3572569},
pmid = {40668693},
issn = {2154-2317},
mesh = {*Brain-Computer Interfaces ; Humans ; *Silicon ; *Synapses/physiology ; Spinal Cord Injuries ; *Brain/physiology ; },
abstract = {The allure of Neuralink is attracting investors to funnel money into the development of brain-computer interface (BCI) technology, primarily aimed at treating spinal cord injury (SCI) patients. But what is the payoff? Jim Banks examines the inspired innovation in BCI that is reestablishing connections for patients with the world.},
}

*Brain-Computer Interfaces
Humans
*Silicon
*Synapses/physiology
Spinal Cord Injuries
*Brain/physiology

@article {pmid40668691,
year = {2025},
author = {Goktas, P and Tun, NN},
title = {EEG-Based Brain-Computer Interfaces: Pioneering Frontier Research in the 21st Century.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {36-39},
doi = {10.1109/MPULS.2025.3572556},
pmid = {40668691},
issn = {2154-2317},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Electroencephalography/methods/trends ; Artificial Intelligence ; *Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Electroencephalography (EEG)-based brain-computer interface (BCI) systems are inevitably needed to set up non-invasive therapies in neurorehabilitation. Along with the artificial intelligence (AI) techniques trending, constructing EEG-based brain computer interfaces is still in demand with high classification accuracy for advancing the state-of-the-art BCIs. From the perspective of pioneering frontier research, this article highlights the 21st-century's EEG-based BCI systems, their challenges, and its future direction for neuroscientists and clinical applications.},
}

*Brain-Computer Interfaces/trends
Humans
*Electroencephalography/methods/trends
Artificial Intelligence
*Signal Processing, Computer-Assisted
Brain/physiology

@article {pmid40668688,
year = {2025},
author = {Zaman, MH},
title = {The Potential of Brain-Computer Interface Technologies in Low- and Middle-Income Countries Global Health Perspective.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {40-42},
doi = {10.1109/MPULS.2025.3572574},
pmid = {40668688},
issn = {2154-2317},
mesh = {*Brain-Computer Interfaces/economics ; Humans ; *Developing Countries ; *Global Health ; },
abstract = {Historically, brain-computer interface (BCI) technologies have almost exclusively been available in high-income countries. What would it take for them to become more available and accessible in low- and middle-income countries, and in complex settings?},
}

*Brain-Computer Interfaces/economics
Humans
*Developing Countries
*Global Health

@article {pmid40668686,
year = {2025},
author = {Grifantini, K},
title = {From Headsets to Healing: The Rise of Wearable Brain Tech and Its Impact on Mental Illness and Cognitive Health.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {25-29},
doi = {10.1109/MPULS.2025.3572580},
pmid = {40668686},
issn = {2154-2317},
mesh = {Humans ; *Brain-Computer Interfaces ; *Cognition/physiology ; *Mental Disorders/therapy ; Mental Health ; *Wearable Electronic Devices ; },
abstract = {The rapidly evolving field of noninvasive brain-machine interfaces (BMIs) is transforming wearable technology from science fiction into a powerful tool for health care, offering a surgery-free and drug-free alternative to traditional treatments. Such devices are currently being used to target conditions such as depression, anxiety, PTSD, insomnia and more through targeted neurostimulation techniques.},
}

Humans
*Brain-Computer Interfaces
*Cognition/physiology
*Mental Disorders/therapy
Mental Health
*Wearable Electronic Devices

@article {pmid40589299,
year = {2025},
author = {George, I and Rao, DP and Jain, A and Ascione, G and Sharma, M and Meharwal, ZS and Sarkar, B and Kochar, N and Shastri, N and Runt, J and Whisenant, B and Wilson, B and Kiser, A and Leon, MB and Pandey, K},
title = {1-Year Results From a Multicenter Trial of a Polymer Surgical Mitral Valve: Insights Into New Technology.},
journal = {Journal of the American College of Cardiology},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jacc.2025.06.017},
pmid = {40589299},
issn = {1558-3597},
abstract = {BACKGROUND: Polymer leaflet material may extend the durability of surgical mitral valve replacement (SMVR) to provide stable long-term hemodynamics. The India Mitral Surgical Trial sought to evaluate the safety and performance of a novel polymer leaflet material as part of a surgical mitral valve (MV) prosthesis.

OBJECTIVES: In this study, the authors sought to report 1-year outcomes in patients undergoing SMVR for MV disease using the Tria Mitral Valve (Foldax).

METHODS: Adult patients requiring MV replacement were enrolled in a prospective single-arm multicenter trial at 8 clinical sites in India from April to November 2023. An independent physician screening committee reviewed each patient for study eligibility before enrollment. Safety events were adjudicated per standard Valve Academic Research Consortium 3 criteria guidelines, and valve performance was assessed by means of echocardiographic and computed tomographic imaging at 30 days and 1 year. Patients were maintained on a vitamin K antagonist (target international normalized ratio: 2.5).

RESULTS: Sixty-seven patients, of whom 64% were female (48% of childbearing age), with a mean age of 42 years (range: 19-67 years), mean body mass index of 22.7 kg/m[2], and body surface area of 1.6 cm[2] were treated with SMVR with 100% technical success. Most patients (54%) were NYHA functional class III or IV at baseline. The mean Society of Thoracic Surgeons score was 1.4%. The etiology of MV disease was stenosis in 27%, regurgitation in 30%, and mixed in 43% of patients, primarily secondary to rheumatic heart disease. The 1-year rates for all-cause mortality, thromboembolic events, stroke, structural valve deterioration, and valve reintervention were 9.1%, 7.5%, 4.9%, 0%, and 0%, respectively. No death was valve related. One-year effective orifice area and mean inflow gradient were 1.4 ± 0.4 cm[2] and 4.6 ± 1.7 mm Hg, respectively. There were 2 thrombotic events and 3 ischemic strokes, all in patients with subtherapeutic international normalized ratio.

CONCLUSIONS: The polymer surgical MV demonstrated an acceptable safety profile and maintained stable hemodynamic performance through 1 year in patients undergoing MV replacement. Further study of this promising polymer leaflet technology is ongoing. (Clinical Investigation for the Foldax Tria Mitral Valve-India; NCT06191718).},
}

@article {pmid40668685,
year = {2025},
author = {Bates, M},
title = {Why Consumer Neurofeedback Devices Are More Than Hype for Brain Health.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {21-24},
doi = {10.1109/MPULS.2025.3572577},
pmid = {40668685},
issn = {2154-2317},
mesh = {Humans ; *Neurofeedback/instrumentation ; *Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; },
abstract = {Neurofeedback uses a brain-computer interface to measure a person's brain activity and show it to them in real time. A number of companies offer neurofeedback devices directly to consumers, with promises of improving meditation and enhancing concentration. However, whether neurofeedback is actually effective remains controversial among researchers.},
}

Humans
*Neurofeedback/instrumentation
*Brain/physiology
*Brain-Computer Interfaces
Electroencephalography

@article {pmid40668684,
year = {2025},
author = {Anderson, C},
title = {Industry Corner Live With Synchron CEO Tom Oxley.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {43-49},
doi = {10.1109/MPULS.2025.3572578},
pmid = {40668684},
issn = {2154-2317},
mesh = {Humans ; *Brain-Computer Interfaces ; *Biomedical Engineering ; Artificial Intelligence ; },
abstract = {Pulse's Industry Corner Live featured a dynamic live Q&A session between IEEE Pulse Editor-in-Chief Chad Andresen and Dr. Tom Oxley, CEO and co-founder of Synchron, a leader in minimally invasive brain-computer interface (BCI) technology. The discussion explored the intersection of neurotechnology, artificial intelligence, and the evolving landscape of entrepreneurship in the MedTech sector. Dr. Oxley shared insights into Synchron's pioneering work with endovascular BCIs, offering a less invasive alternative to traditional neurosurgical approaches, and how this technology is reshaping the possibilities for restoring communication in patients with paralysis. The conversation delved into the growing role of AI in decoding neural signals and driving clinical translation, while also addressing the regulatory, financial, and ethical challenges faced by entrepreneurs in the neurotechnology space. With candid reflections on his journey from clinician to startup founder, Oxley provided an inside look at what it takes to bring disruptive technologies from concept to clinic. This session offered a rare glimpse into the mindset of a neurotech innovator navigating the high-stakes interface of science, medicine, and industry.},
}

Humans
*Brain-Computer Interfaces
*Biomedical Engineering
Artificial Intelligence

@article {pmid40668677,
year = {2025},
author = {Sorrell, E and Wilson, DE and Rule, ME and Yang, H and Forni, F and Harvey, CD and O'Leary, T},
title = {An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation.},
journal = {Cell reports},
volume = {44},
number = {7},
pages = {115862},
doi = {10.1016/j.celrep.2025.115862},
pmid = {40668677},
issn = {2211-1247},
abstract = {Cortical circuits contain diverse sensory, motor, and cognitive signals, and they form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We develop a calcium-imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we find that mice can immediately navigate toward goal locations when control is switched to the BMI. No learning or adaptation is observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decouple from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.},
}

@article {pmid40667422,
year = {2025},
author = {Schroeder, F and Fairclough, S and Dehais, F and Richins, M},
title = {The impact of cross-validation choices on pBCI classification metrics: lessons for transparent reporting.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1582724},
pmid = {40667422},
issn = {2673-6195},
abstract = {Neuroadaptive technologies are a type of passive Brain-computer interface (pBCI) that aim to incorporate implicit user-state information into human-machine interactions by monitoring neurophysiological signals. Evaluating machine learning and signal processing approaches represents a core aspect of research into neuroadaptive technologies. These evaluations are often conducted under controlled laboratory settings and offline, where exhaustive analyses are possible. However, the manner in which classifiers are evaluated offline has been shown to impact reported accuracy levels, possibly biasing conclusions. In the current study, we investigated one of these sources of bias, the choice of cross-validation scheme, which is often not reported in sufficient detail. Across three independent electroencephalography (EEG) n-back datasets and 74 participants, we show how metrics and conclusions based on the same data can diverge with different cross-validation choices. A comparison of cross-validation schemes in which train and test subset boundaries either respect the block-structure of the data collection or not, illustrated how the relative performance of classifiers varies significantly with the evaluation method used. By computing bootstrapped 95% confidence intervals of differences across datasets, we showed that classification accuracies of Riemannian minimum distance (RMDM) classifiers may differ by up to 12.7% while those of a Filter Bank Common Spatial Pattern (FBCSP) based linear discriminant analysis (LDA) may differ by up to 30.4%. These differences across cross-validation implementations may impact the conclusions presented in research papers, which can complicate efforts to foster reproducibility. Our results exemplify why detailed reporting on data splitting procedures should become common practice.},
}

@article {pmid40666829,
year = {2025},
author = {Bo, W and Che, R and Jia, F and Sun, K and Liu, Q and Guo, L and Zhang, X and Gong, Y},
title = {Study on the Effect of the Envelope of Terahertz Unipolar Stimulation on Cell Membrane Communication-Related Variables.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0755},
pmid = {40666829},
issn = {2639-5274},
abstract = {The development of terahertz science and technology has shown new application prospects in artificial intelligence. Terahertz stimulation can lead to information communication of cells. Terahertz unipolar picosecond pulse train stimulation can activate cell membrane hydrophilic pores and protein ion channels. However, the effect of the envelope of the terahertz unipolar stimulation remains unknown. This paper studies the effect of the envelope on membrane communication-related variables and the accompanying energy consumption by a cell model with considerations of hydrophilic pores and Na[+], K[+]-ATPase. According to the results, terahertz unipolar picosecond pulse train stimulation can deliver the signal contained in its envelope into the variation rates of membrane potentials no matter whether the hydrophilic pores are activated or not and also into the variation rates of the ion flow via the pores after activation of the pores. In contrast, the ion flow via Na[+], K[+]-ATPase seems irrelevant to the signal in the envelope. Moreover, the ion flows show a modulation effect on the variation rates of membrane potentials. The accompanying power dissipations in the cases of different envelopes are similar, as low as around the level of 10[-11] W. The results lay the foundations for application in artificial intelligence, like brain-machine communications.},
}

@article {pmid40664224,
year = {2025},
author = {Wang, X and Meng, J and Zheng, Y and Wei, Y and Wang, F and Ding, H and Zhuo, Y},
title = {Characterizing the neural representations and decoding performance of foot rhythmic motor execution or imagery guided by action observation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf011},
pmid = {40664224},
issn = {1741-2552},
abstract = {OBJECTIVE: The limited spatial resolution inherent in electroencephalography (EEG), a widely-adopted non-invasive neuroimaging technique, combined with the intrinsic complexity of performing unilateral lower-limb motor imagery (MI), restricts decoding accuracy. To address these challenges, we propose a paradigm based on action observation-guided rhythmic motor execution (AO-ME) and motor imagery (AO-MI), designed to simplify task demands and enhance decoding performance. Magnetoencephalography (MEG) serves as the data acquisition method, leveraging its superior spatiotemporal resolution.

APPROACH: Spatiotemporal and spectral features were characterized at the sensor level, and source imaging techniques were employed to examine cortical activation patterns. Ensemble task-related component analysis (eTRCA) facilitated decoding of unilateral tasks. Main results. Robust lateralized neural responses were observed, exhibiting low-frequency phase-locked components that distinctly reflected the task frequency and its second harmonic within sensorimotor, parietal, and occipital cortices. Moreover, significant contralateral suppression of the sensorimotor rhythm was observed. Decoding accuracies reached 95.22% ± 4.75% for AO-ME and 88.66% ± 8.52% for AO-MI across twenty participants based on the phase-locked features.

SIGNIFICANCE: Collectively, our findings demonstrate that the AO-ME/MI paradigm elicits stable, distinguishable neural activity, highlighting its potential as an effective strategy for decoding unilateral lower-limb movements within brain-computer interface (BCI) applications. .},
}

@article {pmid40664221,
year = {2025},
author = {Jochumsen, M and Petersen, BS and Mikkelsen Vestergaard, L and Falborg, NF and Wisler, L and Olesen, MV and Andersen, MS and Sørensen, NB and Jørgensen, ST},
title = {Detection of movement-related cortical potentials associated with upper and low limb movements in patients with multiple sclerosis for brain-computer interfacing.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf010},
pmid = {40664221},
issn = {1741-2552},
abstract = {Brain-computer interface (BCI) training has been shown to be effective for inducing neural plasticity and for improving motor function in stroke patients. BCI training could potentially have a positive effect on people with multiple sclerosis (MS) as well by pairing movement-related brain activity with congruent afferent feedback from e.g. functional electrical stimulation. In the current study, the aim was to detect movement-related cortical potentials (MRCPs) from single-trial EEG in people with MS across two separate days using different classifier calibration schemes to estimate the performance of a BCI that can be used for neurorehabilitation. Approach: Fifteen individuals with MS performed 100 wrist movements and 100 ankle movements while continuous EEG was recorded. Also, idle brain activity was recorded. This was repeated on a separate day. The data were filtered and divided into epochs containing data prior to the movement onset. Temporal, spectral and template-matching features were extracted and classified with a random forest classifier using different calibration schemes to estimate the performance when training the classifier on data from the same day and same participant, different day but same participant, and across different participants. Main results: Clear MRCPs were elicited across both recording days, and it was possible to discriminate between idle activity and movement-related brain activity with accuracies between ~80-90% when training and testing the classifier on data from the same day and participant. The performance decreased when using data from a separate day but same participant (~70-80%) or data from separate participants (~70%) for training the classifier. Significance: The results showed that it is feasible for people with MS to use a BCI for inducing neural plasticity. .},
}

@article {pmid40663645,
year = {2025},
author = {Wang, A and Lin, C and Mao, W and Jin, J},
title = {More generosity, less inequity aversion? Neural correlates of fairness perception under social distance and of its relation to generosity.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf152},
pmid = {40663645},
issn = {1460-2199},
support = {//Shanghai Philosophy and Social Sciences Planning Project/ ; //National Nature Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; Young Adult ; Electroencephalography ; Adult ; *Psychological Distance ; *Social Behavior ; *Social Perception ; *Brain/physiology ; Interpersonal Relations ; Games, Experimental ; *Altruism ; Adolescent ; },
abstract = {Humans instinctively react negatively to inequity, while generosity counters this tendency. Previous studies show that both fairness perception and generosity involve balancing behaviors and motivations in social interactions. However, their relationship remains underexplored, limiting our understanding of the complex psychological processes underlying social behavior. Using a social discounting task, we assessed individual generosity, while an Ultimatum Game task with concurrent electroencephalogram recording allowed us to quantify inequity aversion and fairness perception by manipulating social distance and inequity levels. We found that both generosity and fairness perception decrease with increasing social distance, whereas inequity aversion increases. Modeling the decay of generosity across social distances, we found that decayed generosity was positively associated with inequity aversion in the friend condition and negatively correlated with the attenuation of fairness perception. These results suggest that the decay of generosity with social distance is linked to reduced sensitivity to inequity toward friends and heightened neural differences in fairness perception across social relationships. Our study provides electrophysiological evidence of individual variability in generosity and inequity aversion influenced by social distance, expanding inequity aversion theory.},
}

Humans
Male
Female
Young Adult
Electroencephalography
Adult
*Psychological Distance
*Social Behavior
*Social Perception
*Brain/physiology
Interpersonal Relations
Games, Experimental
*Altruism
Adolescent

@article {pmid40662561,
year = {2025},
author = {Vikal, A and Maurya, R and Patel, BB and Patel, P and Kumar, M and Kurmi, BD},
title = {A Mini-Review on Unlocking Cognitive Enhancement: An Innovative Strategy for Optimal Brain Functions.},
journal = {Central nervous system agents in medicinal chemistry},
volume = {},
number = {},
pages = {},
doi = {10.2174/0118715249357704250702140026},
pmid = {40662561},
issn = {1875-6166},
abstract = {Cognitive enhancement, aimed at improving or preserving memory, attention, and executive functions, has gained significant interest from both the scientific community and the public. This review explores various strategies for enhancing cognitive function, including natural compounds, synthetic enhancers, and behavioural approaches. Natural compounds like curcumin, Ginkgo biloba, Panax ginseng, and Rhodiola rosea are examined for their cognitive benefits, with ongoing research on their mechanisms and potential nanoformulation-based drug delivery. Synthetic enhancers such as Modafinil, Piracetam, Methylphenidate, and Noopept show promise in improving cognitive functions. Additionally, substances influencing brain metabolism, like Creatine and Coenzyme Q10, are discussed. Behavioural interventions, including sleep optimization, meditation, and physical exercise, are evaluated for their cognitive-enhancing effects. Noninvasive brain stimulation techniques, such as TMS and tDCS, along with innovative methods like whole-body vibration and brain-machine interfaces, are also explored. The review emphasizes the complex interplay of these strategies and the need for continued research to fully exploit their potential. By highlighting natural compounds, synthetic drugs, and behavioural approaches, the review advocates for a multifaceted approach to cognitive enhancement and calls for more detailed and longitudinal studies to understand their long-term benefits and mechanisms.},
}

@article {pmid40660069,
year = {2025},
author = {Jeppsen, C and McMurray, B},
title = {Reduced Cochlear Implant Performance in Listeners with Single-Sided Deafness: Comparison with Bilateral Listeners.},
journal = {Journal of the Association for Research in Otolaryngology : JARO},
volume = {},
number = {},
pages = {},
pmid = {40660069},
issn = {1438-7573},
support = {P50 DC00242//Foundation for the National Institutes of Health/ ; R01 DC008089/DC/NIDCD NIH HHS/United States ; },
abstract = {PURPOSE: The efficacy of the Cochlear Implant (CI) in listeners with single-sided deafness (SSD) was evaluated by comparing single-ear speech perception in SSD listeners and bilateral cochlear implant listeners (BCI).

METHODS: Consonant-nucleus-consonant (CNC) speech perception scores for the CI-only ear in SSD listeners (N = 55; 36 female, 19 male) were compared to single-ear performance in age and device experience-matched BCI listeners (N = 55; 29 female, 26 male). Separate analyses examined: (1) a matched ear from the BCI listeners (for sequentially implanted BCI listeners, the first-implanted ear in sequential BCI listeners, or, for simultaneously implanted BCI listeners, the ear on the same side as the CI in the matching SSD listener), and (2) the lower-performing ear across BCI listeners. Additional models included moderators such as age, time since activation, CI usage, and etiology. A final analysis compared first and second implants for sequential BCI listeners.

RESULTS: SSD listeners showed significantly lower CNC performance after controlling for age, time since activation, CI usage, and etiology. Sequential BCI listeners exhibited significantly lower CNC performance on their second ear, compared to their first ear.

CONCLUSION: Speech perception with CIs is reduced in SSD listeners compared to BCI users, likely due to blocking, where the normal-hearing ear diminishes reliance on the CI. Lower performance in the second implanted ear of sequential BCI listeners also suggests greater reliance on the more experienced ear. These findings highlight the need for additional training, resources, and support to optimize CI performance in SSD listeners, despite prior evidence of positive CNC outcomes.},
}

@article {pmid40659530,
year = {2025},
author = {Hu, Y and Ma, B and Jin, J},
title = {Neural Synchrony and Consumer Behavior: Predicting Friends' Behavior in Real-World Social Networks.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0073-25.2025},
pmid = {40659530},
issn = {1529-2401},
abstract = {The endogenous aspect of social influence, reflected in the spontaneous alignment of behaviors within close social relationships, plays a crucial role in understanding human social behavior. In two studies involving 222 human subjects (Study 1: n = 175, 106 females; Study 2: n = 47, 33 females), we used a longitudinal behavioral study and a naturalistic stimuli neuroimaging study to investigate the endogenous consumer behavior similarities and their neural basis in real-world social networks. The findings reveal that friends, compared to nonfriends, exhibit higher similarity in product evaluation, which undergoes dynamic changes as the structure of social networks changes. Both neuroimaging and meta-analytic decoding results indicate that friends exhibit heightened neural synchrony, which is linked to cognitive functions such as object perception, attention, memory, social judgment, and reward processing. Stacking machine learning-based predictive models demonstrate that the functional connectivity maps of brain activity can predict the purchase intention of their friends or their own rather than strangers. Based on the significant neural similarity which exists among individuals in close relationships within authentic social networks, the current study reveals the predictive capacity of neural activity in predicting the behavior of friends.Significance Statement Understanding the endogenous aspects of social impact is critical for comprehending human social behavior. The current study provides novel evidence that close social relationships within real-world networks exhibit heightened behavioral and neural synchrony, and dynamically evolve with changes in social network structures. Using naturalistic stimuli and longitudinal studies, it is demonstrated that neural activity not only reflects shared cognitive functions, but also predicts purchase intentions of individuals and their close friends with greater accuracy than strangers. These findings uncover the neural basis of endogenous consumer behavior similarities and highlight the predictive capacity of brain activity in understanding and forecasting the behavior of individuals within close social connections. This research offers valuable insights into the intersection of neuroscience, social behavior, and consumer decision-making.},
}

@article {pmid40658672,
year = {2025},
author = {Eser, A and Erdoğan, SB},
title = {Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms.},
journal = {PloS one},
volume = {20},
number = {7},
pages = {e0325850},
pmid = {40658672},
issn = {1932-6203},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; *Emotions/physiology ; Male ; Female ; Adult ; Young Adult ; *Supervised Machine Learning ; Algorithms ; Prefrontal Cortex/physiology ; Support Vector Machine ; },
abstract = {Automated detection of emotional states through brain-computer interfaces (BCIs) offers significant potential for enhancing user experiences and personalizing services across domains such as mental health, adaptive learning and interactive entertainment. Within this advancing field, the aim of this study was to test the feasibility of a functional near-infrared spectroscopy (fNIRS)-based BCI system for accurate prediction and objective identification of three fundamental emotional states that involved positive, negative and neutral conditions. Consequently, the efficacy of fNIRS signals in predicting the valence of standardized stimuli from the International Affective Picture System (IAPS) was assessed. fNIRS data were collected from twenty healthy participants while images from the IAPS database were presented. The images varied in both valence (i.e., positive, neutral, negative) and arousal (i.e., high, low) level. Hemodynamic responses of prefrontal cortical (PFC) regions were recorded with a twenty-two channel system. Twenty fNIRS derived time domain features were extracted from HbO time traces of each channel corresponding to each stimulus period. Classification performances of three machine learning algorithms, namely the k-Nearest Neighbors (kNN), Ensemble (Subspace kNN) and Support Vector Machines (SVM), in two class and three class classification of positive, neutral and negative states were evaluated with ten runs of a tenfold cross-validation procedure through splitting the data into test, train and validation groups at each run. Three class classification performances of all algorithms were above 90% in terms of accuracy, sensitivity, specificity, F-1 score and precision metrics while two class accuracy performances of all algorithms were above 93% in terms of each performance metric. The high-performance classification results highlight the potential of fNIRS-based BCI systems for real-time, objective detection of basic emotional states for daily life and clinical applications. fNIRSbased BCIs may show promise for future developments in personalized user experiences and clinical applications due to their practicality and low computational complexity.},
}

Humans
Spectroscopy, Near-Infrared/methods
*Brain-Computer Interfaces
*Emotions/physiology
Male
Female
Adult
Young Adult
*Supervised Machine Learning
Algorithms
Prefrontal Cortex/physiology
Support Vector Machine

@article {pmid40658035,
year = {2025},
author = {Niu, Y and Li, Z and Zeng, G and Zhang, Y and Yao, L and Wu, X},
title = {Electroencephalogram-Based Satisfaction Assessment Brain-Computer Interface in Emerging Video Service by Using Graph Representation Learning.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {},
doi = {10.1177/21580014251359107},
pmid = {40658035},
issn = {2158-0022},
abstract = {Background: Emerging video services (EVS) offer users various multimedia presentations, and satisfaction assessment is crucial for enhancing their user experience and competitiveness. However, existing research methods are unable to provide a quantitative satisfaction assessment. Electroencephalogram (EEG), as a popular signal source in brain-computer interface (BCI), with the advantage of being difficult to disguise and containing rich brain activity information, has gained increasing attention from researchers. This article aims to investigate the advantages of employing EEG for modeling satisfaction in EVS. Unlike the subjective metrics assessment in traditional video services, generating satisfaction in EVS involves a range of cognitive functions, including cognitive load, emotion, and audiovisual perception, which are difficult to characterize using a single feature. The representation of brain states for complex cognitive functions has been a major challenge for EEG modeling approaches. Methods: To address this challenge, we propose an EEG-based EVS satisfaction assessment BCI by raising a Point-to-Global graph representation learning strategy (P2G) that efficiently identifies satisfaction level through a parallel coding module and a graph-based brain region perception module. P2G captures satisfaction-sensitive graph representations in EEG samples based on coding and integrating point features and the global topography. Results: We validate the effectiveness of introducing a P2G learning strategy in EVS satisfaction modeling using a self-constructed dataset and a relevant public dataset, and our method outperforms existing methods. Additionally, we provide a detailed visual analysis to unveil neural markers associated with EVS satisfaction, thereby laying a scientific foundation for the optimization and development of video services.},
}

@article {pmid40656548,
year = {2025},
author = {Mueller, NN and Ocoko, MYM and Kim, Y and Li, K and Gisser, K and Glusauskas, G and Lugo, I and Dernelle, P and Hermoso, AC and Wang, J and Duncan, J and Druschel, LN and Graham, F and Capadona, JR and Hess-Dunning, A},
title = {Mechanically-adaptive, resveratrol-eluting neural probes for improved intracortical recording performance and stability.},
journal = {Npj flexible electronics},
volume = {9},
number = {1},
pages = {64},
pmid = {40656548},
issn = {2397-4621},
abstract = {Intracortical microelectrodes are used for recording activity from individual neurons, providing both a valuable neuroscience tool and an enabling medical technology for individuals with motor disabilities. Standard neural probes carrying the microelectrodes are rigid silicon-based structures that can penetrate the brain parenchyma to interface with the targeted neurons. Unfortunately, within weeks after implantation, neural recording quality from microelectrodes degrades, owing largely to a neuroinflammatory response. Key contributors to the neuroinflammatory response include mechanical mismatch at the device-tissue interface and oxidative stress. We developed a mechanically-adaptive, resveratrol-eluting (MARE) neural probe to mitigate both mechanical mismatch and oxidative stress and thereby promote improved neural recording quality and longevity. In this work, we demonstrate that compared to rigid silicon controls, highly-flexible MARE probes exhibit improved recording performance, more stable impedance, and a healing tissue response. With further optimization, MARE probes can serve as long-term, robust neural probes for brain-machine interface applications.},
}

@article {pmid40493396,
year = {2025},
author = {Xiang, S and Chen, P and Shi, X and Cai, H and Shen, Z and Liu, L and Xu, A and Zhang, J and Zhang, X and Bing, S and Wang, J and Shao, X and Cao, J and Yang, B and He, Q and Ying, M},
title = {Disruption of the KLHL37-N-Myc complex restores N-Myc degradation and arrests neuroblastoma growth in mouse models.},
journal = {The Journal of clinical investigation},
volume = {135},
number = {14},
pages = {},
pmid = {40493396},
issn = {1558-8238},
mesh = {*Neuroblastoma/metabolism/pathology/genetics/drug therapy ; Animals ; *N-Myc Proto-Oncogene Protein/metabolism/genetics ; Humans ; Mice ; *Proteolysis ; Cell Line, Tumor ; Xenograft Model Antitumor Assays ; Proto-Oncogene Proteins c-myc ; },
abstract = {The N-Myc gene MYCN amplification accounts for the most common genetic aberration in neuroblastoma and strongly predicts the aggressive progression and poor clinical prognosis. However, clinically effective therapies that directly target N-Myc activity are limited. N-Myc is a transcription factor, and its stability is tightly controlled by ubiquitination-dependent proteasomal degradation. Here, we discovered that Kelch-like protein 37 (KLHL37) played a crucial role in enhancing the protein stability of N-Myc in neuroblastoma. KLHL37 directly interacted with N-Myc to disrupt N-Myc-FBXW7 interaction, thereby stabilizing N-Myc and enabling tumor progression. Suppressing KLHL37 effectively induced the degradation of N-Myc and had a profound inhibitory effect on the growth of MYCN-amplified neuroblastoma. Notably, we identified RTA-408 as an inhibitor of KLHL37 to disrupt the KLHL37-N-Myc complex, promoting the degradation of N-Myc and suppressing neuroblastoma in vivo and in vitro. Moreover, we elucidated the therapeutic potential of RTA-408 for neuroblastoma using patient-derived neuroblastoma cell and patient-derived xenograft tumor models. RTA408's antitumor effects may not occur exclusively via KLHL37, and specific KLHL37 inhibitors are expected to be developed in the future. These findings not only uncover the biological function of KLHL37 in regulating N-Myc stability, but also indicate that KLHL37 inhibition is a promising therapeutic regimen for neuroblastoma, especially in patients with MYCN-amplified tumors.},
}

*Neuroblastoma/metabolism/pathology/genetics/drug therapy
Animals
*N-Myc Proto-Oncogene Protein/metabolism/genetics
Humans
Mice
*Proteolysis
Cell Line, Tumor
Xenograft Model Antitumor Assays
Proto-Oncogene Proteins c-myc

@article {pmid40656455,
year = {2025},
author = {Komosar, M and Tamburro, G and Graichen, U and Comani, S and Haueisen, J},
title = {Combination of spatial and temporal de-noising and artifact reduction techniques in multi-channel dry EEG.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1576954},
pmid = {40656455},
issn = {1662-4548},
abstract = {INTRODUCTION: Dry electroencephalography (EEG) allows for recording cortical activity in ecological scenarios with a high channel count, but it is often more prone to artifacts as compared to gel-based EEG. Spatial harmonic analysis (SPHARA) and ICA-based methods (Fingerprint and ARCI) have been separately used in previous studies for dry EEG de-noising and physiological artifact reduction. Here, we investigate if the combination of these techniques further improves EEG signal quality. For this purpose, we also introduced an improved version of SPHARA.

METHODS: Dry 64-channel EEG was recorded from 11 healthy volunteers during a motor performance paradigm (left and right hand, feet, and tongue movements). EEG signals were denoised separately using Fingerprint + ARCI, SPHARA, a combination of these two methods, and a combination of these two methods including an improved SPHARA version. The improved version of SPHARA includes an additional zeroing of artifactual jumps in single channels before application of SPHARA. The EEG signal quality after application of each denoising method was calculated by means of standard deviation (SD), signal to noise ratio (SNR), and root mean square deviation (RMSD), and a generalized linear mixed effects (GLME) model was used to identify significant changes of these parameters and quantify the changes in the EEG signal quality.

RESULTS: The grand average values of SD improved from 9.76 (reference preprocessed EEG) to 8.28, 7.91, 6.72, and 6.15 μV for Fingerprint + ARCI, SPHARA, Fingerprint + ARCI + SPHARA, and Fingerprint + ARCI + improved SPHARA, respectively. Similarly, the RMSD values improved from 4.65 to 4.82, 6.32, and 6.90 μV, and the SNR values changed from 2.31 to 1.55, 4.08, and 5.56 dB.

DISCUSSION: Our results demonstrate the different performance aspects of Fingerprint + ARCI and SPHARA, artifact reduction and de-noising techniques that complement each other. We also demonstrated that a combination of these techniques yields superior performance in the reduction of artifacts and noise in dry EEG recordings, which can be extended to infant EEG and adult MEG applications.},
}

@article {pmid40655558,
year = {2025},
author = {Alonso-Vázquez, D and Mendoza-Montoya, O and Caraza, R and Martinez, HR and Antelis, JM},
title = {From pronounced to imagined: improving speech decoding with multi-condition EEG data.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1583428},
pmid = {40655558},
issn = {1662-5196},
abstract = {INTRODUCTION: Imagined speech decoding using EEG holds promising applications for individuals with motor neuron diseases, although its performance remains limited due to small dataset sizes and the absence of sensory feedback. Here, we investigated whether incorporating EEG data from overt (pronounced) speech could enhance imagined speech classification.

METHODS: Our approach systematically compares four classification scenarios by modifying the training dataset: intra-subject (using only imagined speech, combining overt and imagined speech, and using only overt speech) and multi-subject (combining overt speech data from different participants with the imagined speech of the target participant). We implemented all scenarios using the convolutional neural network EEGNet. To this end, twenty-four healthy participants pronounced and imagined five Spanish words.

RESULTS: In binary word-pair classifications, combining overt and imagined speech data in the intra-subject scenario led to accuracy improvements of 3%-5.17% in four out of 10 word pairs, compared to training with imagined speech only. Although the highest individual accuracy (95%) was achieved with imagined speech alone, the inclusion of overt speech data allowed more participants to surpass 70% accuracy, increasing from 10 (imagined only) to 15 participants. In the intra-subject multi-class scenario, combining overt and imagined speech did not yield statistically significant improvements over using imagined speech exclusively.

DISCUSSION: Finally, we observed that features such as word length, phonological complexity, and frequency of use contributed to higher discriminability between certain imagined word pairs. These findings suggest that incorporating overt speech data can improve imagined speech decoding in individualized models, offering a feasible strategy to support the early adoption of brain-computer interfaces before speech deterioration occurs in individuals with motor neuron diseases.},
}

@article {pmid40654838,
year = {2025},
author = {Lin, X and Zhang, X and Wang, Z and Chen, J and Lee, J and Lee, AJ and Yang, H and Remy, A and Shen, H and He, Y and Zhao, H and Zhang, X and Wang, W and Aljović, A and Vlassak, JJ and Lu, N and Liu, J},
title = {Plastic-elastomer heterostructure for robust flexible brain-computer interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.29.651325},
pmid = {40654838},
issn = {2692-8205},
abstract = {Electronics for neural signal recording must be robust across multiple and deep brain regions while preserving tissue-level flexibility to ensure stable tracking over months or years. However, existing electronics cannot simultaneously achieve robustness and tissue-level flexibility, limiting their potential for customizable and scalable neuroscience research and clinical applications. Here, we introduce FlexiSoft, an electronic platform based on a plastic-elastomer heterostructure that uniquely integrates mechanical robustness and tissue-level flexibility. Compared to conventional flexible electronics of similar thickness, the FlexiSoft platform demonstrates an order-of- magnitude improvement in both mechanical robustness (critical energy release rate) and flexibility (flexural rigidity). Leveraging these mechanical advantages, we developed FlexiSoft probe for robust implantation, demonstrated by its ability to withstand repeated insertion and removal, as well as to reach centimeter-scale depths comparable to those in the human brain. The platform enables long-term recording from the same neurons across the hippocampus (HPC) and primary motor cortex (M1) during a months-long motor learning task, thereby revealing long-term dynamic changes in neuronal firing patterns. Additionally, FlexiSoft's unique robustness and flexibility enable curved implantation routes, opening new directions of customizable implantation pathways. In summary, we present FlexiSoft as a novel, robust, and tissue-level flexible heterostructure electronics platform that advances flexible brain-computer interfaces (BCIs) with strong translational potential for neuroscience and clinical applications.},
}

@article {pmid40653584,
year = {2025},
author = {Monteiro, RV and Amarante, JEV and Bona, VS and Lins, RBE and Lopes, GC and Blackburn, M and Swanson, C and Latorre, JA and De Souza, GM},
title = {Microshear bond strength of conventional and bioactive restorative materials to irradiated and non-irradiated dentin: an in vitro study.},
journal = {Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer},
volume = {33},
number = {8},
pages = {688},
pmid = {40653584},
issn = {1433-7339},
mesh = {Humans ; *Dentin/radiation effects/ultrastructure ; *Composite Resins/chemistry ; *Dental Bonding/methods ; Materials Testing ; In Vitro Techniques ; Shear Strength ; Microscopy, Electron, Scanning ; Resin Cements/chemistry ; Dentin-Bonding Agents/chemistry ; Surface Properties ; Time Factors ; },
abstract = {PURPOSE: To evaluate the effect of conventional and bioactive restorative materials on bond strength to control (non-irradiated) and irradiated dentin.

METHODS: Human dentin fragments (240) were polished and divided into non-irradiated dentin (NI; n = 120) and irradiated dentin (ID; n = 120). ID specimens received 70 Gy irradiation (2 Gy/fraction, 5 days/week for 7 weeks). All dentin surfaces were bonded to restorative materials with Scotchbond universal adhesive in self-etching mode. Microshear bond strength cylinders were built on the bonded surface according to the restorative material (4 subgroups, n = 30): conventional resin composite (CC-Filtek Z250) and three bioactive restorative composites (BCI-Activa BioActive-Restorative; BCII-Beautiful II; BCIII-Predicta Bulk). Specimens were stored in water at 37 °C for 24 h or 30 days and subjected to microshear bond strength test. The data was analyzed using two-way ANOVA and Tukey's post-hoc test (⍺ < 0.05). The morphological surface of both NI and ID dentin (n = 3) was analyzed using Scanning Electron Microscopy (SEM).

RESULTS: Two-way ANOVA revealed a significant effect of the Time/Radiation (p < 0.001). Restorative material (p = 0.191) and the interaction Time/Radiation*Restorative material (p = 0.169) were not significant. Irradiation decreased the bond strength of CC specimens at both 24 h (p < 0.001) and 30 days (p < 0.001). None of the bioactive materials were significantly affected by irradiation and storage time. The SEM analysis revealed morphological changes in the ID specimens.

CONCLUSION: Ionizing radiation-induced morphological changes in the dentin surface. These changes negatively affected the conventional resin composite bond strengths to dentin. However, these morphological alterations did not affect the bond strength of the bioactive restorative materials.},
}

Humans
*Dentin/radiation effects/ultrastructure
*Composite Resins/chemistry
*Dental Bonding/methods
Materials Testing
In Vitro Techniques
Shear Strength
Microscopy, Electron, Scanning
Resin Cements/chemistry
Dentin-Bonding Agents/chemistry
Surface Properties
Time Factors

@article {pmid40649800,
year = {2025},
author = {Jaszczuk, P and Bratelj, D and Capone, C and Rudnick, M and Pötzel, T and Verma, RK and Fiechter, M},
title = {Advances in Neuromodulation and Digital Brain-Spinal Cord Interfaces for Spinal Cord Injury.},
journal = {International journal of molecular sciences},
volume = {26},
number = {13},
pages = {},
pmid = {40649800},
issn = {1422-0067},
mesh = {Humans ; *Spinal Cord Injuries/therapy/rehabilitation/physiopathology ; *Brain-Computer Interfaces ; *Spinal Cord Stimulation/methods ; Spinal Cord/physiopathology ; Animals ; },
abstract = {Spinal cord injury (SCI) results in a significant loss of motor, sensory, and autonomic function, imposing substantial biosocial and economic burdens. Traditional approaches, such as stem cell therapy and immune modulation, have faced translational challenges, whereas neuromodulation and digital brain-spinal cord interfaces combining brain-computer interface (BCI) technology and epidural spinal cord stimulation (ESCS) to create brain-spine interfaces (BSIs) offer promising alternatives by leveraging residual neural pathways to restore physiological function. This review examines recent advancements in neuromodulation, focusing on the future translation of clinical trial data to clinical practice. We address key considerations, including scalability, patient selection, surgical techniques, postoperative rehabilitation, and ethical implications. By integrating interdisciplinary collaboration, standardized protocols, and patient-centered design, neuromodulation has the potential to revolutionize SCI rehabilitation, reducing long-term disability and enhancing quality of life globally.},
}

Humans
*Spinal Cord Injuries/therapy/rehabilitation/physiopathology
*Brain-Computer Interfaces
*Spinal Cord Stimulation/methods
Spinal Cord/physiopathology
Animals

@article {pmid40648603,
year = {2025},
author = {Bonanno, M and Saracino, B and Ciancarelli, I and Panza, G and Manuli, A and Morone, G and Calabrò, RS},
title = {Assistive Technologies for Individuals with a Disability from a Neurological Condition: A Narrative Review on the Multimodal Integration.},
journal = {Healthcare (Basel, Switzerland)},
volume = {13},
number = {13},
pages = {},
pmid = {40648603},
issn = {2227-9032},
abstract = {BACKGROUND/OBJECTIVES: Neurological disorders often result in a broad spectrum of disabilities that impact mobility, communication, cognition, and sensory processing, leading to significant limitations in independence and quality of life. Assistive technologies (ATs) offer tools to compensate for these impairments, support daily living, and improve quality of life. The World Health Organization encourages the adoption and diffusion of effective assistive technology (AT). This narrative review aims to explore the integration, benefits, and challenges of assistive technologies in individuals with neurological disabilities, focusing on their role across mobility, communication, cognitive, and sensory domains.

METHODS: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders.

RESULTS: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain-computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach.

CONCLUSIONS: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain-computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users' physiological and behavioral data to optimize support in daily tasks.},
}

@article {pmid40648390,
year = {2025},
author = {Jezierska, K and Turoń-Skrzypińska, A and Rotter, I and Syroka, A and Łukowiak, M and Rawojć, K and Rawojć, P and Rył, A},
title = {Latency and Amplitude of Cortical Activation in Interactive vs. Passive Tasks: An fNIRS Study Using the NefroBall System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
pmid = {40648390},
issn = {1424-8220},
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Brain Mapping/methods ; Brain-Computer Interfaces ; Movement/physiology ; *Prefrontal Cortex/physiology ; *Spectroscopy, Near-Infrared/methods ; *Visual Cortex/physiology ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) allows non-invasive assessment of cortical activity during naturalistic tasks. This study aimed to compare cortical activation dynamics-specifically the latency (tmax) and amplitude (ΔoxyHb) of oxygenated haemoglobin changes-in passive observation and an interactive task using the Nefroball system. A total of 117 healthy adults performed two tasks involving rhythmic hand movements: a passive protocol and an interactive game-controlled condition. fNIRS recorded signals from the visual, parietal, motor, and prefrontal cortices of the left hemisphere. The Mann-Whitney test revealed significantly shorter tmax in all areas during the interactive task, suggesting faster recruitment of cortical networks. ΔoxyHb amplitude was significantly higher only in the visual cortex during the interactive task, indicating increased visual processing demand. No significant ΔoxyHb differences were observed in the motor, prefrontal, or parietal cortices. Weak but significant positive correlations were found between tmax and ΔoxyHb in the motor and prefrontal regions, but only in the passive condition. These findings support the notion that interactive tasks elicit faster, though not necessarily stronger, cortical responses. The results have potential implications for designing rehabilitation protocols and brain-computer interfaces involving visual-motor integration.},
}

Adult
Female
Humans
Male
Young Adult
Brain Mapping/methods
Brain-Computer Interfaces
Movement/physiology
*Prefrontal Cortex/physiology
*Spectroscopy, Near-Infrared/methods
*Visual Cortex/physiology