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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 24 Jul 2024 at 01:39 Created: 

Brain-Computer Interface

Wikipedia: A brain–computer interface (BCI), sometimes called a neural control interface (NCI), mind–machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. BCI-effected sensory input: Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. BCI-effected motor output: When artificial intelligence is used to decode neural activity, then send that decoded information to some kind of effector device, BCIs have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. However, in May of 2021 a study showed that an AI/BCI system could be use to translate thoughts about handwriting into the output of legible characters at a usable rate (90 characters per minute with 94% accuracy).

Created with PubMed® Query: (bci OR (brain-computer OR brain-machine OR mind-machine OR neural-control interface) NOT 26799652[PMID] ) NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)

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RevDate: 2024-07-18
CmpDate: 2024-07-18

Han Y, Du L, Huang Q, et al (2024)

Enhancing specialization of attention-related EEG power and phase synchronism brain patterns by meditation.

Cerebral cortex (New York, N.Y. : 1991), 34(7):.

Meditation, mental training that aims to improve one's ability to regulate their cognition, has been widely applied in clinical medicine. However, the mechanism by which meditation affects brain activity is still unclear. To explore this question, electroencephalogram data were recorded in 20 long-term meditators and 20 nonmeditators during 2 high-level cognitive tasks (meditation and mental calculation) and a relaxed resting state (control). Then, the power spectral density and phase synchronization of the electroencephalogram were extracted and compared between these 2 groups. In addition, machine learning was used to discriminate the states within each group. We found that the meditation group showed significantly higher classification accuracy and calculation efficiency than the control group. Then, during the calculation task, both the power and global phase synchronism of the gamma response decreased in meditators compared to their relaxation state; yet, no such change was observed in the control group. A potential explanation for our observations is that meditation improved the flexibility of the brain through neural plastic mechanism. In conclusion, we provided robust evidence that long-term meditation experience could produce detectable neurophysiological changes in brain activity, which possibly enhance the functional segregation and/or specialization in the brain.

RevDate: 2024-07-19

Xi Y, Schriver KE, Roe AW, et al (2024)

Quantifying tissue temperature changes induced by infrared neural stimulation: numerical simulation and MR thermometry.

Biomedical optics express, 15(7):4111-4131.

Infrared neural stimulation (INS) delivered via short pulse trains is an innovative tool that has potential for us use for studying brain function and circuitry, brain machine interface, and clinical use. The prevailing mechanism for INS involves the conversion of light energy into thermal transients, leading to neuronal membrane depolarization. Due to the potential risks of thermal damage, it is crucial to ensure that the resulting local temperature increases are within non-damaging limits for brain tissues. Previous studies have estimated damage thresholds using histological methods and have modeled thermal effects based on peripheral nerves. However, additional quantitative measurements and modeling studies are needed for the central nervous system. Here, we performed 7 T MRI thermometry on ex vivo rat brains following the delivery of infrared pulse trains at five different intensities from 0.1-1.0 J/cm[2] (each pulse train 1,875 nm, 25 us/pulse, 200 Hz, 0.5 s duration, delivered through 200 µm fiber). Additionally, we utilized the General BioHeat Transfer Model (GBHTM) to simulate local temperature changes in perfused brain tissues while delivering these laser energies to tissue (with optical parameters of human skin) via three different sizes of optical fibers at five energy intensities. The simulation results clearly demonstrate that a 0.5 second INS pulse train induces an increase followed by an immediate drop in temperature at stimulation offset. The delivery of multiple pulse trains with 2.5 s interstimulus interval (ISI) leads to rising temperatures that plateau. Both thermometry and modeling results show that, using parameters that are commonly used in biological applications (200 µm diameter fiber, 0.1-1.0 J/cm[2]), the final temperature increase at the end of the 60 sec stimuli duration does not exceed 1°C with stimulation values of 0.1-0.5 J/cm[2] and does not exceed 2°C with stimulation values of up to 1.0 J/cm[2]. Thus, the maximum temperature rise is consistent with the thermal damage threshold reported in previous studies. This study provides a quantitative evaluation of the temperature changes induced by INS, suggesting that existing practices pose minimal major safety concerns for biological tissues.

RevDate: 2024-07-18

Wang J, Huang Y, Wu L, et al (2024)

Sleep-specific repetitive negative thinking processes and prenatal insomnia symptoms: A naturalistic follow-up study from mid- to late-pregnancy.

Journal of sleep research [Epub ahead of print].

Insomnia symptoms are highly prevalent during pregnancy; therefore, identifying modifiable risk markers is important for risk prediction and early intervention. This study aimed to examine the role of sleep-specific rumination and sleep-specific worry in prenatal insomnia symptoms. A total of 859 married pregnant women without history of psychiatric illnesses (mean [standard deviation] age, 30.15 [3.86] years; 593 [69.0%] with a bachelor's degree or above) were enrolled from the obstetrical outpatient departments of two tertiary comprehensive hospitals in Shandong, China, who completed assessments of sleep-specific rumination, sleep-specific worry, and insomnia symptoms at baseline (mid-pregnancy) and follow-up (late-pregnancy). Measures included Daytime Insomnia Symptom Response Scale, Anxiety and Preoccupation about Sleep Questionnaire, and Insomnia Severity Index. Our results showed that after controlling for covariates, both sleep-specific rumination and sleep-specific worry showed significant concurrent and prospective associations with insomnia symptoms, and the increases in scores of sleep-specific rumination and sleep-specific worry over time were significantly associated with the increased likelihood of insomnia symptoms at follow-up. Moreover, the increases in sleep-specific rumination and sleep-specific worry over time were significantly associated with the increased likelihood of reporting newly developed insomnia symptoms rather than persistent normal sleep. However, the changes in sleep-specific rumination and sleep-specific worry were not significantly associated with the likelihood of reporting persistent or remitted insomnia symptoms rather than persistent normal sleep. In conclusion, sleep-specific rumination and sleep-specific worry were significantly associated with concurrent or subsequent insomnia symptoms; thus, they may be promising cognitive risk markers and intervention targets.

RevDate: 2024-07-17

Mohammadi Z, Denman D, Klug A, et al (2024)

A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature.

Journal of neural engineering [Epub ahead of print].

The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy - but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these alogorithms. Here we report our Graph nEtwork Multichannel (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features. These algorithmic modifications allow GEMsort to rapidly sort neural spikes and this approach is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability. Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.

RevDate: 2024-07-17

Zhu Z, Miao L, Li K, et al (2024)

A hypothalamic-amygdala circuit underlying sexually dimorphic aggression.

Neuron pii:S0896-6273(24)00457-4 [Epub ahead of print].

Male animals often display higher levels of aggression than females. However, the neural circuitry mechanisms underlying this sexually dimorphic aggression remain elusive. Here, we identify a hypothalamic-amygdala circuit that mediates male-biased aggression in mice. Specifically, the ventrolateral part of the ventromedial hypothalamus (VMHvl), a sexually dimorphic region associated with eliciting male-biased aggression, projects densely to the posterior substantia innominata (pSI), an area that promotes similar levels of attack in both sexes of mice. Although the VMHvl innervates the pSI unidirectionally through both excitatory and inhibitory connections, it is the excitatory VMHvl-pSI projections that are strengthened in males to promote aggression, whereas the inhibitory connections that reduce aggressive behavior are strengthened in females. Consequently, the convergent hypothalamic input onto the pSI leads to heightened pSI activity in males, resulting in male-biased aggression. Our findings reveal a sexually distinct excitation-inhibition balance of a hypothalamic-amygdala circuit that underlies sexually dimorphic aggression.

RevDate: 2024-07-18

Herbert C, G Northoff (2024)

Editorial: Analyzing and computing humans - the role of language, culture, brain and health.

Frontiers in human neuroscience, 18:1439729.

RevDate: 2024-07-18

He Q, Yang C, Xu Y, et al (2024)

Anatomical-related factors and outcome of percutaneous short-term spinal cord stimulation electrode shift in patients with disorders of consciousness: a retrospective study.

Frontiers in aging neuroscience, 16:1403156.

BACKGROUND: Disorders of consciousness (DoC) represent a spectrum of neurological conditions that pose significant treatment challenges. Percutaneous short-term spinal cord stimulation (SCS) has emerged as a promising experimental diagnostic treatment to assess and potentially improve consciousness levels. However, the effectiveness of this intervention is frequently compromised by the shift of electrodes, particularly in the cervical region, which can negatively affect therapeutic outcomes.

METHODS: This retrospective study aimed to study if electrodes shift in percutaneous short-term SCS in patients with DoC would affect the outcome. We analyzed the relationship between electrode shift length and patient outcome, as well as the correlation with various anatomical parameters, including the actual length of the cervical spine, linear length, spinal canal transverse diameter, spinal canal diameter, and C2 cone height, in a cohort of patients undergoing the procedure.

RESULTS: Our findings revealed that in patients with better outcome, there are significant less patient with electrode shift (p = 0.019). Further, a linear correlation was found between the length of electrode shift and patients' outcome (Rho = 0.583, p = 0.002), with longer shift lengths associated with poorer outcomes. Contrary to our expectations, there was no significant association between the measured anatomical parameters and the extent of electrode shift. However, a trend was found between the actual length of the cervical spine and the shift of the electrode (p = 0.098). Notably, the shorter spinal canal transverse diameter was found to be significantly associated with better outcome in patients with DoC receiving percutaneous short-term SCS (p = 0.033).

CONCLUSION: These results highlight the clinical importance of electrode stability in the cervical region during SCS treatment for patients with DoC. Ensuring secure placement of electrodes may play a crucial role in enhancing patients' outcome and minimize postoperative complications. Given the lack of association with expected anatomical parameters, future research should investigate other factors that could impact electrode stability to optimize this therapeutic intervention.

RevDate: 2024-07-19
CmpDate: 2024-07-16

Lyreskog DM, Zohny H, Mann SP, et al (2024)

Decentralising the Self - Ethical Considerations in Utilizing Decentralised Web Technology for Direct Brain Interfaces.

Science and engineering ethics, 30(4):28.

The rapidly advancing field of brain-computer (BCI) and brain-to-brain interfaces (BBI) is stimulating interest across various sectors including medicine, entertainment, research, and military. The developers of large-scale brain-computer networks, sometimes dubbed 'Mindplexes' or 'Cloudminds', aim to enhance cognitive functions by distributing them across expansive networks. A key technical challenge is the efficient transmission and storage of information. One proposed solution is employing blockchain technology over Web 3.0 to create decentralised cognitive entities. This paper explores the potential of a decentralised web for coordinating large brain-computer constellations, and its associated benefits, focusing in particular on the conceptual and ethical challenges this innovation may pose pertaining to (1) Identity, (2) Sovereignty (encompassing Autonomy, Authenticity, and Ownership), (3) Responsibility and Accountability, and (4) Privacy, Safety, and Security. We suggest that while a decentralised web can address some concerns and mitigate certain risks, underlying ethical issues persist. Fundamental questions about entity definition within these networks, the distinctions between individuals and collectives, and responsibility distribution within and between networks, demand further exploration.

RevDate: 2024-07-17

Song J, Zhai Q, Wang C, et al (2024)

EEGGAN-Net: enhancing EEG signal classification through data augmentation.

Frontiers in human neuroscience, 18:1430086.

BACKGROUND: Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, the constrained accuracy of electroencephalography (EEG) signal classification poses numerous hurdles in real-world applications.

METHODS: In response to this predicament, we introduce a novel EEG signal classification model termed EEGGAN-Net, leveraging a data augmentation framework. By incorporating Conditional Generative Adversarial Network (CGAN) data augmentation, a cropped training strategy and a Squeeze-and-Excitation (SE) attention mechanism, EEGGAN-Net adeptly assimilates crucial features from the data, consequently enhancing classification efficacy across diverse BCI tasks.

RESULTS: The EEGGAN-Net model exhibits notable performance metrics on the BCI Competition IV-2a and IV-2b datasets. Specifically, it achieves a classification accuracy of 81.3% with a kappa value of 0.751 on the IV-2a dataset, and a classification accuracy of 90.3% with a kappa value of 0.79 on the IV-2b dataset. Remarkably, these results surpass those of four other CNN-based decoding models.

CONCLUSIONS: In conclusion, the amalgamation of data augmentation and attention mechanisms proves instrumental in acquiring generalized features from EEG signals, ultimately elevating the overall proficiency of EEG signal classification.

RevDate: 2024-07-19

Liu ML, Liu YP, Guo XX, et al (2024)

Orientation selectivity mapping in the visual cortex.

Progress in neurobiology, 240:102656 pii:S0301-0082(24)00092-3 [Epub ahead of print].

The orientation map is one of the most well-studied functional maps of the visual cortex. However, results from the literature are of different qualities. Clear boundaries among different orientation domains and blurred uncertain distinctions were shown in different studies. These unclear imaging results will lead to an inaccuracy in depicting cortical structures, and the lack of consideration in experimental design will also lead to biased depictions of the cortical features. How we accurately define orientation domains will impact the entire field of research. In this study, we test how spatial frequency (SF), stimulus size, location, chromatic, and data processing methods affect the orientation functional maps (including a large area of dorsal V4, and parts of dorsal V1) acquired by intrinsic signal optical imaging. Our results indicate that, for large imaging fields, large grating stimuli with mixed SF components should be considered to acquire the orientation map. A diffusion model image enhancement based on the difference map could further improve the map quality. In addition, the similar outcomes of achromatic and chromatic gratings indicate two alternative types of afferents from LGN, pooling in V1 to generate cue-invariant orientation selectivity.

RevDate: 2024-07-15

Liu H, Ding S, Lin X, et al (2024)

Bone Fracture Healing under the Intervention of a Stretchable Ultrasound Array.

ACS nano [Epub ahead of print].

Ultrasound treatment has been recognized as an effective and noninvasive approach to promote fracture healing. However, traditional rigid ultrasound probe is bulky, requiring cumbersome manual operations and inducing unfavorable side effects when functioning, which precludes the wide application of ultrasound in bone fracture healing. Here, we report a stretchable ultrasound array for bone fracture healing, which features high-performance 1-3 piezoelectric composites as transducers, stretchable multilayered serpentine metal films in a bridge-island pattern as electrical interconnects, soft elastomeric membranes as encapsulations, and polydimethylsiloxane (PDMS) with low curing agent ratio as adhesive layers. The resulting ultrasound array offers the benefits of large stretchability for easy skin integration and effective affecting region for simple skin alignment with good electromechanical performance. Experimental investigations of the stretchable ultrasound array on the delayed union model in femoral shafts of rats show that the callus growth is more active in the second week of treatment and the fracture site is completely osseous healed in the sixth week of treatment. Various bone quality indicators (e.g., bone modulus, bone mineral density, bone tissue/total tissue volume, and trabecular bone thickness) could be enhanced with the intervention of a stretchable ultrasound array. Histological and immunohistochemical examinations indicate that ultrasound promotes osteoblast differentiation, bone formation, and remodeling by promoting the expression of osteopontin (OPN) and runt-related transcription factor 2 (RUNX2). This work provides an effective tool for bone fracture healing in a simple and convenient manner and creates engineering opportunities for applying ultrasound in medical applications.

RevDate: 2024-07-16

Wang R, Wang X, Platt ML, et al (2024)

Decomposing loss aversion from a single neural signal.

iScience, 27(7):110153.

People often display stronger aversion to losses than appetite for equivalent gains, a widespread phenomenon known as loss aversion. The prevailing theory attributes loss aversion to a valuation bias that amplifies losses relative to gains. An alternative account attributes loss aversion to a response bias that avoids choices that might result in loss. By modeling the temporal dynamics of scalp electrical activity during decisions to accept or reject gambles within a sequential sampling framework, we decomposed valuation bias and response bias from a single event-related neural signal, the P3. Specifically, we found valuation bias manifested as larger sensitivity of P3 to losses than gains, which was localizable to reward-related brain regions. By contrast, response bias manifested as larger P3 preceding gamble acceptance than rejection and was localizable to motor cortex. Our study reveals the dissociable neural biomarkers of response bias and valuation bias underpinning loss-averse decisions.

RevDate: 2024-07-16

van Stuijvenberg OC, Samlal DPS, Vansteensel MJ, et al (2024)

The ethical significance of user-control in AI-driven speech-BCIs: a narrative review.

Frontiers in human neuroscience, 18:1420334.

AI-driven brain-computed interfaces aimed at restoring speech for individuals living with locked-in-syndrome are paired with ethical implications for user's autonomy, privacy and responsibility. Embedding options for sufficient levels of user-control in speech-BCI design has been proposed to mitigate these ethical challenges. However, how user-control in speech-BCIs is conceptualized and how it relates to these ethical challenges is underdetermined. In this narrative literature review, we aim to clarify and explicate the notion of user-control in speech-BCIs, to better understand in what way user-control could operationalize user's autonomy, privacy and responsibility and explore how such suggestions for increasing user-control can be translated to recommendations for the design or use of speech-BCIs. First, we identified types of user control, including executory control that can protect voluntariness of speech, and guidance control that can contribute to semantic accuracy. Second, we identified potential causes for a loss of user-control, including contributions of predictive language models, a lack of ability for neural control, or signal interference and external control. Such a loss of user control may have implications for semantic accuracy and mental privacy. Third we explored ways to design for user-control. While embedding initiation signals for users may increase executory control, they may conflict with other aims such as speed and continuity of speech. Design mechanisms for guidance control remain largely conceptual, similar trade-offs in design may be expected. We argue that preceding these trade-offs, the overarching aim of speech-BCIs needs to be defined, requiring input from current and potential users. Additionally, conceptual clarification of user-control and other (ethical) concepts in this debate has practical relevance for BCI researchers. For instance, different concepts of inner speech may have distinct ethical implications. Increased clarity of such concepts can improve anticipation of ethical implications of speech-BCIs and may help to steer design decisions.

RevDate: 2024-07-14

White AJ, Kelly-Hedrick M, Miranda SP, et al (2024)

Bioethics and Neurosurgery: An Overview of Existing and Emerging Topics for the Practicing Neurosurgeon.

World neurosurgery pii:S1878-8750(24)01191-4 [Epub ahead of print].

Neurosurgery is field with complex ethical issues. In this article, we aim to provide an overview of key and emerging ethical issues in neurosurgery with a focus on issues relevant to practicing neurosurgeons. These issues include those of informed consent, capacity, clinical trials, emerging neurotechnology, innovation, equity and justice, and emerging bioethics areas including community engagement and organizational ethics. We argue bioethics can help neurosurgeons think about and address these issues, and in turn, the field of bioethics can benefit from engagement by neurosurgeons. Several ideas for increasing engagement in bioethics are proposed.

RevDate: 2024-07-12

Guo J, Zhou YL, Yang Y, et al (2024)

Structural basis of tethered agonism and G protein coupling of protease-activated receptors.

Cell research [Epub ahead of print].

Protease-activated receptors (PARs) are a unique group within the G protein-coupled receptor superfamily, orchestrating cellular responses to extracellular proteases via enzymatic cleavage, which triggers intracellular signaling pathways. Protease-activated receptor 1 (PAR1) is a key member of this family and is recognized as a critical pharmacological target for managing thrombotic disorders. In this study, we present cryo-electron microscopy structures of PAR1 in its activated state, induced by its natural tethered agonist (TA), in complex with two distinct downstream proteins, the Gq and Gi heterotrimers, respectively. The TA peptide is positioned within a surface pocket, prompting PAR1 activation through notable conformational shifts. Contrary to the typical receptor activation that involves the outward movement of transmembrane helix 6 (TM6), PAR1 activation is characterized by the simultaneous downward shift of TM6 and TM7, coupled with the rotation of a group of aromatic residues. This results in the displacement of an intracellular anion, creating space for downstream G protein binding. Our findings delineate the TA recognition pattern and highlight a distinct role of the second extracellular loop in forming β-sheets with TA within the PAR family, a feature not observed in other TA-activated receptors. Moreover, the nuanced differences in the interactions between intracellular loops 2/3 and the Gα subunit of different G proteins are crucial for determining the specificity of G protein coupling. These insights contribute to our understanding of the ligand binding and activation mechanisms of PARs, illuminating the basis for PAR1's versatility in G protein coupling.

RevDate: 2024-07-12

Montero AS, Aliouat I, Ribon M, et al (2024)

Effect of ultrasound-mediated blood-spinal cord barrier opening on survival and motor function in females in an amyotrophic lateral sclerosis mouse model.

EBioMedicine, 106:105235 pii:S2352-3964(24)00270-6 [Epub ahead of print].

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by a progressive loss of motor neurons. The limited efficacy of recent therapies in clinical development may be linked to lack of drug penetration to the affected motor neurons due to the blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB).

METHODS: In this work, the safety and efficacy of repeated short transient opening of the BSCB by low intensity pulsed ultrasound (US, sonication) was studied in females of an ALS mouse model (B6.Cg-Tg(SOD1∗G93A)1Gur/J). The BSCB was disrupted using a 1 MHz ultrasound transducer coupled to the spinal cord, with and without injection of insulin-like growth factor 1 (IGF1), a neurotrophic factor that has previously shown efficacy in ALS models.

FINDINGS: Results in wild-type (WT) animals demonstrated that the BSCB can be safely disrupted and IGF1 concentrations significantly enhanced after a single session of transient BSCB disruption (176 ± 32 μg/g vs. 0.16 ± 0.008 μg/g, p < 0.0001). Five repeated weekly US sessions performed in female ALS mice demonstrated a survival advantage in mice treated with IGF1 and US (US IGF1) compared to treatment with IGF1 alone (176 vs. 166 days, p = 0.0038). Surprisingly, this survival advantage was also present in mice treated with US alone vs. untreated mice (178.5 vs. 166.5 days, p = 0.0061). Muscle strength did not show difference among the groups. Analysis of glial cell immunoreactivity and microglial transcriptome showing reduced cell proliferation pathways, in addition to lymphocyte infiltration, suggested that the beneficial effect of US or US IGF1 could act through immune cell modulation.

INTERPRETATION: These results show the first step towards a possible beneficial impact of transient BSCB opening for ALS therapy and suggest implication of immune cells.

FUNDING: Fondation pour la Recherche Médicale (FRM). Investissements d'avenirANR-10-IAIHU-06, Société Française de Neurochirurgie (SFNC), Fond d'étude et de Recherche du Corps Medical (FERCM), Aide à la Recherche des Maladies du Cerveau (ARMC), SLA Fondation Recherche (SLAFR), French Ministry for High Education and Research (MENR), Carthera, Laboratoire de Recherche en Technologies Chirurgicales Avancées (LRTCA).

RevDate: 2024-07-12

Venot T, Desbois A, Corsi MC, et al (2024)

Intentional binding for noninvasive BCI control.

Journal of neural engineering [Epub ahead of print].

Noninvasive brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood. To address this question, we performed a hybrid EEG-based BCI training involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm. Main results showed that the hand grasping motor imagery timing significantly affects the BCI accuracy evolution as well as the spatiotemporal brain dynamics. Larger accuracy improvement was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions. Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.

RevDate: 2024-07-20

Aljuhani W, Y Sayyad (2024)

Orthopedic Research Funding: Assessing the Relationship between Investments and Breakthroughs.

Orthopedic reviews, 16:120368.

Orthopedic research plays a crucial role in improving patient outcomes for musculoskeletal disorders. This narrative review explores the intricate interplay between funding patterns and the trajectory of breakthroughs achieved in this dynamic field. A meticulous search strategy identified studies illuminating the diverse sources of orthopedic research funding, including public funding (government agencies), philanthropic organizations, private sector investment, and international funding bodies. The review further delved into the spectrum of breakthroughs, encompassing fundamental scientific discoveries, technological advancements, and personalized medicine approaches. Public funding emerged as a significant pillar, supporting foundational research that lays the groundwork for future advancements. Philanthropic organizations addressed specific musculoskeletal disorders, often focusing on patient-centric applications. International funding bodies played a role in supporting research in low- and middle-income countries. Breakthroughs extended beyond cutting-edge prosthetics and minimally invasive surgeries, encompassing fundamental discoveries in areas like gene therapy and biomaterials science. Technological advancements included brain-computer interface prosthetics and 3D-printed implants. Personalized medicine offered the potential for tailored treatments based on individual needs and genetic profiles. This review underscores the complex interplay between funding patterns and breakthroughs in orthopedic research. A multifaceted approach is essential for continued progress. Fostering collaboration, optimizing funding models, and prioritizing both foundational and translational research hold the key to unlocking the true potential of orthopedic research and transforming the lives of patients suffering from musculoskeletal disorders.

RevDate: 2024-07-14

Yan T, Su C, Xue W, et al (2024)

Mobile phone short video use negatively impacts attention functions: an EEG study.

Frontiers in human neuroscience, 18:1383913.

The pervasive nature of short-form video platforms has seamlessly integrated into daily routines, yet it is important to recognize their potential adverse effects on both physical and mental health. Prior research has identified a detrimental impact of excessive short-form video consumption on attentional behavior, but the underlying neural mechanisms remain unexplored. In the current study, we aimed to investigate the effect of short-form video use on attentional functions, measured through the attention network test (ANT). A total of 48 participants, consisting of 35 females and 13 males, with a mean age of 21.8 years, were recruited. The mobile phone short video addiction tendency questionnaire (MPSVATQ) and self-control scale (SCS) were conducted to assess the short video usage behavior and self-control ability. Electroencephalogram (EEG) data were recorded during the completion of the ANT task. The correlation analysis showed a significant negative relationship between MPSVATQ and theta power index reflecting the executive control in the prefrontal region (r = -0.395, p = 0.007), this result was not observed by using theta power index of the resting-state EEG data. Furthermore, a significant negative correlation was identified between MPSVATQ and SCS outcomes (r = -0.320, p = 0.026). These results suggest that an increased tendency toward mobile phone short video addiction could negatively impact self-control and diminish executive control within the realm of attentional functions. This study sheds light on the adverse consequences stemming from short video consumption and underscores the importance of developing interventions to mitigate short video addiction.

RevDate: 2024-07-11
CmpDate: 2024-07-11

van Putten MJAM, Ruijter BJ, Horn J, et al (2024)

Quantitative Characterization of Rhythmic and Periodic EEG Patterns in Patients in a Coma After Cardiac Arrest and Association With Outcome.

Neurology, 103(3):e209608.

OBJECTIVES: Rhythmic and periodic patterns (RPPs) on EEG in patients in a coma after cardiac arrest are associated with a poor neurologic outcome. We characterize RPPs using qEEG in relation to outcomes.

METHODS: Post hoc analysis was conducted on 172 patients in a coma after cardiac arrest from the TELSTAR trial, all with RPPs. Quantitative EEG included corrected background continuity index (BCI*), relative discharge power (RDP), discharge frequency, and shape similarity. Neurologic outcomes at 3 months after arrest were categorized as poor (CPC = 3-5) or good (CPC = 1-2).

RESULTS: A total of 16 patients (9.3%) had a good outcome. Patients with good outcomes showed later RPP onset (28.5 vs 20.1 hours after arrest, p < 0.05) and higher background continuity at RPP onset (BCI[*] = 0.83 vs BCI[*] = 0.59, p < 0.05). BCI* <0.45 at RPP onset, maximum BCI* <0.76, RDP >0.47, or shape similarity >0.75 were consistently associated with poor outcomes, identifying 36%, 22%, 40%, or 24% of patients with poor outcomes, respectively. In patients meeting both BCI* >0.44 at RPP onset and BCI* >0.75 within 72 hours, the probability of good outcomes doubled to 18%.

DISCUSSION: Sufficient EEG background continuity before and during RPPs is crucial for meaningful recovery. Background continuity, discharge power, and shape similarity can help select patients with relevant chances of recovery and may guide treatment.

February 4, 2014, ClinicalTrial.gov, NCT02056236.

RevDate: 2024-07-12

Barrio-Pujante A, Bleriot I, Blasco L, et al (2024)

Regulation of anti-phage defense mechanisms by using cinnamaldehyde as a quorum sensing inhibitor.

Frontiers in microbiology, 15:1416628.

BACKGROUND: Multidrug-resistant bacteria and the shortage of new antibiotics constitute a serious health problem. This problem has led to increased interest in the use of bacteriophages, which have great potential as antimicrobial agents but also carry the risk of inducing resistance. The objective of the present study was to minimize the development of phage resistance in Klebsiella pneumoniae strains by inhibiting quorum sensing (QS) and thus demonstrate the role of QS in regulating defense mechanisms.

RESULTS: Cinnamaldehyde (CAD) was added to K. pneumoniae cultures to inhibit QS and thus demonstrate the role of the signaling system in regulating the anti-phage defense mechanism. The QS inhibitory activity of CAD in K. pneumoniae was confirmed by a reduction in the quantitative expression of the lsrB gene (AI-2 pathway) and by proteomic analysis. The infection assays showed that the phage was able to infect a previously resistant K. pneumoniae strain in the cultures to which CAD was added. The results were confirmed using proteomic analysis. Thus, anti-phage defense-related proteins from different systems, such as cyclic oligonucleotide-based bacterial anti-phage signaling systems (CBASS), restriction-modification (R-M) systems, clustered regularly interspaced short palindromic repeat-Cas (CRISPR-Cas) system, and bacteriophage control infection (BCI), were present in the cultures with phage but not in the cultures with phage and CAD. When the QS and anti-phage defense systems were inhibited by the combined treatment, proteins related to phage infection and proliferation, such as the tail fiber protein, the cell division protein DamX, and the outer membrane channel protein TolC, were detected.

CONCLUSION: Inhibition of QS reduces phage resistance in K. pneumoniae, resulting in the infection of a previously resistant strain by phage, with a significant increase in phage proliferation and a significant reduction in bacterial growth. QS inhibitors could be considered for therapeutic application by including them in phage cocktails or in phage-antibiotic combinations to enhance synergistic effects and reduce the emergence of antimicrobial resistance.

RevDate: 2024-07-14

Osawa T, Wei JT, Abe T, et al (2021)

Comparison of Health-Related Quality of Life Between Japanese and American Patients with Bladder Cancer as Measured by a Newly Developed Japanese Version of the Bladder Cancer Index.

Bladder cancer (Amsterdam, Netherlands), 7(1):61-69.

INTRODUCTION: The aim of this study is to characterize health related quality of life (HRQOL) in Japanese patients after bladder cancer surgery and to perform cross-cultural comparison between Japanese and American patients.

METHODS: Firstly, we cross-sectionally assessed HRQOL of 371 patients in Japan using the Bladder Cancer Index (BCI-Japanese). HRQOL of the four groups of patients (native bladder without intravesical therapy, native bladder with intravesicaltherapy, cystectomy with ileal conduit, and cystectomy with neobladder) were assessed. Secondly, we compared the Japanese with the American cohort (n = 315) from the original BCI paper. After adjusting for age and gender, the differences in each BCI subdomain score was analyzed.

RESULTS: Among Japanese patients, the urinary domain function score was significantly lower among the cystectomy with neobladder group, compared to the cystectomy with ileal conduit group (p < 0. 01). Despite this, the urinary bother was comparable between the two groups. Although there were apparent differences between Japanese and American patients, there were few differences in Urinary and Bowel HRQOL. In three of the four treatment groups (other than native bladder with intravesical therapy), Japanese patients were more likely than Americans to report poor sexual function (p < 0.05). However, Japanese patients were less likely than Americans to be bothered by their lower sexual function, regardless of treatment (p < 0.05).

CONCLUSIONS: HRQOL outcomes following treatment of bladder cancer in Japan are comparable to those in the USA, except for sexual functioning and sexual bother. The BCI can be used for cross-cultural assessments of HRQOL in bladder cancer patients.

RevDate: 2024-07-12

Liang J, Wang Z, Han J, et al (2024)

EEG-based driving intuition and collision anticipation using joint temporal-frequency multi-layer dynamic brain network.

Frontiers in neuroscience, 18:1421010.

Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers. The results show that brain region activity is more stable in the advanced phase of intuition training compared to the initial phase. Particularly in the alart state task, the JTF-MDBN demonstrated stronger connection strength. Multi-layer network analysis indicates that modularity is significantly higher for the non-alert state task than the alert state task in the alpha and beta bands. In the W4 time window (1 second before a collision), we identified significant features that can differentiate situations where a car collision is imminent from those where no collision occurs. Single-layer network analysis also revealed statistical differences in node strength and local efficiency for some EEG channels in the alpha and beta bands during the W4 and W5 time windows. Using these biomarkers to predict vehicle collision risk, the classification accuracy of a linear kernel SVM reached up to 87.5%, demonstrating the feasibility of predicting driving collisions through brain network biomarkers. These findings are important for the study of human intuition and the development of brain-computer interface-based intelligent driving hazard perception assistance systems.

RevDate: 2024-07-12

Hong Y, Ryun S, CK Chung (2024)

Evoking artificial speech perception through invasive brain stimulation for brain-computer interfaces: current challenges and future perspectives.

Frontiers in neuroscience, 18:1428256.

Encoding artificial perceptions through brain stimulation, especially that of higher cognitive functions such as speech perception, is one of the most formidable challenges in brain-computer interfaces (BCI). Brain stimulation has been used for functional mapping in clinical practices for the last 70 years to treat various disorders affecting the nervous system, including epilepsy, Parkinson's disease, essential tremors, and dystonia. Recently, direct electrical stimulation has been used to evoke various forms of perception in humans, ranging from sensorimotor, auditory, and visual to speech cognition. Successfully evoking and fine-tuning artificial perceptions could revolutionize communication for individuals with speech disorders and significantly enhance the capabilities of brain-computer interface technologies. However, despite the extensive literature on encoding various perceptions and the rising popularity of speech BCIs, inducing artificial speech perception is still largely unexplored, and its potential has yet to be determined. In this paper, we examine the various stimulation techniques used to evoke complex percepts and the target brain areas for the input of speech-like information. Finally, we discuss strategies to address the challenges of speech encoding and discuss the prospects of these approaches.

RevDate: 2024-07-10

Park H, SC Jun (2024)

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Although Motor Imagery-based Brain-Computer Interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state EEG network differs between BCI-literacy and -illiteracy.

APPROACH: To address the issues above, we analysed three large public EEG datasets using three functional connectivity (FC) and three effective connectivity (EC) metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for Multivariate Granger Causality (MVGC). The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found.

SIGNIFICANCE: Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

RevDate: 2024-07-10

He X, Li H, Yu P, et al (2024)

DP-MP: a novel cross-subject fatigue detection framework with DANN-based prototypical representation and mix-up pairwise learning.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue detection in real-world scenarios are often challenging, particularly in cases involving subjects not included in the training datasets, owing to bio-individual differences and noisy labels. This study aims to develop an effective framework for cross-subject fatigue detection by addressing these challenges.

APPROACH: In this study, we propose a novel framework, termed DP-MP, for cross-subject fatigue detection, which utilizes a Domain-Adversarial Neural Network (DANN)-based prototypical representation in conjunction with Mix-up pairwise learning. Our proposed DP-MP framework aims to mitigate the impact of bio-individual differences by encoding fatigue-related semantic structures within EEG signals and exploring shared fatigue prototype features across individuals. Notably, to the best of our knowledge, this work is the first to conceptualize fatigue detection as a pairwise learning task, thereby effectively reducing the interference from noisy labels. Furthermore, we propose the Mix-up pairwise learning (MixPa) approach in the field of fatigue detection, which broadens the advantages of pairwise learning by introducing more diverse and informative relationships among samples.

RESULTS: Cross-subject experiments were conducted on two benchmark databases, SEED-VIG and FTEF, achieving state-of-the-art performance with average accuracies of 88.14% and 97.41%, respectively. These promising results demonstrate our model's effectiveness and excellent generalization capability.

SIGNIFICANCE: This is the first time EEG-based fatigue detection has been conceptualized as a pairwise learning task, offering a novel perspective to this field. Moreover, our proposed DP-MP framework effectively tackles the challenges of bio-individual differences and noisy labels in the fatigue detection field and demonstrates superior performance. Our work provides valuable insights for future research, promoting the application of brain-computer interfaces for fatigue detection in real-world scenarios. .

RevDate: 2024-07-10

Lo YT, Jiang L, Woodington B, et al (2024)

Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation.

Journal of neural engineering [Epub ahead of print].

Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to use μECoG array to decode, in rats, body position during open field navigation, through isolated single-unit activities. Approach: μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300 to 3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns. Main results: Single-unit spikes could be extracted during chronic recording from μECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively. Significance: μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.

RevDate: 2024-07-10

Ploesser M, Abraham ME, Broekman MLD, et al (2024)

Electrical and Magnetic Neuromodulation Technologies and Brain-Computer Interfaces: Ethical Considerations for Enhancement of Brain Function in Healthy People - A Systematic Scoping Review.

Stereotactic and functional neurosurgery pii:000539757 [Epub ahead of print].

INTRODUCTION: This scoping review aimed to synthesize the fragmented evidence on ethical concerns related to the use of electrical and magnetic neuromodulation technologies, as well as brain-computer interfaces for enhancing brain function in healthy individuals, addressing the gaps in understanding spurred by rapid technological advancements and ongoing ethical debates.

METHODS: The following databases and interfaces were queried: MEDLINE (via PubMed), Web of Science, PhilPapers, and Google Scholar. Additional references were identified via bibliographies of included citations. References included experimental studies, reviews, opinion papers, and letters to editors published in peer-reviewed journals that explored the ethical implications of electrical and magnetic neuromodulation technologies and brain-computer interfaces for enhancement of brain function in healthy adult or pediatric populations.

RESULTS: A total of 23 articles were included in the review, of which the majority explored expert opinions in the form of qualitative studies or surveys as well as reviews. Two studies explored the view of laypersons on the topic. The majority of evidence pointed to ethical concerns relating to a lack of sufficient efficacy and safety data for these new technologies, with the risks of invasive procedures potentially outweighing the benefits. Additionally, concerns about potential socioeconomic consequences were raised that could further exacerbate existing socioeconomic inequalities, as well as the risk of changes to person and environment.

CONCLUSION: This scoping review highlights a critical shortage of ethical research on electrical and magnetic neuromodulation technologies and brain-computer interfaces for enhancement of brain function in healthy individuals, with key concerns regarding the safety, efficacy, and socioeconomic impacts of neuromodulation technologies. It underscores the urgent need for integrating ethical considerations into neuroscientific research to address significant gaps and ensure equitable access and outcomes.

RevDate: 2024-07-10

Jochumsen M, Poulsen KB, Sørensen SL, et al (2024)

Single-trial movement intention detection estimation in patients with Parkinson's disease: a movement-related cortical potential study.

Journal of neural engineering [Epub ahead of print].

Parkinson patients often suffer from motor impairments such as tremor and freezing of movement that can be difficult to treat. To unfreeze movement, it has been suggested to provide sensory stimuli. To avoid constant stimulation, episodes with freezing of movement needs to be detected which is a challenge. This can potentially be obtained using a brain-computer interface (BCI) based on movement-related cortical potentials (MRCPs) that are observed in association with the intention to move. The objective in this study was to detect MRCPs from single-trial EEG. Approach: Nine Parkinson patients executed 100 wrist movements and 100 ankle movements while continuous EEG and EMG were recorded. The experiment was repeated in two sessions on separate days. Using temporal, spectral and template matching features, a random forest, linear discriminant analysis, and k-nearest neighbours classifier were constructed in offline analysis to discriminate between epochs containing movement-related or idle brain activity to provide an estimation of the performance of a BCI. Three classification scenarios were tested: 1) within-session (using training and testing data from the same session and participant), between-session (using data from the same participant from session one for training and session two for testing), and across-participant (using data from all participants except one for training and testing on the remaining participant). Main results: The within-session classification scenario was associated with the highest classification accuracies which were in the range of 88-89% with a similar performance across sessions. The performance dropped to 69-75% and 70-75% for the between-session and across-participant classification scenario, respectively. The highest classification accuracies were obtained for the random forest and k-nearest neighbours classifiers. Significance: The results indicate that it is possible to detect movement intentions in individuals with Parkinson's disease such that they can operate a BCI which may control the delivery of sensory stimuli to unfreeze movement. .

RevDate: 2024-07-10

Zhang D, Li H, J Xie (2024)

Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification.

Neural networks : the official journal of the International Neural Network Society, 179:106497 pii:S0893-6080(24)00421-0 [Epub ahead of print].

The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based local class information to enhance the classification accuracy of motor imagery signals. Depending on the amount of labeled data available in the target domain, the method is implemented in both unsupervised and semi-supervised versions. Specifically, at the global level, we employ the maximum mean difference (MMD) loss to globally constrain the feature space, achieving comprehensive alignment. In the context of class-level operations, we propose two memory banks designed to accommodate class prototypes in each domain and constrain feature embeddings by applying two prototype-based contrastive losses. The source contrastive loss is used to organize source features spatially based on categories, thereby reconciling inter-class and intra-class relationships, while the interactive contrastive loss is employed to facilitate cross-domain information interaction. Simultaneously, in unsupervised scenarios, to mitigate the adverse effects of excessive pseudo-labels, we introduce an entropy-aware strategy that dynamically evaluates the confidence level of target data and personalized constraints on the participation of interactive contrastive loss. To validate our approach, extensive experiments were conducted on a highly regarded public EEG dataset, namely Dataset IIa of the BCI Competition IV, as well as a large-scale EEG dataset called GigaDB. The experiments yielded average classification accuracies of 86.03% and 84.22% respectively. These results demonstrate that our method is an effective EEG decoding model, conducive to advancing the development of motor imagery brain-computer interfaces. The architecture proposed in this study and the code for data partitioning can be found at https://github.com/zhangdx21/GPL.

RevDate: 2024-07-10

Cheng Y, Yan L, Shoukat MU, et al (2024)

An Improved SSVEP-based Brain-Computer Interface with Low Contrast Visual Stimulation and its Application in UAV Control.

Journal of neurophysiology [Epub ahead of print].

Efficient communication and regulation are crucial for advancing brain-computer interfaces (BCIs), with the steady-state visual evoked potential (SSVEP) paradigm demonstrating high accuracy and information transfer rates. However, the conventional SSVEP paradigm encounters challenges related to visual occlusion and fatigue. In this study, we propose an improved SSVEP paradigm that addresses these issues by lowering the contrast of visual stimuli. visual stimulation. The improved paradigms outperform the traditional paradigm in the experiments, significantly reducing the visual stimulation of the SSVEP paradigm. Furthermore, we apply this enhanced paradigm to a BCI navigation system, enabling two-dimensional navigation of Unmanned Aerial Vehicles (UAVs) through a first-person perspective. Experimental results indicate the enhanced SSVEP-based BCI system's accuracy in performing navigation and search tasks. Our findings highlight the feasibility of the enhanced SSVEP paradigm in mitigating visual occlusion and fatigue issues, presenting a more intuitive and natural approach for BCIs to control external equipment.

RevDate: 2024-07-10
CmpDate: 2024-07-10

Zhu F, Cai J, Zheng H, et al (2024)

Suppression of negative transfer in motor imagery brain-computer interface based on mutual information and Pearson correlation coefficient.

The Review of scientific instruments, 95(7):.

The focus of this paper is on the main challenges in brain-computer interface transfer learning: how to address data characteristic length and the source domain sample selection problems caused by individual differences. To overcome the negative migration that results from feature length, we propose a migration algorithm based on mutual information transfer (MIT), which selects effective features by calculating the entropy value of the probability distribution and conditional distribution, thereby reducing negative migration and improving learning efficiency. Source domain participants who differ too much from the target domain distribution can affect the overall classification performance. On the basis of MIT, we propose the Pearson correlation coefficient source domain automatic selection algorithm (PDAS algorithm). The PDAS algorithm can automatically select the appropriate source domain participants according to the target domain distribution, which reduces the negative migration of participant data among the source domain participants, improves experimental accuracy, and greatly reduces training time. The two proposed algorithms were tested offline and online on two public datasets, and the results were compared with those from existing advanced algorithms. The experimental results showed that the MIT algorithm and the MIT + PDAS algorithm had obvious advantages.

RevDate: 2024-07-11

Darvishi S, Datta Gupta A, Hamilton-Bruce A, et al (2024)

Enhancing poststroke hand movement recovery: Efficacy of RehabSwift, a personalized brain-computer interface system.

PNAS nexus, 3(7):pgae240.

This study explores the efficacy of our novel and personalized brain-computer interface (BCI) therapy, in enhancing hand movement recovery among stroke survivors. Stroke often results in impaired motor function, posing significant challenges in daily activities and leading to considerable societal and economic burdens. Traditional physical and occupational therapies have shown limitations in facilitating satisfactory recovery for many patients. In response, our study investigates the potential of motor imagery-based BCIs (MI-BCIs) as an alternative intervention. In this study, MI-BCIs translate imagined hand movements into actions using a combination of scalp-recorded electrical brain activity and signal processing algorithms. Our prior research on MI-BCIs, which emphasizes the benefits of proprioceptive feedback over traditional visual feedback and the importance of customizing the delay between brain activation and passive hand movement, led to the development of RehabSwift therapy. In this study, we recruited 12 chronic-stage stroke survivors to assess the effectiveness of our solution. The primary outcome measure was the Fugl-Meyer upper extremity (FMA-UE) assessment, complemented by secondary measures including the action research arm test, reaction time, unilateral neglect, spasticity, grip and pinch strength, goal attainment scale, and FMA-UE sensation. Our findings indicate a remarkable improvement in hand movement and a clinically significant reduction in poststroke arm and hand impairment following 18 sessions of neurofeedback training. The effects persisted for at least 4 weeks posttreatment. These results underscore the potential of MI-BCIs, particularly our solution, as a prospective tool in stroke rehabilitation, offering a personalized and adaptable approach to neurofeedback training.

RevDate: 2024-07-12
CmpDate: 2024-07-10

Daigle L, Khalid H, Gagnon CA, et al (2024)

High prevalence of SARS-CoV-2 antibodies and low prevalence of SARS-CoV-2 RNA in cats recently exposed to human cases.

BMC veterinary research, 20(1):304.

BACKGROUND: The primary objective of this cross-sectional study, conducted in Québec and Bristish Columbia (Canada) between February 2021 and January 2022, was to measure the prevalence of viral RNA in oronasal and rectal swabs and serum antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) amongst cats living in households with at least one confirmed human case. Secondary objectives included a description of potential risk factors for the presence of SARS-CoV-2 antibodies and an estimation of the association between the presence of viral RNA in swabs as well as SARS-CoV-2 antibodies and clinical signs. Oronasal and rectal swabs and sera were collected from 55 cats from 40 households at most 15 days after a human case confirmation, and at up to two follow-up visits. A RT-qPCR assay and an ELISA were used to detect SARS-CoV-2 RNA in swabs and serum SARS-CoV-2 IgG antibodies, respectively. Prevalence and 95% Bayesian credibility intervals (BCI) were calculated, and associations were evaluated using prevalence ratio and 95% BCI obtained from Bayesian mixed log-binomial models.

RESULTS: Nine (0.16; 95% BCI = 0.08-0.28) and 38 (0.69; 95% BCI = 0.56-0.80) cats had at least one positive RT-qPCR and at least one positive serological test result, respectively. No risk factor was associated with the prevalence of SARS-CoV-2 serum antibodies. The prevalence of clinical signs suggestive of COVID-19 in cats, mainly sneezing, was 2.12 (95% BCI = 1.03-3.98) times higher amongst cats with detectable viral RNA compared to those without.

CONCLUSIONS: We showed that cats develop antibodies to SARS-CoV-2 when exposed to recent human cases, but detection of viral RNA on swabs is rare, even when sampling occurs soon after confirmation of a human case. Moreover, cats with detectable levels of virus showed clinical signs more often than cats without signs, which can be useful for the management of such cases.

RevDate: 2024-07-11
CmpDate: 2024-07-11

Mejia LA (2024)

Real control in virtual rats.

Nature neuroscience, 27(7):1214.

RevDate: 2024-07-09

Li R, Bai D, Li Z, et al (2024)

The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the existing SSVEP brain control methods cannot adapt to dynamic or unstructured environments. Moreover, the recognition accuracy from the conventional decoding algorithm still needs to improve. To address the above problems, this study proposed a steady-state hybrid visual evoked potentials (SSHVEP) paradigm using the grasping targets in their environment to improve the connection between the subjects' and their dynamic environments. Moreover, a novel EEG decoding method, using the multivariate variational mode decomposition (MVMD) algorithm for adaptive sub-band division and convolutional neural network (CNN) for target recognition, was applied to improve the decoding accuracy of the SSHVEPs. 18 subjects participated in the offline and online experiments. The offline accuracy across 18 subjects by the 9-target SSHVEP paradigm was up to 95.41 ± 2.70%, which is a 5.80% improvement compared to the conventional algorithm. To further validate the performance of the proposed method, the brain-controlled grasping robot system using the SSHVEP paradigm was built. The average accuracy reached 93.21 ± 10.18% for the online experiment. All the experimental results demonstrated the effectiveness of the brain computer interaction method based on the SSHVEP paradigm and the MVMD combined CNN algorithm studied in this paper.

RevDate: 2024-07-15
CmpDate: 2024-07-15

Bottosso M, Miglietta F, Vernaci GM, et al (2024)

Gene Expression Assays to Tailor Adjuvant Endocrine Therapy for HR+/HER2- Breast Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research, 30(14):2884-2894.

Adjuvant endocrine therapy (ET) represents the standard of care for almost all hormone receptor (HR)+/HER2- breast cancers, and different agents and durations are currently available. In this context, the tailoring and optimization of adjuvant endocrine treatment by reducing unnecessary toxic treatment while taking into account the biological heterogeneity of HR+/HER2- breast cancer represents a clinical priority. There is therefore a significant need for the integration of biological biomarkers in the choice of adjuvant ET beyond currently used clinicopathological characteristics. Several gene expression assays have been developed to identify patients with HR+/HER2- breast cancer who will not derive benefit from the addition of adjuvant chemotherapy. By enhancing risk stratification and predicting therapeutic response, genomic assays have also shown to be a promising tool for optimizing endocrine treatment decisions. In this study, we review evidence supporting the use of most common commercially available gene expression assays [Oncotype DX, MammaPrint, Breast Cancer Index (BCI), Prosigna, and EndoPredict] in tailoring adjuvant ET. Available data on the use of genomic tests to inform extended adjuvant treatment choice based on the risk of late relapse and on the estimated benefit of a prolonged ET are discussed. Moreover, preliminary evidence regarding the use of genomic assays to inform de-escalation of endocrine treatment, such as shorter durations or omission, for low-risk patients is reviewed. Overall, gene expression assays are emerging as potential tools to further personalize adjuvant treatment for patients with HR+/HER2- breast cancers.

RevDate: 2024-07-09

Pan G, Zhao B, Zhang M, et al (2024)

Nucleus Accumbens Corticotropin-Releasing Hormone Neurons Projecting to the Bed Nucleus of the Stria Terminalis Promote Wakefulness and Positive Affective State.

Neuroscience bulletin [Epub ahead of print].

The nucleus accumbens (NAc) plays an important role in various emotional and motivational behaviors that rely on heightened wakefulness. However, the neural mechanisms underlying the relationship between arousal and emotion regulation in NAc remain unclear. Here, we investigated the roles of a specific subset of inhibitory corticotropin-releasing hormone neurons in the NAc (NAc[CRH]) in regulating arousal and emotional behaviors in mice. We found an increased activity of NAc[CRH] neurons during wakefulness and rewarding stimulation. Activation of NAc[CRH] neurons converts NREM or REM sleep to wakefulness, while inhibition of these neurons attenuates wakefulness. Remarkably, activation of NAc[CRH] neurons induces a place preference response (PPR) and decreased basal anxiety level, whereas their inactivation induces a place aversion response and anxious state. NAc[CRH] neurons are identified as the major NAc projection neurons to the bed nucleus of the stria terminalis (BNST). Furthermore, activation of the NAc[CRH]-BNST pathway similarly induced wakefulness and positive emotional behaviors. Taken together, we identified a basal forebrain CRH pathway that promotes the arousal associated with positive affective states.

RevDate: 2024-07-08

Ha J, Park MK, Park SN, et al (2024)

Tinnitus reduction after active bone-conduction implantation in patients with single-sided deafness: a prospective multicenter study.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery [Epub ahead of print].

PURPOSE: Single-sided deafness (SSD) presents significant challenges for patients, including compromised sound localization, reduced speech recognition, and often, tinnitus. These issues are typically addressed using interventions such as cochlear implantation (CI) and bone conduction implant (BCI). However, evidence regarding the efficacy of BCI in reducing tinnitus in SSD patients remains limited. This study explored the ability of a novel active transcutaneous BCI (Bonebridge BCI602) to alleviate tinnitus in SSD patients.

STUDY DESIGN: Prospective cohort multicenter study.

SETTING: Tertiary referral hospitals.

METHODS: A prospective multicenter study of 30 SSD patients was conducted. The patients were divided into two groups: those with (n = 19) and without (n = 11) tinnitus. Audiometric assessments, subjective questionnaires including the Abbreviated Profile of Hearing Aid Benefit (APHAB) and the Bern Benefit in Single-Sided Deafness (BBSS), and tinnitus evaluations with the Tinnitus Handicap Inventory (THI) and tinnitogram were conducted before and after BCI surgery.

RESULTS: THI scores after surgery were significantly reduced in SSD patients with tinnitus. Subjective satisfaction improved in both the tinnitus and non-tinnitus groups; however, the former group exhibited a significantly greater improvement in the APHAB questionnaire score. According to tinnitograms, the loudness of tinnitus decreased, particularly in patients with ipsilateral tinnitus. Patients with residual hearing had greater reductions in their THI scores. However, three patients without residual hearing had a relative worsening of tinnitus after surgery.

CONCLUSION: The Bonebridge BCI602 effectively reduced tinnitus in SSD patients, particularly in those with residual hearing. Subjective satisfaction improved in both the tinnitus and non-tinnitus groups. These findings demonstrate the therapeutic potential of BCI for managing SSD and associated tinnitus.

RevDate: 2024-07-08

M AL, R R (2024)

Rehabilitation Based on BCI: An Innovative Enhancement for Sensorimotor Cortex Rhythms Systemization.

Advanced biology [Epub ahead of print].

The research proposes a novel strategy for categorizing electroencephalograms (EEG) in real-time brain-computer interfaces that have rehabilitation applications. The methodology utilizes Five Cross-Common Spatial Patterns (FCCSP) to develop a motor movement/imagery systemization model that extracts multi-domain characteristics with excellent performance. The goal is to eliminate the impact caused by EEG's nonstationarity. The article highlights the findings of a real-time technique that is incorporated into a comprehensive prediction system, and it offers an innovative method to boost accuracy in real-time Sensory-Motor cortex Rhythms (SMR). The accuracy increased from 57.14% using raw EEG to 85.71% after preprocessing, and from 58.08% to 97.94% in public domain SMR. The proposed Butterworth bandpass filter is optimized using the FCCSP to determine the ideal bandwidth that incorporates the whole EEG features in beta waves. The Hybrid Systemization of the Correlated Feature Removal classifier is then integrated with the FCCSP method to create improved predictive models. As a consequence, while applied to real-time and PhysioNet datasets, the outcome system achieved outstanding accuracy values of 85.71% and 97.94%, respectively. This demonstrates the robustness of the strategy to increase SMR prediction efficiency.

RevDate: 2024-07-08

Zhu H, Beierholm U, L Shams (2024)

BCI Toolbox: An open-source python package for the Bayesian causal inference model.

PLoS computational biology, 20(7):e1011791 pii:PCOMPBIOL-D-24-00004 [Epub ahead of print].

Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual and sensorimotor processes in humans. Therefore, we introduce the BCI Toolbox, a statistical and analytical tool in Python, enabling researchers to conveniently perform quantitative modeling and analysis of behavioral data. Additionally, we describe the algorithm of the BCI model and test its stability and reliability via parameter recovery. The present BCI toolbox offers a robust platform for BCI model implementation as well as a hands-on tool for learning and understanding the model, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.

RevDate: 2024-07-12
CmpDate: 2024-07-11

Wang Z, Shen L, Yang Y, et al (2024)

A Least-Square Unified Framework for Spatial Filtering in SSVEP-Based BCIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 32:2470-2481.

The steady-state visual evoked potential (SSVEP) has become one of the most prominent BCI paradigms with high information transfer rate, and has been widely applied in rehabilitation and assistive applications. This paper proposes a least-square (LS) unified framework to summarize the correlation analysis (CA)-based SSVEP spatial filtering methods from a machine learning perspective. Within this framework, the commonalities and differences between various spatial filtering methods appear apparent, the interpretation of computational factors becomes intuitive, and spatial filters can be determined by solving a generalized optimization problem with non-linear and regularization items. Moreover, the proposed LS framework provides the foundation of utilizing the knowledge behind these spatial filtering methods in further classification/regression model designs. Through a comparative analysis of existing representative spatial filtering methods, recommendations are made for the superior and robust design strategies. These recommended strategies are further integrated to fill the research gaps and demonstrate the ability of the proposed LS framework to promote algorithmic improvements, resulting in five new spatial filtering methods. This study could offer significant insights in understanding the relationships between various design strategies in the spatial filtering methods from the machine learning perspective, and would also contribute to the development of the SSVEP recognition methods with high performance.

RevDate: 2024-07-09

Kalani M, A Anjankar (2024)

Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment.

Cureus, 16(6):e61706.

Artificial intelligence (AI) has emerged as a powerful tool in the field of neurology, significantly impacting the diagnosis and treatment of neurological disorders. Recent technological breakthroughs have given us access to a plethora of information relevant to many aspects of neurology. Neuroscience and AI share a long history of collaboration. Along with great potential, we encounter obstacles relating to data quality, ethics, and inherent difficulty in applying data science in healthcare. Neurological disorders pose intricate challenges due to their complex manifestations and variability. Automating image interpretation tasks, AI algorithms accurately identify brain structures and detect abnormalities. This accelerates diagnosis and reduces the workload on medical professionals. Treatment optimization benefits from AI simulations that model different scenarios and predict outcomes. These AI systems can currently perform many of the sophisticated perceptual and cognitive capacities of biological systems, such as object identification and decision making. Furthermore, AI is rapidly being used as a tool in neuroscience research, altering our understanding of brain functioning. It has the ability to revolutionize healthcare as we know it into a system in which humans and robots collaborate to deliver better care for our patients. Image analysis activities such as recognizing particular brain regions, calculating changes in brain volume over time, and detecting abnormalities in brain scans can be automated by AI systems. This lessens the strain on radiologists and neurologists while improving diagnostic accuracy and efficiency. It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings. In conclusion, AI's integration into neurology has revolutionized diagnosis, treatment, and research. As AI technologies advance, they promise to unravel the complexities of neurological disorders further, leading to improved patient care and quality of life. The symbiosis of AI and neurology offers a glimpse into a future where innovation and compassion converge to reshape neurological healthcare. This abstract provides a concise overview of the role of AI in neurology and its transformative potential.

RevDate: 2024-07-09

Li Y, Nie Y, Quan Z, et al (2024)

Brain-machine interactive neuromodulation research tool with edge AI computing.

Heliyon, 10(12):e32609.

Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.

RevDate: 2024-07-09

Liu Y, Gao X, Zhang Y, et al (2024)

Geographical variation in dementia prevalence across China: a geospatial analysis.

The Lancet regional health. Western Pacific, 47:101117.

BACKGROUND: Dementia poses great health and social challenges in China. Dementia prevalence may vary across geographic areas, while comparable estimations on provincial level is lacking. This study aims to estimate dementia prevalence by provinces across China, taking into account risk factors of individual level and potential spatial correlation of provinces.

METHODS: In this study, 17,176 adults aged 50 years or older were included from the fourth wave of the China Health and Retirement Longitudinal Study (CHARLS 2018), covering 28 provinces, autonomous regions and municipalities. To improve provincial representativeness, we constructed provincial survey weights based on China 7th census (2020). The prevalence of dementia and 95% Bayesian credible intervals (BCIs) were estimated using a Bayesian conditional autoregressive (CAR) model with spatially varying coefficients of covariates.

FINDINGS: The weighted prevalence of dementia at provincial level in China in 2018 ranged from 2.62% (95%BCI: 1.70%, 3.91%) to 13.53% (95%BCI: 8.82%, 20.93%). High dementia prevalence was concentrated in North China, with a prominent high-high cluster, while provinces of low prevalence were concentrated on East and South China, characterized by a low-low cluster. Ordered by the median estimation of prevalence, the top 10% of provinces, include Xinjiang, Jilin, and Beijing. Meanwhile, Fujian, Zhejiang, and Guangdong rank among the last. The association between dementia prevalence and drinking, smoking, social isolation, physical inactivity, hearing impairment, hypertension, and diabetes exhibits provincial variation.

INTERPRETATION: Our study identifies a geospatial disparity in dementia prevalence and risk factor effects across China's provinces, with high-high and low-low clusters in some northern and southern provinces, respectively. The findings emphasize the need for targeted strategies, such as addressing hypertension and hearing impairment, in specific regions for more effective dementia prevention and treatment.

FUNDING: National Science Foundation of China/the Economic and Social Research Council, UK Research and Innovation joint call: Understanding and Addressing Health and Social Challenges for Ageing in the UK and China. UK-China Health And Social Challenges Ageing Project (UKCHASCAP): present and future burden of dementia, and policy responses (grant number 72061137003, ES/T014377/1).

RevDate: 2024-07-09

Zhang J, Zhang Y, Zhang X, et al (2024)

A high-performance general computer cursor control scheme based on a hybrid BCI combining motor imagery and eye-tracking.

iScience, 27(6):110164.

This study introduces a novel virtual cursor control system designed to empower individuals with neuromuscular disabilities in the digital world. By combining eye-tracking with motor imagery (MI) in a hybrid brain-computer interface (BCI), the system enhances cursor control accuracy and simplicity. Real-time classification accuracy reaches 87.92% (peak of 93.33%), with cursor stability in the gazing state at 96.1%. Integrated into common operating systems, it enables tasks like text entry, online chatting, email, web surfing, and picture dragging, with an average text input rate of 53.2 characters per minute (CPM). This technology facilitates fundamental computing tasks for patients, fostering their integration into the online community and paving the way for future developments in BCI systems.

RevDate: 2024-07-08
CmpDate: 2024-07-08

Zou X, Chen B, Y Li (2024)

[Research status and progress of bilateral cochlear implantation].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology, head, and neck surgery, 38(7):666-670.

With the development of social economic and technology, Cochlear Implantation has became an effective therapy for patients who suffered from severe or profound hearing impairment. In the meantime, patients' demands for sound and auditory quality are also increasing. In terms of speech recognition, localization, and auditory quality, bilateral hearing is closer to the auditory experience of normal individuals, so bilateral cochlear implantation(BCI) emerged as the times require. In this article, we will introduce the status and progress of bimodal regarding to the following aspects: the brief history, the advantages of BCI, different methods for BCI, and the problems encountered in BCI.

RevDate: 2024-07-06

Casasanta N, Patel R, Raymond S, et al (2024)

Correlating Predicted Adjuvant Therapy Benefit and Risk of Recurrence Between Breast Cancer Index (BCI) and the 21-Gene Oncotype DX Recurrence Score (RS).

Clinical breast cancer pii:S1526-8209(24)00167-8 [Epub ahead of print].

INTRODUCTION: Breast Cancer Index (BCI) is a genomic assay that evaluates the benefit of extending endocrine therapy (ET) from 5 to 10 years and predicts recurrence risk (RR). We evaluated the association between BCI and Oncotype DX (ODX).

PATIENTS: Women with hormone receptor (HR)-positive early-stage breast cancer (EBC) who had BCI and ODX performed were included.

METHODS: We performed a retrospective review of women with HR-positive EBC. BCI was categorized as predictive of extended ET versus not and ODX recurrence score (RS) as low (0-10), intermediate (11-25), and high (26-100). Univariate and multivariable logistic and linear regression models assessed the relationship between BCI and ODX, factors associated with each, and discordance between scores.

RESULTS: We identified 153 women, 22% were premenopausal and 18% were lymph node positive. The univariate logistic and linear models revealed an association between BCI predictive score and ODX RS (OR 7.84, CI, 2.63-23.36, P < .001) and log of BCI RR (Beta 0.04, CI, 0.02-0.06, P < .001). Seventy-four percent of BCI predictive scores were concordant with ODX RS and 83% of BCI RR was concordant with ODX RR. In a univariate logistic regression model, BCI predictive of ET benefit was associated with discordance (OR 28.00, CI, 10.58-74.02, P < .001). Higher ODX RR was associated with discordance (OR 1.92, CI, 1.42-2.59, P < .001).

CONCLUSION: We found a significant association between ODX and BCI predictive and prognostic scores. BCI predictive of extended ET benefit was associated with discordance with ODX RS. Higher predicted RR on ODX was associated with discordance with BCI predicted RR.

RevDate: 2024-07-08
CmpDate: 2024-07-05

de Borman A, Wittevrongel B, Dauwe I, et al (2024)

Imagined speech event detection from electrocorticography and its transfer between speech modes and subjects.

Communications biology, 7(1):818.

Speech brain-computer interfaces aim to support communication-impaired patients by translating neural signals into speech. While impressive progress was achieved in decoding performed, perceived and attempted speech, imagined speech remains elusive, mainly due to the absence of behavioral output. Nevertheless, imagined speech is advantageous since it does not depend on any articulator movements that might become impaired or even lost throughout the stages of a neurodegenerative disease. In this study, we analyzed electrocortigraphy data recorded from 16 participants in response to 3 speech modes: performed, perceived (listening), and imagined speech. We used a linear model to detect speech events and examined the contributions of each frequency band, from delta to high gamma, given the speech mode and electrode location. For imagined speech detection, we observed a strong contribution of gamma bands in the motor cortex, whereas lower frequencies were more prominent in the temporal lobe, in particular of the left hemisphere. Based on the similarities in frequency patterns, we were able to transfer models between speech modes and participants with similar electrode locations.

RevDate: 2024-07-05

Kitta T, Wada N, Shinohara S, et al (2024)

Validation of the area under the Watts factor curve during the voiding cycle as a novel parameter for diagnosing detrusor underactivity in females.

International journal of urology : official journal of the Japanese Urological Association [Epub ahead of print].

OBJECTIVE: Detrusor underactivity (DU) is a common cause of lower urinary tract symptoms (LUTS). To date, no consensus has been reached on the urodynamic criteria for defining DU. We previously proposed the area under the curve of the Watts factor (WF-AUC) as a new parameter for diagnosing DU. By comparing previously reported five criteria for DU and WF-AUC, we analyzed whether the WF-AUC could assess detrusor contraction in women with LUTS.

METHODS: Using urodynamic data of consecutive 77 women with LUTS, first, we classified DU based on previously reported five criteria. Second, we assessed the potential correlation between multiple parameters and WF-AUC. Third, receiver operating characteristic curve analysis was performed to determine the cutoff value of WF-AUC for diagnosing DU based on previously reported five criteria. Fourth, a linear regression analysis was conducted and compared using multiple criteria and female bladder outlet obstruction index (BOOIf).

RESULTS: WF-AUC was positively correlated with the maximum values of WF, bladder contractility index (BCI), and projected isovolumetric pressure 1 (PIP1) with correlation coefficients of 0.63, 0.57, and 0.34, respectively. AUC for diagnosing DU based on previously reported five criteria ranging from 0.773 to 0.896 with different cutoff values of AUC-WF. The Spearman's correlation test revealed that BOOIf was significantly correlated with BCI, but not Wmax, PIP1 and WF-AUC.

CONCLUSIONS: This study demonstrated the non-inferiority of the WF-AUC compared to previously reported criteria for defining DU. Depending on the cutoff value, the WF-AUC could appropriately evaluate women with DU, regardless of the presence of BOO.

RevDate: 2024-07-05

Kumari A, Edla DR, Reddy RR, et al (2024)

EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning.

Journal of neuroscience methods pii:S0165-0270(24)00160-2 [Epub ahead of print].

Brain-computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model's overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments.

RevDate: 2024-07-05

Wu H, Xie Q, Yu Z, et al (2024)

Unsupervised heterogeneous domain adaptation for EEG classification.

Journal of neural engineering [Epub ahead of print].

$Objective.$ Domain adaptation has been recognized as a potent solution to the challenge of limited training data for electroencephalography (EEG) classification tasks. Existing studies primarily focus on homogeneous environments, however, the heterogeneous properties of EEG data arising from device diversity cannot be overlooked. This motivates the development of heterogeneous domain adaptation methods that can fully exploit the knowledge from an auxiliary heterogeneous domain for EEG classification. $Approach.$ In this article, we propose a novel model named Informative Representation Fusion (IRF) to tackle the problem of unsupervised heterogeneous domain adaptation in the context of EEG data. In IRF, we consider different perspectives of data, i.e., independent identically distributed (iid) and non-iid, to learn different representations. Specifically, from the non-iid perspective, IRF models high-order correlations among data by hypergraphs and develops hypergraph encoders to obtain data representations of each domain. From the non-iid perspective, by applying multi-layer perceptron networks to the source and target domain data, we achieve another type of representation for both domains. Subsequently, an attention mechanism is used to fuse these two types of representations to yield informative features. To learn transferable representations, the Maximum Mean Discrepancy is utilized to align the distributions of the source and target domains based on the fused features. $Main~results.$ Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model. $Significance.$ This article handles an EEG classification situation where the source and target EEG data lie in different spaces, and what's more, under an unsupervised learning setting. This situation is practical in the real world but barely studied in the literature. The proposed model achieves high classification accuracy, and this study is important for the commercial applications of EEG-based BCIs.

RevDate: 2024-07-06

Qi G, Liu R, Guan W, et al (2024)

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network.

Cyborg and bionic systems (Washington, D.C.), 5:0130.

In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain-computer interface in autonomous driving.

RevDate: 2024-07-06

Sun J, C Li (2024)

Editorial: Advanced neurotechnology in stroke rehabilitation.

Frontiers in neurology, 15:1440752.

RevDate: 2024-07-07
CmpDate: 2024-07-04

Xu M, Zhou W, Shen X, et al (2024)

Temporal-spatial cross attention network for recognizing imagined characters.

Scientific reports, 14(1):15432.

Previous research has primarily employed deep learning models such as Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined character signals. These approaches have treated the temporal and spatial features of the signals in a sequential, parallel, or single-feature manner. However, there has been limited research on the cross-relationships between temporal and spatial features, despite the inherent association between channels and sampling points in Brain-Computer Interface (BCI) signal acquisition, which holds significant information about brain activity. To address the limited research on the relationships between temporal and spatial features, we proposed a Temporal-Spatial Cross-Attention Network model, named TSCA-Net. The TSCA-Net is comprised of four modules: the Temporal Feature (TF), the Spatial Feature (SF), the Temporal-Spatial Cross (TSCross), and the Classifier. The TF combines LSTM and Transformer to extract temporal features from BCI signals, while the SF captures spatial features. The TSCross is introduced to learn the correlations between the temporal and spatial features. The Classifier predicts the label of BCI data based on its characteristics. We validated the TSCA-Net model using publicly available datasets of handwritten characters, which recorded the spiking activity from two micro-electrode arrays (MEAs). The results showed that our proposed TSCA-Net outperformed other comparison models (EEG-Net, EEG-TCNet, S3T, GRU, LSTM, R-Transformer, and ViT) in terms of accuracy, precision, recall, and F1 score, achieving 92.66 % , 92.77 % , 92.70 % , and 92.58 % , respectively. The TSCA-Net model demonstrated a 3.65 % to 7.49 % improvement in accuracy over the comparison models.

RevDate: 2024-07-04

Tian Y, Zheng J, Zhu X, et al (2024)

A prefrontal-habenular circuitry regulates social fear behaviour.

Brain : a journal of neurology pii:7706043 [Epub ahead of print].

The medial prefrontal cortex (mPFC) has been implicated in the pathophysiology of social impairments including social fear. However, the precise subcortical partners that mediate mPFC dysfunction on social fear behaviour have not been identified. Employing a social fear conditioning paradigm, we induced robust social fear in mice and found that the lateral habenula (LHb) neurons and LHb-projecting mPFC neurons are synchronously activated during social fear expression. Moreover, optogenetic inhibition of the mPFC-LHb projection significantly reduced social fear responses. Importantly, consistent with animal studies, we observed an elevated prefrontal-habenular functional connectivity in subclinical individuals with higher social anxiety characterized by heightened social fear. These results unravel a crucial role of the prefrontal-habenular circuitry in social fear regulation and suggest that this pathway could serve as a potential target for the treatment of social fear symptom often observed in many psychiatric disorders.

RevDate: 2024-07-06

Kukkar KK, Rao N, Huynh D, et al (2024)

Context-dependent reduction in corticomuscular coupling for balance control in chronic stroke survivors.

Experimental brain research [Epub ahead of print].

Balance control is an important indicator of mobility and independence in activities of daily living. How the functional coupling between the cortex and the muscle for balance control is affected following stroke remains to be known. We investigated the changes in coupling between the cortex and leg muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Fourteen participants with stroke and ten healthy controls performed a challenging balance task. They stood on a computerized support surface that was either fixed (low difficulty condition) or sway-referenced with varying gain (medium and high difficulty conditions). We computed corticomuscular coherence between electrodes placed over the sensorimotor area (electroencephalography) and leg muscles (electromyography) and assessed balance performance using clinical and laboratory-based tests. We found significantly lower delta frequency band coherence in stroke participants when compared with healthy controls under medium difficulty condition, but not during low and high difficulty conditions. These differences were found for most of the distal but not for proximal leg muscle groups. No differences were found at other frequency bands. Participants with stroke showed poor balance clinical scores when compared with healthy controls, but no differences were found for laboratory-based tests. The observation of effects at distal but not at proximal muscle groups suggests differences in the (re)organization of the descending connections across two muscle groups for balance control. We argue that the observed group difference in delta band coherence indicates balance context-dependent alteration in mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support surface for balance maintenance following stroke.

RevDate: 2024-07-04

Martinez-Peon D, Garcia-Hernandez NV, Benavides-Bravo FG, et al (2024)

Characterization and classification of kinesthetic motor imagery levels.

Journal of neural engineering [Epub ahead of print].

Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals. Approach. A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0, 10, 40, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbours (kNN). Main results. A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN. Significance. Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

RevDate: 2024-07-05

Singh AK, L Bianchi (2024)

Encoding temporal information in deep convolution neural network.

Frontiers in neuroergonomics, 5:1287794.

A recent development in deep learning techniques has attracted attention to the decoding and classification of electroencephalogram (EEG) signals. Despite several efforts to utilize different features in EEG signals, a significant research challenge is using time-dependent features in combination with local and global features. Several attempts have been made to remodel the deep learning convolution neural networks (CNNs) to capture time-dependency information. These features are usually either handcrafted features, such as power ratios, or splitting data into smaller-sized windows related to specific properties, such as a peak at 300 ms. However, these approaches partially solve the problem but simultaneously hinder CNNs' capability to learn from unknown information that might be present in the data. Other approaches, like recurrent neural networks, are very suitable for learning time-dependent information from EEG signals in the presence of unrelated sequential data. To solve this, we have proposed an encoding kernel (EnK), a novel time-encoding approach, which uniquely introduces time decomposition information during the vertical convolution operation in CNNs. The encoded information lets CNNs learn time-dependent features in addition to local and global features. We performed extensive experiments on several EEG data sets-physical human-robot collaborations, P300 visual-evoked potentials, motor imagery, movement-related cortical potentials, and the Dataset for Emotion Analysis Using Physiological Signals. The EnK outperforms the state of the art with an up to 6.5% reduction in mean squared error (MSE) and a 9.5% improvement in F1-scores compared to the average for all data sets together compared to base models. These results support our approach and show a high potential to improve the performance of physiological and non-physiological data. Moreover, the EnK can be applied to virtually any deep learning architecture with minimal effort.

RevDate: 2024-07-05

Khazaei S, Parshi S, Alam S, et al (2024)

A multimodal dataset for investigating working memory in presence of music: a pilot study.

Frontiers in neuroscience, 18:1406814.

INTRODUCTION: Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the working memory experiment in the presence of personalized musical stimuli.

METHODS: Participants perform a working memory experiment called the n-back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session.

RESULTS: A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session.

DISCUSSION: Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassing multiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation.

RevDate: 2024-07-04

Sands I, Demarco R, Thurber L, et al (2024)

Interface-Mediated Neurogenic Signaling: The Impact of Surface Geometry and Chemistry on Neural Cell Behavior for Regenerative and Brain-Machine Interfacing Applications.

Advanced materials (Deerfield Beach, Fla.) [Epub ahead of print].

Nanomaterial advancements have driven progress in central and peripheral nervous system applications such as tissue regeneration and brain-machine interfacing. Ideally, neural interfaces with native tissue shall seamlessly integrate, a process that is often mediated by the interfacial material properties. Surface topography and material chemistry are significant extracellular stimuli that can influence neural cell behavior to facilitate tissue integration and augment therapeutic outcomes. This review characterizes topographical modifications, including micropillars, microchannels, surface roughness, and porosity, implemented on regenerative scaffolding and brain-machine interfaces. Their impact on neural cell response is summarized through neurogenic outcome and mechanistic analysis. The effects of surface chemistry on neural cell signaling with common interfacing compounds like carbon-based nanomaterials, conductive polymers, and biologically inspired matrices are also reviewed. Finally, the impact of these extracellular mediated neural cues on intracellular signaling cascades is discussed to provide perspective on the manipulation of neuron and neuroglia cell microenvironments to drive therapeutic outcomes.

RevDate: 2024-07-03

Van Den Kerchove A, Si-Mohammed H, Van Hulle MM, et al (2024)

Correcting for ERP latency jitter improves gaze-independent BCI decoding.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Patients suffering from heavy paralysis or Locked-in-Syndrome can regain communication using a Brain-Computer Interface (BCI). Visual event-related potential (ERP) based BCI paradigms exploit visuospatial attention (VSA) to targets laid out on a screen. However, performance drops if the user does not direct their eye gaze at the intended target, harming the utility of this class of BCIs for patients suffering from eye motor deficits. We aim to create an ERP decoder that is less dependent on eye gaze.

METHODS: ERP component latency jitter plays a role in covert visuospatial attention (VSA) decoding. We introduce a novel decoder which compensates for these latency effects, termed Woody Classifier-based Latency Estimation (WCBLE). We carried out a BCI experiment recording ERP data in overt and covert visuospatial attention (VSA), and introduce a novel special case of covert VSA termed split VSA, simulating the experience of patients with severely impaired eye motor control. We evaluate WCBLE on this dataset and the BNCI2014-009 dataset, within and across VSA conditions to study the dependency on eye gaze and the variation thereof during the experiment.

RESULTS & DISCUSSION: WCBLE outperforms state-of-the-art methods in the VSA conditions of interest in gaze-independent decoding, without reducing overt VSA performance. Results from across-condition evaluation show that WCBLE is more robust to varying VSA conditions throughout a BCI operation session. Together, these results point towards a pathway to achieving gaze independence through suited ERP decoding. Our proposed gaze-independent solution enhances decoding performance in those cases where performing overt VSA is not possible.

RevDate: 2024-07-04

Eidel M, Pfeiffer M, Ziebell P, et al (2024)

Recording the tactile P300 with the cEEGrid for potential use in a brain-computer interface.

Frontiers in human neuroscience, 18:1371631.

Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because electroencephalography (EEG), a prevalent method to acquire brain activity for BCI operation, is considered too impractical to be applied in daily life of end-users with physical impairment as an assistive device. Hence, miniaturized EEG systems such as the cEEGrid have been developed. While they promise to be a step toward bridging the gap between BCI development, lab demonstrations, and home use, they still require further validation. Encouragingly, the cEEGrid has already demonstrated its ability to record visually and auditorily evoked event-related potentials (ERP), which are important as input signal for many BCIs. With this study, we aimed at evaluating the cEEGrid in the context of a BCI based on tactually evoked ERPs. To compare the cEEGrid with a conventional scalp EEG, we recorded brain activity with both systems simultaneously. Forty healthy participants were recruited to perform a P300 oddball task based on vibrotactile stimulation at four different positions. This tactile paradigm has been shown to be feasible for BCI repeatedly but has never been tested with the cEEGrid. We found distinct P300 deflections in the cEEGrid data, particularly at vertical bipolar channels. With an average of 63%, the cEEGrid classification accuracy was significantly above the chance level (25%) but significantly lower than the 81% reached with the EEG cap. Likewise, the P300 amplitude was significantly lower (cEEGrid R2-R7: 1.87 μV, Cap Cz: 3.53 μV). These results indicate that a tactile BCI using the cEEGrid could potentially be operated, albeit with lower efficiency. Additionally, participants' somatosensory sensitivity was assessed, but no correlation to the accuracy of either EEG system was shown. Our research contributes to the growing amount of literature comparing the cEEGrid to conventional EEG systems and provides first evidence that the tactile P300 can be recorded behind the ear. A BCI based on a thus simplified EEG system might be more readily accepted by potential end-users, provided the accuracy can be substantially increased, e.g., by training and improved classification.

RevDate: 2024-07-05
CmpDate: 2024-07-03

Xu T, Ji Z, Xu X, et al (2024)

Filter bank temporally local multivariate synchronization index for SSVEP-based BCI.

BMC bioinformatics, 25(1):227.

BACKGROUND: Multivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components.

RESULTS: We employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively.

CONCLUSIONS: The promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.

RevDate: 2024-07-02

Huang Q, Ding J, X Wang (2024)

A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.

Neuroscience bulletin [Epub ahead of print].

Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural networks (CNNs) effectively extract these features but face limitations like overfitting due to small datasets. To address this issue, we propose a lightweight CNN and assess its interpretability through the fully connected layer (FCL). Initially tested with two tasks (Task 1: open vs closed eyes, Task 2: interictal vs ictal stage), the CNN demonstrated enhanced spectral features in the alpha band for Task 1 and the theta band for Task 2, aligning with established neurophysiological characteristics. Subsequent experiments on two brain-computer interface tasks revealed a correlation between delta activity (around 1.55 Hz) and hand movement, with consistent results across pericentral electroencephalogram (EEG) channels. Compared to recent research, our method stands out by delivering task-related spectral features through FCL, resulting in significantly fewer trainable parameters while maintaining comparable interpretability. This indicates its potential suitability for a wider array of EEG decoding scenarios.

RevDate: 2024-07-02

Köhler RM, Binns TS, Merk T, et al (2024)

Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in Parkinson's disease.

Brain : a journal of neurology pii:7703258 [Epub ahead of print].

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography (ECoG) and subthalamic local field potential (LFP) recordings were performed OFF therapy (N = 22), ON dopaminergic medication (N = 18) and ON subthalamic deep brain stimulation (N = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography (EMG). In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

RevDate: 2024-07-03

Mulpuri RP, Konda N, Gadde ST, et al (2024)

Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review.

Cureus, 16(5):e61400.

Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing on AI/ML in neuroregeneration were selected from a total of 247. Two researchers independently conducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT) 2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals included diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronic health records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard ML algorithms constituted 29%. The review underscores the growing interest in AI/ML for neuroregenerative medicine, with increasing publications. These technologies aid in diagnosing diseases and facilitating functional recovery through robotics and targeted stimulation. AI-driven drug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressing existing limitations remains crucial in this rapidly evolving field.

RevDate: 2024-07-03

Vaccari FE, Diomedi S, De Vitis M, et al (2024)

Similar neural states, but dissimilar decoding patterns for motor control in parietal cortex.

Network neuroscience (Cambridge, Mass.), 8(2):486-516.

Discrete neural states are associated with reaching movements across the fronto-parietal network. Here, the Hidden Markov Model (HMM) applied to spiking activity of the somato-motor parietal area PE revealed a sequence of states similar to those of the contiguous visuomotor areas PEc and V6A. Using a coupled clustering and decoding approach, we proved that these neural states carried spatiotemporal information regarding behaviour in all three posterior parietal areas. However, comparing decoding accuracy, PE was less informative than V6A and PEc. In addition, V6A outperformed PEc in target inference, indicating functional differences among the parietal areas. To check the consistency of these differences, we used both a supervised and an unsupervised variant of the HMM, and compared its performance with two more common classifiers, Support Vector Machine and Long-Short Term Memory. The differences in decoding between areas were invariant to the algorithm used, still showing the dissimilarities found with HMM, thus indicating that these dissimilarities are intrinsic in the information encoded by parietal neurons. These results highlight that, when decoding from the parietal cortex, for example, in brain machine interface implementations, attention should be paid in selecting the most suitable source of neural signals, given the great heterogeneity of this cortical sector.

RevDate: 2024-07-03

Liu W, Guo Y, Xie J, et al (2024)

Establishment and validation of a bad outcomes prediction model based on EEG and clinical parameters in prolonged disorder of consciousness.

Frontiers in human neuroscience, 18:1387471.

OBJECTIVE: This study aimed to explore the electroencephalogram (EEG) indicators and clinical factors that may lead to poor prognosis in patients with prolonged disorder of consciousness (pDOC), and establish and verify a clinical predictive model based on these factors.

METHODS: This study included 134 patients suffering from prolonged disorder of consciousness enrolled in our department of neurosurgery. We collected the data of sex, age, etiology, coma recovery scales (CRS-R) score, complications, blood routine, liver function, coagulation and other laboratory tests, resting EEG data and follow-up after discharge. These patients were divided into two groups: training set (n = 107) and verification set (n = 27). These patients were divided into a training set of 107 and a validation set of 27 for this study. Univariate and multivariate regression analysis were used to determine the factors affecting the poor prognosis of pDOC and to establish nomogram model. We use the receiver operating characteristic (ROC) and calibration curves to quantitatively test the effectiveness of the training set and the verification set. In order to further verify the clinical practical value of the model, we use decision curve analysis (DCA) to evaluate the model.

RESULT: The results from univariate and multivariate logistic regression analyses suggested that an increased frequency of occurrence microstate A, reduced CRS-R scores at the time of admission, the presence of episodes associated with paroxysmal sympathetic hyperactivity (PSH), and decreased fibrinogen levels all function as independent prognostic factors. These factors were used to construct the nomogram. The training and verification sets had areas under the curve of 0.854 and 0.920, respectively. Calibration curves and DCA demonstrated good model performance and significant clinical benefits in both sets.

CONCLUSION: This study is based on the use of clinically available and low-cost clinical indicators combined with EEG to construct a highly applicable and accurate model for predicting the adverse prognosis of patients with prolonged disorder of consciousness. It provides an objective and reliable tool for clinicians to evaluate the prognosis of prolonged disorder of consciousness, and helps clinicians to provide personalized clinical care and decision-making for patients with prolonged disorder of consciousness and their families.

RevDate: 2024-07-01

Song H, Hsieh TH, Yeon SH, et al (2024)

Continuous neural control of a bionic limb restores biomimetic gait after amputation.

Nature medicine [Epub ahead of print].

For centuries scientists and technologists have sought artificial leg replacements that fully capture the versatility of their intact biological counterparts. However, biological gait requires coordinated volitional and reflexive motor control by complex afferent and efferent neural interplay, making its neuroprosthetic emulation challenging after limb amputation. Here we hypothesize that continuous neural control of a bionic limb can restore biomimetic gait after below-knee amputation when residual muscle afferents are augmented. To test this hypothesis, we present a neuroprosthetic interface consisting of surgically connected, agonist-antagonist muscles including muscle-sensing electrodes. In a cohort of seven leg amputees, the interface is shown to augment residual muscle afferents by 18% of biologically intact values. Compared with a matched amputee cohort without the afferent augmentation, the maximum neuroprosthetic walking speed is increased by 41%, enabling equivalent peak speeds to persons without leg amputation. Further, this level of afferent augmentation enables biomimetic adaptation to various walking speeds and real-world environments, including slopes, stairs and obstructed pathways. Our results suggest that even a small augmentation of residual muscle afferents restores biomimetic gait under continuous neuromodulation in individuals with leg amputation.

RevDate: 2024-07-04
CmpDate: 2024-07-02

Li F, Gallego J, Tirko NN, et al (2024)

Low-intensity pulsed ultrasound stimulation (LIPUS) modulates microglial activation following intracortical microelectrode implantation.

Nature communications, 15(1):5512.

Microglia are important players in surveillance and repair of the brain. Implanting an electrode into the cortex activates microglia, produces an inflammatory cascade, triggers the foreign body response, and opens the blood-brain barrier. These changes can impede intracortical brain-computer interfaces performance. Using two-photon imaging of implanted microelectrodes, we test the hypothesis that low-intensity pulsed ultrasound stimulation can reduce microglia-mediated neuroinflammation following the implantation of microelectrodes. In the first week of treatment, we found that low-intensity pulsed ultrasound stimulation increased microglia migration speed by 128%, enhanced microglia expansion area by 109%, and a reduction in microglial activation by 17%, indicating improved tissue healing and surveillance. Microglial coverage of the microelectrode was reduced by 50% and astrocytic scarring by 36% resulting in an increase in recording performance at chronic time. The data indicate that low-intensity pulsed ultrasound stimulation helps reduce the foreign body response around chronic intracortical microelectrodes.

RevDate: 2024-07-04
CmpDate: 2024-07-04

Wu X, Metcalfe B, He S, et al (2024)

A Review of Motor Brain-Computer Interfaces Using Intracranial Electroencephalography Based on Surface Electrodes and Depth Electrodes.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 32:2408-2431.

Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used clinical invasive devices that have the potential for BCIs applications include surface electrodes used in electrocorticography (ECoG) and depth electrodes used in Stereo-electroencephalography (SEEG) and deep brain stimulation (DBS). This review focused on BCIs research using surface (ECoG) and depth electrodes (including SEEG, and DBS electrodes) for movement decoding on human subjects. Unlike previous reviews, the findings presented here are from the perspective of the decoding target or task. In detail, five tasks will be considered, consisting of the kinematic decoding, kinetic decoding,identification of body parts, dexterous hand decoding, and motion intention decoding. The typical studies are surveyed and analyzed. The reviewed literature demonstrated a distributed motor-related network that spanned multiple brain regions. Comparison between surface and depth studies demonstrated that richer information can be obtained using surface electrodes. With regard to the decoding algorithms, deep learning exhibited superior performance using raw signals than traditional machine learning algorithms. Despite the promising achievement made by the open-loop BCIs, closed-loop BCIs with sensory feedback are still in their early stage, and the chronic implantation of both ECoG surface and depth electrodes has not been thoroughly evaluated.

RevDate: 2024-07-01

Flores C, Contreras M, Macedo I, et al (2024)

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultural differences affecting neural data. Studies often focus on uniform single-site experiments in uniform settings, leading to high performance that may not translate well to real-world diversity. Deep learning models aim to enhance BCI classification accuracy, and transfer learning has been suggested to adapt models to individual neural patterns using a base model trained on others' data. This approach promises better generalizability and reduced overfitting, yet challenges remain in handling diverse and imbalanced datasets from different equipment, subjects, multiple centres in different countries, and both healthy and patient populations for effective model transfer and tuning. In a setting characterized by maximal heterogeneity, we proposed P300 wave detection in BCIs employing a convolutional neural network fitted with adaptive transfer learning based on Poison Sampling Disk (PDS) called Active Sampling (AS), which flexibly adjusts the transition from source data to the target domain. Our results reported for subject adaptive with 40% of adaptive fine-tuning that the averaged classification accuracy improved by 5.36% and standard deviation reduced by 12.22% using two distinct, internationally replicated datasets. These results outperformed in classification accuracy, computational time, and training efficiency, mainly due to the proposed Active Sampling (AS) method for transfer learning.

RevDate: 2024-07-01

Lü C, Wang T, Xi X, et al (2024)

A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs.

Computer methods in biomechanics and biomedical engineering [Epub ahead of print].

Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study's findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.

RevDate: 2024-06-30

Wang X, Yang W, Qi W, et al (2024)

STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding.

Neural networks : the official journal of the International Neural Network Society, 178:106471 pii:S0893-6080(24)00395-2 [Epub ahead of print].

Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external environments through brain signals. In response to the demands for high accuracy, robustness, and end-to-end capabilities within BCIs based on motor imagery (MI), this paper introduces STaRNet, a novel model that integrates multi-scale spatio-temporal convolutional neural networks (CNNs) with Riemannian geometry. Initially, STaRNet integrates a multi-scale spatio-temporal feature extraction module that captures both global and local features, facilitating the construction of Riemannian manifolds from these comprehensive spatio-temporal features. Subsequently, a matrix logarithm operation transforms the manifold-based features into the tangent space, followed by a dense layer for classification. Without preprocessing, STaRNet surpasses state-of-the-art (SOTA) models by achieving an average decoding accuracy of 83.29% and a kappa value of 0.777 on the BCI Competition IV 2a dataset, and 95.45% accuracy with a kappa value of 0.939 on the High Gamma Dataset. Additionally, a comparative analysis between STaRNet and several SOTA models, focusing on the most challenging subjects from both datasets, highlights exceptional robustness of STaRNet. Finally, the visualizations of learned frequency bands demonstrate that temporal convolutions have learned MI-related frequency bands, and the t-SNE analyses of features across multiple layers of STaRNet exhibit strong feature extraction capabilities. We believe that the accurate, robust, and end-to-end capabilities of the STaRNet will facilitate the advancement of BCIs.

RevDate: 2024-06-30

Wu X, Gao R, Tian X, et al (2024)

Co-composting of dewatered sludge and wheat straw with newly isolated Xenophilus azovorans: Carbon dynamics, humification, and driving pathways.

Journal of environmental management, 365:121613 pii:S0301-4797(24)01599-8 [Epub ahead of print].

Composting is a biological reaction caused by microorganisms. Composting efficiency can be adequately increased by adding biochar and/or by inoculating with exogenous microorganisms. In this study, we looked at four methods for dewatered sludge waste (DSW) and wheat straw (WS) aerobic co-composting: T1 (no additive), T2 (5% biochar), T3 (5% of a newly isolated strain, Xenophilus azovorans (XPA)), and T4 (5% of biochar-immobilized XPA (BCI-XPA)). Throughout the course of the 42-day composting period, we looked into the carbon dynamics, humification, microbial community succession, and modifications to the driving pathways. Compared to T1 and T2, the addition of XPA (T3) and BCI-XPA (T4) extended the thermophilic phase of composting without negatively affecting compost maturation. Notably, T4 exhibited a higher seed germination index (132.14%). Different from T1 and T2 treatments, T3 and T4 treatments increased CO2 and CH4 emissions in the composting process, in which the cumulative CO2 emissions increased by 18.61-47.16%, and T3 and T4 treatments also promoted the formation of humic acid. Moreover, T4 treatment with BCI-XPA addition showed relatively higher activities of urease, polyphenol oxidase, and laccase, as well as a higher diversity of microorganisms compared to other processes. The Functional Annotation of Prokaryotic Taxa (FAPROTAX) analysis showed that microorganisms involved in the carbon cycle dominated the entire composting process in all treatments, with chemoheterotrophy and aerobic chemoheterotrophy being the main pathways of organic materials degradation. Moreover, the presence of XPA accelerated the breakdown of organic materials by catabolism of aromatic compounds and intracellular parasite pathways. On the other hand, the xylanolysis pathway was aided in the conversion of organic materials to dissolved organics by the addition of BCI-XPA. These findings indicate that XPA and BCI-XPA have potential as additives to improve the efficiency of dewatered sludge and wheat straw co-composting.

RevDate: 2024-06-29

Kim JS, Kim H, Chung CK, et al (2024)

Dual model transfer learning to compensate for individual variability in brain-computer interface.

Computer methods and programs in biomedicine, 254:108294 pii:S0169-2607(24)00289-X [Epub ahead of print].

Recent advancements in brain-computer interface (BCI) technology have seen a significant shift towards incorporating complex decoding models such as deep neural networks (DNNs) to enhance performance. These models are particularly crucial for sophisticated tasks such as regression for decoding arbitrary movements. However, these BCI models trained and tested on individual data often face challenges with limited performance and generalizability across different subjects. This limitation is primarily due to a tremendous number of parameters of DNN models. Training complex models demands extensive datasets. Nevertheless, group data from many subjects may not produce sufficient decoding performance because of inherent variability in neural signals both across individuals and over time METHODS: To address these challenges, this study proposed a transfer learning approach that could effectively adapt to subject-specific variability in cortical regions. Our method involved training two separate movement decoding models: one on individual data and another on pooled group data. We then created a salience map for each cortical region from the individual model, which helped us identify the input's contribution variance across subjects. Based on the contribution variance, we combined individual and group models using a modified knowledge distillation framework. This approach allowed the group model to be universally applicable by assigning greater weights to input data, while the individual model was fine-tuned to focus on areas with significant individual variance RESULTS: Our combined model effectively encapsulated individual variability. We validated this approach with nine subjects performing arm-reaching tasks, with our method outperforming (mean correlation coefficient, r = 0.75) both individual (r = 0.70) and group models (r = 0.40) in decoding performance. In particular, there were notable improvements in cases where individual models showed low performances (e.g., r = 0.50 in the individual decoder to r = 0.61 in the proposed decoder) CONCLUSIONS: These results not only demonstrate the potential of our method for robust BCI, but also underscore its ability to generalize individual data for broader applicability.

RevDate: 2024-06-29

Sun H, Ding Y, Bao J, et al (2024)

Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention.

Neural networks : the official journal of the International Neural Network Society, 178:106470 pii:S0893-6080(24)00394-0 [Epub ahead of print].

Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification performance and require significant time for new users to collect subject-specific training data. This limitation diminishes the user-friendliness of BCIs and presents significant challenges in developing effective subject-independent models. In response to these challenges, we propose a novel subject-independent framework for learning temporal dependency for motor imagery BCIs by Contrastive Learning and Self-attention (CLS). In CLS model, we incorporate self-attention mechanism and supervised contrastive learning into a deep neural network to extract important information from electroencephalography (EEG) signals as features. We evaluate the CLS model using two large public datasets encompassing numerous subjects in a subject-independent experiment condition. The results demonstrate that CLS outperforms six baseline algorithms, achieving a mean classification accuracy improvement of 1.3 % and 4.71 % than the best algorithm on the Giga dataset and OpenBMI dataset, respectively. Our findings demonstrate that CLS can effectively learn invariant discriminative features from training data obtained from non-target subjects, thus showcasing its potential for building models for new users without the need for calibration.

RevDate: 2024-06-28

Awais MA, Ward T, Redmond P, et al (2024)

From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.

Journal of neural engineering [Epub ahead of print].

Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 Event-related Potential (ERP), elicited in the Rapid Serial Visual Presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection. Approach. In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording. Main results. The results, presented as Area Under the Receiver Operating Characteristic Curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials. Significance. Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

RevDate: 2024-06-28

Huang Z, Sun Y, Liu S, et al (2024)

A machine learning based method for tracking of simultaneously imaged neural activity and body posture of freely moving maggot.

Biochemical and biophysical research communications, 727:150290 pii:S0006-291X(24)00826-X [Epub ahead of print].

To understand neural basis of animal behavior, it is necessary to monitor neural activity and behavior in freely moving animal before building relationship between them. Here we use light sheet fluorescence microscope (LSFM) combined with microfluidic chip to simultaneously capture neural activity and body movement in small freely behaving Drosophila larva. We develop a transfer learning based method to simultaneously track the continuously changing body posture and activity of neurons that move together using a sub-region tracking network with a precise landmark estimation network for the inference of target landmark trajectory. Based on the tracking of each labelled neuron, the activity of the neuron indicated by fluorescent intensity is calculated. For each video, annotation of only 20 frames in a video is sufficient to yield human-level accuracy for all other frames. The validity of this method is further confirmed by reproducing the activity pattern of PMSIs (period-positive median segmental interneurons) and larval movement as previously reported. Using this method, we disclosed the correlation between larval movement and left-right asymmetry in activity of a group of unidentified neurons labelled by R52H01-Gal4 and further confirmed the roles of these neurons in bilateral balance of body contraction during larval crawling by genetic inhibition of these neurons. Our method provides a new tool for accurate extraction of neural activities and movement of freely behaving small-size transparent animals.

RevDate: 2024-06-28

Brewe AM, Antezana L, Carlton CN, et al (2024)

A Randomized Trial Utilizing EEG Brain Computer Interface to Improve Facial Emotion Recognition in Autistic Adults.

Journal of autism and developmental disorders [Epub ahead of print].

PURPOSE: Many individuals with autism spectrum disorder (ASD) experience challenges with facial emotion recognition (FER), which may exacerbate social difficulties in ASD. Few studies have examined whether FER can be experimentally manipulated and improved for autistic people. This study utilized a randomized controlled trial design to examine acceptability and preliminary clinical impact of a novel mixed reality-based neurofeedback program, FER Assistant, using EEG brain computer interface (BCI)-assisted technology to improve FER for autistic adolescents and adults.

METHODS: Twenty-seven autistic male participants (M age: 21.12 years; M IQ: 105.78; 85% white) were randomized to the active condition to receive FER Assistant (n = 17) or waitlist control (n = 10). FER Assistant participants received ten sessions utilizing BCI-assisted neurofeedback training in FER. All participants, regardless of randomization, completed a computerized FER task at baseline and endpoint.

RESULTS: Results partially indicated that FER Assistant was acceptable to participants. Regression analyses demonstrated that participation in FER Assistant led to group differences in FER at endpoint, compared to a waitlist control. However, analyses examining reliable change in FER indicated no reliable improvement or decline for FER Assistant participants, whereas two waitlist participants demonstrated reliable decline.

CONCLUSION: Given the preliminary nature of this work, results collectively suggest that FER Assistant may be an acceptable intervention. Results also suggest that FER may be a potential mechanism that is amenable to intervention for autistic individuals, although additional trials using larger sample sizes are warranted.

RevDate: 2024-06-28

Zhang Z, Huang Y, Chen X, et al (2024)

State-specific Regulation of Electrical Stimulation in the Intralaminar Thalamus of Macaque Monkeys: Network and Transcriptional Insights into Arousal.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Epub ahead of print].

Long-range thalamocortical communication is central to anesthesia-induced loss of consciousness and its reversal. However, isolating the specific neural networks connecting thalamic nuclei with various cortical regions for state-specific anesthesia regulation is challenging, with the biological underpinnings still largely unknown. Here, simultaneous electroencephalogram-fuctional magnetic resonance imaging (EEG-fMRI) and deep brain stimulation are applied to the intralaminar thalamus in macaques under finely-tuned propofol anesthesia. This approach led to the identification of an intralaminar-driven network responsible for rapid arousal during slow-wave oscillations. A network-based RNA-sequencing analysis is conducted of region-, layer-, and cell-specific gene expression data from independent transcriptomic atlases and identifies 2489 genes preferentially expressed within this arousal network, notably enriched in potassium channels and excitatory, parvalbumin-expressing neurons, and oligodendrocytes. Comparison with human RNA-sequencing data highlights conserved molecular and cellular architectures that enable the matching of homologous genes, protein interactions, and cell types across primates, providing novel insight into network-focused transcriptional signatures of arousal.

RevDate: 2024-06-30
CmpDate: 2024-06-27

Morozova M, Nasibullina A, Yakovlev L, et al (2024)

Tactile versus motor imagery: differences in corticospinal excitability assessed with single-pulse TMS.

Scientific reports, 14(1):14862.

Tactile Imagery (TI) remains a fairly understudied phenomenon despite growing attention to this topic in recent years. Here, we investigated the effects of TI on corticospinal excitability by measuring motor evoked potentials (MEPs) induced by single-pulse transcranial magnetic stimulation (TMS). The effects of TI were compared with those of tactile stimulation (TS) and kinesthetic motor imagery (kMI). Twenty-two participants performed three tasks in randomly assigned order: imagine finger tapping (kMI); experience vibratory sensations in the middle finger (TS); and mentally reproduce the sensation of vibration (TI). MEPs increased during both kMI and TI, with a stronger increase for kMI. No statistically significant change in MEP was observed during TS. The demonstrated differential effects of kMI, TI and TS on corticospinal excitability have practical implications for devising the imagery-based and TS-based brain-computer interfaces (BCIs), particularly the ones intended to improve neurorehabilitation by evoking plasticity changes in sensorimotor circuitry.

RevDate: 2024-06-27

Savalle E, Pillette L, Won K, et al (2024)

Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Presence is an important aspect of user experience in virtual reality. It corresponds to the illusion of being physically located in a virtual environment. This feeling is usually measured through questionnaires that disrupt presence, are subjective and do not allow for real-time measurement. Electroencephalography (EEG), which measures brain activity, is increasingly used to monitor the state of users, especially while immersed in virtual reality.

APPROACH: In this paper, we present a way of evaluating presence, through the measure of the attention dedicated to the real environment via an EEG oddball paradigm. Using breaks in presence (BiP), this experimental protocol constitutes an ecological method for the study of presence, as different levels of presence are experienced in an identical virtual environment.

MAIN RESULTS: Through analyzing the EEG data of 18 participants, a significant increase in the neurophysiological reaction to the oddball, i.e., the P300 amplitude, was found in low presence condition compared to high presence condition. This amplitude was significantly correlated with the self-reported measure of presence. Using Riemannian geometry to perform single-trial classification, we present a classification algorithm with 79% accuracy in detecting between two presence conditions.

SIGNIFICANCE: Taken together our results promote the use of EEG and oddball stimuli to monitor presence offline or in real-time without interrupting the user in the virtual environment.

RevDate: 2024-06-28

Ren G, Kumar A, Mahmoud SS, et al (2024)

A deep neural network and transfer learning combined method for cross-task classification of error-related potentials.

Frontiers in human neuroscience, 18:1394107.

BACKGROUND: Error-related potentials (ErrPs) are electrophysiological responses that naturally occur when humans perceive wrongdoing or encounter unexpected events. It offers a distinctive means of comprehending the error-processing mechanisms within the brain. A method for detecting ErrPs with high accuracy holds significant importance for various ErrPs-based applications, such as human-in-the-loop Brain-Computer Interface (BCI) systems. Nevertheless, current methods fail to fulfill the generalization requirements for detecting such ErrPs due to the high non-stationarity of EEG signals across different tasks and the limited availability of ErrPs datasets.

METHODS: This study introduces a deep learning-based model that integrates convolutional layers and transformer encoders for the classification of ErrPs. Subsequently, a model training strategy, grounded in transfer learning, is proposed for the effective training of the model. The datasets utilized in this study are available for download from the publicly accessible databases.

RESULTS: In cross-task classification, an average accuracy of about 78% was achieved, exceeding the baseline. Furthermore, in the leave-one-subject-out, within-session, and cross-session classification scenarios, the proposed model outperformed the existing techniques with an average accuracy of 71.81, 78.74, and 77.01%, respectively.

CONCLUSIONS: Our approach contributes to mitigating the challenge posed by limited datasets in the ErrPs field, achieving this by reducing the requirement for extensive training data for specific target tasks. This may serve as inspiration for future studies that concentrate on ErrPs and their applications.

RevDate: 2024-07-04
CmpDate: 2024-07-04

Chen W, Wang S, Bao J, et al (2024)

Restoration of coherent reach-grasp-pull movement via sequential intraneural peripheral nerve stimulation in rats.

Journal of neural engineering, 21(4):.

Objective.Peripheral nerve stimulation (PNS) has been demonstrated as an effective way to selectively activate muscles and to produce fine hand movements. However, sequential multi-joint upper limb movements, which are critical for paralysis rehabilitation, has not been tested with PNS. Here, we aimed to restore multiple upper limb joint movements through an intraneural interface with a single electrode, achieving coherent reach-grasp-pull movement tasks through sequential stimulation.Approach.A transverse intrafascicular multichannel electrode was implanted under the axilla of the rat's upper limb, traversing the musculocutaneous, radial, median, and ulnar nerves. Intramuscular electrodes were implanted into the biceps brachii (BB), triceps brachii (TB), flexor carpi radialis (FCR), and extensor carpi radialis (ECR) muscles to record electromyographic (EMG) activity and video recordings were used to capture the kinematics of elbow, wrist, and digit joints. Charge-balanced biphasic pulses were applied to different channels to recruit distinct upper limb muscles, with concurrent recording of EMG signals and joint kinematics to assess the efficacy of the stimulation. Finally, a sequential stimulation protocol was employed by generating coordinated pulses in different channels.Main results.BB, TB, FCR and ECR muscles were selectively activated and various upper limb movements, including elbow flexion, elbow extension, wrist flexion, wrist extension, digit flexion, and digit extension, were reliably generated. The modulation effects of stimulation parameters, including pulse width, amplitude, and frequency, on induced joint movements were investigated and reach-grasp-pull movement was elicited by sequential stimulation.Significance.Our results demonstrated the feasibility of sequential intraneural stimulation for functional multi-joint movement restoration, providing a new approach for clinical rehabilitation in paralyzed patients.

RevDate: 2024-06-29
CmpDate: 2024-06-27

Jochumsen M, Lavesen ER, Griem AB, et al (2024)

The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection.

Sensors (Basel, Switzerland), 24(12):.

Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies.

RevDate: 2024-06-29
CmpDate: 2024-06-27

Papadopoulou A, Hermiz J, Grace C, et al (2024)

A Modular 512-Channel Neural Signal Acquisition ASIC for High-Density 4096 Channel Electrophysiology.

Sensors (Basel, Switzerland), 24(12):.

The complexity of information processing in the brain requires the development of technologies that can provide spatial and temporal resolution by means of dense electrode arrays paired with high-channel-count signal acquisition electronics. In this work, we present an ultra-low noise modular 512-channel neural recording circuit that is scalable to up to 4096 simultaneously recording channels. The neural readout application-specific integrated circuit (ASIC) uses a dense 8.2 mm × 6.8 mm 2D layout to enable high-channel count, creating an ultra-light 350 mg flexible module. The module can be deployed on headstages for small animals like rodents and songbirds, and it can be integrated with a variety of electrode arrays. The chip was fabricated in a TSMC 0.18 µm 1.8 V CMOS technology and dissipates a total of 125 mW. Each DC-coupled channel features a gain and bandwidth programmable analog front-end along with 14 b analog-to-digital conversion at speeds up to 30 kS/s. Additionally, each front-end includes programmable electrode plating and electrode impedance measurement capability. We present both standalone and in vivo measurements results, demonstrating the readout of spikes and field potentials that are modulated by a sensory input.

RevDate: 2024-06-29
CmpDate: 2024-06-27

Mwata-Velu T, Zamora E, Vasquez-Gomez JI, et al (2024)

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.

Sensors (Basel, Switzerland), 24(12):.

This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain-computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).

RevDate: 2024-07-03
CmpDate: 2024-07-03

Jin X, Yang X, Kong W, et al (2024)

TSFAN: tensorized spatial-frequency attention network with domain adaptation for cross-session EEG-based biometric recognition.

Journal of neural engineering, 21(4):.

Objective.Electroencephalogram (EEG) signals are promising biometrics owning to their invisibility, adapting to the application scenarios with high-security requirements. However, It is challenging to explore EEG identity features without the interference of device and state differences of the subject across sessions. Existing methods treat training sessions as a single domain, affected by the different data distribution among sessions. Although most multi-source unsupervised domain adaptation (MUDA) methods bridge the domain gap between multiple source and target domains individually, relationships among the domain-invariant features of each distribution alignment are neglected.Approach.In this paper, we propose a MUDA method, Tensorized Spatial-Frequency Attention Network (TSFAN), to assist the performance of the target domain for EEG-based biometric recognition. Specifically, significant relationships of domain-invariant features are modeled via a tensorized attention mechanism. It jointly incorporates appropriate common spatial-frequency representations of pairwise source and target but also cross-source domains, without the effect of distribution discrepancy among source domains. Additionally, considering the curse of dimensionality, our TSFAN is approximately represented in Tucker format. Benefiting the low-rank Tucker Network, the TSFAN can scale linearly in the number of domains, providing us the great flexibility to extend TSFAN to the case associated with an arbitrary number of sessions.Main results.Extensive experiments on the representative benchmarks demonstrate the effectiveness of TSFAN in EEG-based biometric recognition, outperforming state-of-the-art approaches, as verified by cross-session validation.Significance.The proposed TSFAN aims to investigate the presence of consistent EEG identity features across sessions. It is achieved by utilizing a novel tensorized attention mechanism that collaborates intra-source transferable information with inter-source interactions, while remaining unaffected by domain shifts in multiple source domains. Furthermore, the electrode selection shows that EEG-based identity features across sessions are distributed across brain regions, and 20 electrodes based on 10-20 standard system are able to extract stable identity information.

RevDate: 2024-06-29
CmpDate: 2024-06-27

Huang W, Liu X, Yang W, et al (2024)

Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO.

Sensors (Basel, Switzerland), 24(12):.

A motor imagery brain-computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.

RevDate: 2024-06-29

Krüger L, Kamp O, Alfen K, et al (2024)

Pediatric Carotid Injury after Blunt Trauma and the Necessity of CT and CTA-A Narrative Literature Review.

Journal of clinical medicine, 13(12):.

Background: Blunt carotid injury (BCI) in pediatric trauma is quite rare. Due to the low number of cases, only a few reports and studies have been conducted on this topic. This review will discuss how frequent BCI/blunt cerebrovascular injury (BCVI) on pediatric patients after blunt trauma is, what routine diagnostics looks like, if a computed tomography (CT)/computed tomography angiography (CTA) scan on pediatric patients after blunt trauma is always necessary and if there are any negative health effects. Methods: This narrative literature review includes reviews, systematic reviews, case reports and original studies in the English language between 1999 and 2020 that deal with pediatric blunt trauma and the diagnostics of BCI and BCVI. Furthermore, publications on the risk of radiation exposure for children were included in this study. For literature research, Medline (PubMed) and the Cochrane library were used. Results: Pediatric BCI/BCVI shows an overall incidence between 0.03 and 0.5% of confirmed BCI/BCVI cases due to pediatric blunt trauma. In total, 1.1-3.5% of pediatric blunt trauma patients underwent CTA to detect BCI/BCVI. Only 0.17-1.2% of all CTA scans show a positive diagnosis for BCI/BCVI. In children, the median volume CT dose index on a non-contrast head CT is 33 milligrays (mGy), whereas a computed tomography angiography needs at least 138 mGy. A cumulative dose of about 50 mGy almost triples the risk of leukemia, and a cumulative dose of about 60 mGy triples the risk of brain cancer. Conclusions: Given that a BCI/BCVI could have extensive neurological consequences for children, it is necessary to evaluate routine pediatric diagnostics after blunt trauma. CT and CTA are mostly used in routine BCI/BCVI diagnostics. However, since radiation exposure in children should be as low as reasonably achievable, it should be asked if other diagnostic methods could be used to identify risk groups. Trauma guidelines and clinical scores like the McGovern score are established BCI/BCVI screening options, as well as duplex ultrasound.

RevDate: 2024-06-29

Li X, Yang S, Fei N, et al (2024)

A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG.

Bioengineering (Basel, Switzerland), 11(6):.

The application of wearable electroencephalogram (EEG) devices is growing in brain-computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state visual evoked potential (SSVEP)-based BCI. However, fewer-channel EEGs can cause the BCI performance to decrease. To address this issue, an attention-based complex spectrum-convolutional neural network (atten-CCNN) is proposed in this study, which combines a CNN with a squeeze-and-excitation block and uses the spectrum of the EEG signal as the input. The proposed model was assessed on a wearable 40-class dataset and a public 12-class dataset under subject-independent and subject-dependent conditions. The results show that whether using a three-channel EEG or single-channel EEG for SSVEP identification, atten-CCNN outperformed the baseline models, indicating that the new model can effectively enhance the performance of SSVEP-BCI with few-channel EEGs. Therefore, this SSVEP identification algorithm based on a few-channel EEG is particularly suitable for use with wearable EEG devices.

RevDate: 2024-06-26

Wyse Sookoo K, Luo S, Candrea D, et al (2024)

Stability of ECoG high gamma signals during speech and implications for a speech BCI system in an individual with ALS: a year-long longitudinal study.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Speech brain-computer interfaces (BCIs) have the potential to augment communication in individuals with impaired speech due to muscle weakness, for example in ALS and other neurological disorders. However, to achieve long-term, reliable use of a speech BCI, it is essential for speech-related neural signal changes to be stable over long periods of time. Here we study, for the first time, the stability of speech-related electrocorticographic (ECoG) signals recorded from a chronically implanted ECoG BCI over a 12 month period.

APPROACH: ECoG signals were recorded by an ECoG array implanted over the ventral sensorimotor cortex (vSMC) in a clinical trial participant with ALS. Because ECoG-based speech decoding has most often relied on broadband high gamma signal changes relative to baseline (non-speech) conditions, we studied longitudinal changes of high gamma band (HG) power at baseline and during speech, and we compared these with residual high frequency (HF) noise levels at baseline. Stability was further assessed by longitudinal measurements of signal-to-noise ratio (SNR), activation ratio (ActR), and peak speech-related HG response magnitude (HG response peaks). Lastly, we analyzed the stability of the event-related HG power changes (HG responses) for individual syllables at each electrode.

MAIN RESULTS: We found that speech-related ECoG signal responses were stable over a range of syllables activating different articulators for the first year after implantation.

SIGNIFICANCE: Together, our results indicate that ECoG can be a stable recording modality for long-term speech BCI systems for those living with severe paralysis.

CLINICALTRIALS: gov, registration number NCT03567213.

RevDate: 2024-06-26

Augenstein TE, Nagalla D, Mohacey A, et al (2024)

A novel virtual robotic platform for controlling six degrees of freedom assistive devices with body-machine interfaces.

Computers in biology and medicine, 178:108778 pii:S0010-4825(24)00863-1 [Epub ahead of print].

Body-machine interfaces (BoMIs)-systems that control assistive devices (e.g., a robotic manipulator) with a person's movements-offer a robust and non-invasive alternative to brain-machine interfaces for individuals with neurological injuries. However, commercially-available assistive devices offer more degrees of freedom (DOFs) than can be efficiently controlled with a user's residual motor function. Therefore, BoMIs often rely on nonintuitive mappings between body and device movements. Learning these mappings requires considerable practice time in a lab/clinic, which can be challenging. Virtual environments can potentially address this challenge, but there are limited options for high-DOF assistive devices, and it is unclear if learning with a virtual device is similar to learning with its physical counterpart. We developed a novel virtual robotic platform that replicated a commercially-available 6-DOF robotic manipulator. Participants controlled the physical and virtual robots using four wireless inertial measurement units (IMUs) fixed to the upper torso. Forty-three neurologically unimpaired adults practiced a target-matching task using either the physical (sample size n = 25) or virtual device (sample size n = 18) involving pre-, mid-, and post-tests separated by four training blocks. We found that both groups made similar improvements from pre-test in movement time at mid-test (Δvirtual: 9.9 ± 9.5 s; Δphysical: 11.1 ± 9.9 s) and post-test (Δvirtual: 11.1 ± 9.1 s; Δphysical: 11.8 ± 10.5 s) and in path length at mid-test (Δvirtual: 6.1 ± 6.3 m/m; Δphysical: 3.3 ± 3.5 m/m) and post-test (Δvirtual: 6.6 ± 6.2 m/m; Δphysical: 3.5 ± 4.0 m/m). Our results indicate the feasibility of using virtual environments for learning to control assistive devices. Future work should determine how these findings generalize to clinical populations.

RevDate: 2024-06-27

Huang J, Lv Y, Zhang ZQ, et al (2024)

Temporally Local Weighting-Based Phase-Locked Time-Shift Data Augmentation Method for Fast-Calibration SSVEP-BCI.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Various training-based spatial filtering methods have been proposed to decode steady-state visual evoked potentials (SSVEPs) efficiently. However, these methods require extensive calibration data to obtain valid spatial filters and temporal templates. The time-consuming data collection and calibration process would reduce the practicality of SSVEP-based brain-computer interfaces (BCIs). Therefore, we propose a temporally local weighting-based phase-locked time-shift (TLW-PLTS) data augmentation method to augment training data for calculating valid spatial filters and temporal templates. In this method, the sliding window strategy using the SSVEP response period as a time-shift step is to generate the augmented data, and the time filter which maximises the temporally local covariance between the original template signal and the sine-cosine reference signal is used to suppress the temporal noise in the augmented data. For the performance evaluation, the TLW-PLTS method was incorporated with state-of-the-art training-based spatial filtering methods to calculate classification accuracies and information transfer rates (ITRs) using three SSVEP datasets. Compared with state-of-the-art training-based spatial filtering methods and other data augmentation methods, the proposed TLW-PLTS method demonstrates superior decoding performance with fewer calibration data, which is promising for the development of fast-calibration BCIs.

RevDate: 2024-06-28
CmpDate: 2024-06-26

Iwama S, Tsuchimoto S, Mizuguchi N, et al (2024)

EEG decoding with spatiotemporal convolutional neural network for visualization and closed-loop control of sensorimotor activities: A simultaneous EEG-fMRI study.

Human brain mapping, 45(9):e26767.

Closed-loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high-density whole-head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote regions. We simultaneously acquired EEG and functional magnetic resonance images in humans during the brain-computer interface (BCI) based neurofeedback training and compared the reconstructed and modeled hemodynamic responses of the sensorimotor network. Filters constructed with a convolutional neural network captured activities in the targeted network with spatial precision and specificity superior to those of the EEG signals preprocessed with standard pipelines used in BCI-based neurofeedback paradigms. The middle layers of the trained model were examined to characterize the neuronal oscillatory features that contributed to the reconstruction. Analysis of the layers for spatial convolution revealed the contribution of distributed cortical circuitries to reconstruction, including the frontoparietal and sensorimotor areas, and those of temporal convolution layers that successfully reconstructed the hemodynamic response function. Employing a spatiotemporal filter and leveraging the electrophysiological signatures of the sensorimotor excitability identified in our middle layer analysis would contribute to the development of a further effective neurofeedback intervention.

RevDate: 2024-06-26

Chen J, Zeng H, Cheng Y, et al (2024)

Identifying radiogenomic associations of breast cancer based on DCE-MRI by using Siamese Neural Network with manufacturer bias normalization.

Medical physics [Epub ahead of print].

BACKGROUND AND PURPOSE: The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides prognostic information and guides treatment for patients with invasive breast cancer. The objective of this paper is to establish a non-invasive system for identifying HER2 and HR in breast cancer using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

METHODS: In light of the absence of high-performance algorithms and external validation in previously published methods, this study utilizes 3D deep features and radiomics features to represent the information of the Region of Interest (ROI). A Siamese Neural Network was employed as the classifier, with 3D deep features and radiomics features serving as the network input. To neutralize manufacturer bias, a batch effect normalization method, ComBat, was introduced. To enhance the reliability of the study, two datasets, Predict Your Therapeutic Response with Imaging and moLecular Analysis (I-SPY 1) and I-SPY 2, were incorporated. I-SPY 2 was utilized for model training and validation, while I-SPY 1 was exclusively employed for external validation. Additionally, a breast tumor segmentation network was trained to improve radiomic feature extraction.

RESULTS: The results indicate that our approach achieved an average Area Under the Curve (AUC) of 0.632, with a Standard Error of the Mean (SEM) of 0.042 for HER2 prediction in the I-SPY 2 dataset. For HR prediction, our method attained an AUC of 0.635 (SEM 0.041), surpassing other published methods in the AUC metric. Moreover, the proposed method yielded competitive results in other metrics. In external validation using the I-SPY 1 dataset, our approach achieved an AUC of 0.567 (SEM 0.032) for HR prediction and 0.563 (SEM 0.033) for HER2 prediction.

CONCLUSION: This study proposes a non-invasive system for identifying HER2 and HR in breast cancer. Although the results do not conclusively demonstrate superiority in both tasks, they indicate that the proposed method achieved good performance and is a competitive classifier compared to other reference methods. Ablation studies demonstrate that both radiomics features and deep features for the Siamese Neural Network are beneficial for the model. The introduced manufacturer bias normalization method has been shown to enhance the method's performance. Furthermore, the external validation of the method enhances the reliability of this research. Source code, pre-trained segmentation network, Radiomics and deep features, data for statistical analysis, and Supporting Information of this article are online at: https://github.com/FORRESTHUACHEN/Siamese_Neural_Network_based_Brest_cancer_Radiogenomic.

RevDate: 2024-06-28

Bardella G, Franchini S, Pan L, et al (2024)

Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons.

Entropy (Basel, Switzerland), 26(6):.

Brain-computer interfaces have seen extraordinary surges in developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro and meso scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neuron activity, and generalize the maximum entropy model for neural networks so that the time evolution of the system is also taken into account. This is obtained by bridging particle physics and neuroscience, paving the way for particle physics-inspired models of the neocortex.

RevDate: 2024-06-28

Neven H, Zalcman A, Read P, et al (2024)

Testing the Conjecture That Quantum Processes Create Conscious Experience.

Entropy (Basel, Switzerland), 26(6):.

The question of what generates conscious experience has mesmerized thinkers since the dawn of humanity, yet its origins remain a mystery. The topic of consciousness has gained traction in recent years, thanks to the development of large language models that now arguably pass the Turing test, an operational test for intelligence. However, intelligence and consciousness are not related in obvious ways, as anyone who suffers from a bad toothache can attest-pain generates intense feelings and absorbs all our conscious awareness, yet nothing particularly intelligent is going on. In the hard sciences, this topic is frequently met with skepticism because, to date, no protocol to measure the content or intensity of conscious experiences in an observer-independent manner has been agreed upon. Here, we present a novel proposal: Conscious experience arises whenever a quantum mechanical superposition forms. Our proposal has several implications: First, it suggests that the structure of the superposition determines the qualia of the experience. Second, quantum entanglement naturally solves the binding problem, ensuring the unity of phenomenal experience. Finally, a moment of agency may coincide with the formation of a superposition state. We outline a research program to experimentally test our conjecture via a sequence of quantum biology experiments. Applying these ideas opens up the possibility of expanding human conscious experience through brain-quantum computer interfaces.

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Rajesh Rao has written the perfect introduction to the exciting world of brain-computer interfaces. The book is remarkably comprehensive — not only including full descriptions of classic and current experiments but also covering essential background concepts, from the brain to Bayes and back. Brain-Computer Interfacing will be welcomed by a wide range of intelligent readers interested in understanding the first steps toward the symbiotic merger of brains and computers. Eberhard E. Fetz, UW

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