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The current work aimed to understand the behavioral manifestations that result from disruptions to the selective facilitation of task-relevant sensory information at early cortical processing stages in those with a history of concussion. A total of 40 participants were recruited to participate in this study, with 25 in the concussion history group (Hx) and 15 in the control group (No-Hx). Somatosensory-evoked potentials (SEPs) were elicited via median nerve stimulation while subjects performed a task that manipulated their focus of attention toward or away from proprioceptive cues. Participants also completed an implicit motor sequence learning task relying solely on proprioceptive cues, as well as a visual attentional blink (AB) task to understand the effect of concussion on rapid shifts in attention. The Hx SEP data replicated past work showing an absence of relevancy-based facilitation at early cortical processing stages (N20-P27) that emerged at later processing stages. Our Hx showed evidence of relevancy-based facilitation at either the P50-N70 or the N70-P100. Performance on the learning task was not significantly different between the Hx and No-Hx. Performance on the AB task revealed greater AB magnitude in the Hx compared to the No-Hx. Collectively, these results suggest a compensatory strategy in the Hx that enables them to learn to the same degree as controls. However, when the attentional system is taxed with high temporal demands there are decrements in performance. These results are of particular importance given that these individuals are at an increased risk of sustaining subsequent concussions, and musculoskeletal injuries. [ABSTRACT FROM AUTHOR]

• Long-interval Intracortical Inhibition (LICI) in the left dorsolateral prefrontal cortex was observed in the 30–250 ms range. • More accurate LICI results can be obtained by using a sham coil that induces an equivalent magnetic field around the coil. • Applying signal source estimation confirmed a more robust LICI. This study aimed to optimally evaluate the effect of the long-interval intracortical inhibition (LICI) in the dorsolateral prefrontal cortex (DLPFC) through transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) by eliminating the volume conductance with signal source estimation and using a realistic sham coil as a control. We compared the LICI effects from the DLPFC between the active and sham stimulation conditions in 27 healthy participants. Evoked responses between the two conditions were evaluated at the sensor and source levels. At the sensor level, a significant LICI effect was confirmed in the active condition in the global mean field power analysis; however, in the local mean field power analysis focused on the DLPFC, no LICI effect was observed in the active condition. However, in the signal source estimation analysis for the DLPFC, we could reconfirm a significant LICI effect (p = 0.023) in the interval 30–250 ms post-stimulus, compared to the sham condition. Our results demonstrate that application of realistic sham stimulation condition and source estimation method allows for a robust and optimal identification of the LICI effect in the DLPFC. The optimal DLPFC-LICI effect was identified by the use of the sophisticated sham coil. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) of the patient is used to identify their motor intention, which is then converted into a control signal through a brain-computer interface (BCI) based on motor imagery. Whenever gathering features from EEG signals, making a BCI is difficult in part because of the enormous dimensionality of the data. Three stages make up the suggested methodology: pre-processing, extraction of features, selection, and categorization. To remove unwanted artifacts, the EEG signals are filtered by a fifth-order Butterworth multichannel band-pass filter. This decreases execution time and memory use, both of which improve system performance. Then a novel multichannel optimized CSP-ICA feature extraction technique is used to separate and eliminate non-discriminative information from discriminative information in the EEG channels. Furthermore, CSP uses the concept of an Artificial Bee Colony (ABC) algorithm to automatically identify the simultaneous global ideal frequency band and time interval combination for the extraction and classification of common spatial pattern characteristics. Finally, a Tunable optimized feed-forward neural network (FFNN) classifier is utilized to extract and categorize the temporal and frequency domain features, which employs an FFNN classifier with Tunable-Q wavelet transform. The proposed framework, therefore optimizes signal processing, enabling enhanced EEG signal classification for BCI applications. The result shows that the models that use Tunable optimized FFNN produce higher classification accuracy of more than 20% when compared to the existing models. [ABSTRACT FROM AUTHOR]

It is crucial to evaluate learning outcomes by identifying the cognitive state of the learner during the learning process. Studies utilizing Electroencephalography (EEG) and other peripheral physiological signals, combined with deep learning models, have demonstrated improved performance in cognitive state recognition. These studies have primarily focused on unimodal data, which are vulnerable to various types of noise, making it difficult to fully capture and represent cognitive states. Leveraging the complementarity between multimodal physiological signals can mitigate the impact of anomalies in unimodal data, thereby improving the accuracy and stability of cognitive state recognition. Therefore, this study proposes a multimodal physiological signal feature representation fusion model based on multi-level attention (PSFMMA). The model aims to integrate multimodal physiological signals to identify learners’ cognitive states with greater stability and accuracy. PSFMMA first extracts the temporal features of physiological signals by multiplexing the embedding layer. Subsequently, it generates signal representation vectors by further extracting semantic features through a signal feature mapping layer and enhancing important features with designed attention modules. Finally, the model employs an attention mechanism based on different signal representation vectors to fuse multimodal information for identifying learners’ cognitive states. This study designs various learning activities and collects electroencephalography (EEG), electrodermal activity (EDA), and photoplethysmography (PPG) data from 22 participants engaging in these activities to create the Based on Learning Activities Collection (BLAC) dataset. The proposed model was evaluated on the BLAC dataset, achieving an identification accuracy of 96.32 ± 0.32%. The results demonstrate that the model can effectively recognize learners’ cognitive states. Furthermore, the model’s performance was validated on the publicly available emotion classification dataset DEAP, attaining an accuracy of 99.15 ± 0.12%. The source code is available at . [ABSTRACT FROM AUTHOR]

AbstractConvolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features from low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with a lightweight temporal attention mechanism for EEG-Based MI decoding. Specifically, MBMSNet first extracts multi-view representations from raw EEG signals, followed by independent branches to capture spatial, spectral, temporal-spatial, and temporal-spectral features. Each branch includes a domain-specific convolutional layer, a variance layer, and a temporal attention layer. Finally, the features derived from each branch are concatenated with weights and classified through a fully connected layer. Experiments demonstrate MBMSNet outperforms state-of-the-art models, achieving accuracies of 84.60% on BCI Competition IV 2a, 87.80% on 2b, and 74.58% on OpenBMI, showcasing its potential for robust BCI applications. [ABSTRACT FROM AUTHOR]

Background: The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved. This study aims to investigate the EEG correlates of unimanual, non-repetitive finger flexion and extension. Methods: Sixteen healthy, right-handed participants completed multiple sessions of right-hand finger movement experiments. These included five individual (Thumb, Index, Middle, Ring, and Pinky) and four coordinated (Pinch, Point, ThumbsUp, and Fist) finger flexions and extensions, along with a rest condition (None). High-density EEG and finger trajectories were simultaneously recorded and analyzed. We examined low-frequency (0.3–3 Hz) time series and movement-related cortical potentials (MRCPs), and event-related desynchronization/synchronization (ERD/S) in the alpha- (8–13 Hz) and beta (13–30 Hz) bands. A clustering approach based on Riemannian distances was used to chart similarities between the broadband EEG responses (0.3–70 Hz) to the different finger scenarios. The contribution of different state-of-the-art features was identified across sub-bands, from low-frequency to low gamma (30–70 Hz), and an ensemble approach was used to pairwise classify single-trial finger movements and rest. Results: A significant decrease in EEG amplitude in the low-frequency time series was observed in the contralateral frontal-central regions during finger flexion and extension. Distinct MRCP patterns were found in the pre-, ongoing-, and post-movement stages. Additionally, strong ERD was detected in the contralateral central brain regions in both alpha and beta bands during finger flexion and extension, with the beta band showing a stronger rebound (ERS) post-movement. Within the finger movement repertoire, the Thumb was most distinctive, followed by the Fist. Decoding results indicated that low-frequency time-domain amplitude better differentiates finger movements, while alpha and beta band power and Riemannian features better detect movement versus rest. Combining these features yielded over 80% finger movement detection accuracy, while pairwise classification accuracy exceeded 60% for the Thumb versus the other fingers. Conclusion: Our findings confirm that non-repetitive finger movements, whether individual or coordinated, can be precisely detected from EEG. However, differentiating between specific movements is challenging due to highly overlapping neural correlates in time, spectral, and spatial domains. Nonetheless, certain finger movements, such as those involving the Thumb, exhibit distinct EEG responses, making them prime candidates for dexterous finger neuroprostheses. [ABSTRACT FROM AUTHOR]

Introduction: Early identification of Autism Spectrum Disorder (ASD) is critical for effective intervention. Restricted interests (RIs), a subset of repetitive behaviors, are a prominent but underutilized domain for early ASD diagnosis. This study aimed to identify objective biomarkers for ASD by integrating electroencephalography (EEG) and eye-tracking (ET) to analyze toddlers' visual attention and cortical responses to RI versus neutral interest (NI) objects. Methods: The study involved 59 toddlers aged 2-4 years, including 32 with ASD and 27 non-ASD controls. Participants underwent a 24-object passive viewing paradigm, featuring RI (e.g., transportation items) and NI objects (e.g., balloons). ET metrics (fixation time and pupil size) and EEG time-frequency (TF) power in theta (4-8 Hz) and alpha (8-13 Hz) bands were analyzed. Statistical methods included logistic regression models to assess the predictive potential of combined EEG and ET biomarkers. Results: Toddlers with ASD exhibited significantly increased fixation times and pupil sizes for RI objects compared to NI objects, alongside distinct EEG patterns with elevated theta and reduced alpha power in occipital regions during RI stimuli. The multimodal logistic regression model, incorporating EEG and ET metrics, achieved an area under the curve (AUC) of 0.75, demonstrating robust predictive capability for ASD. Discussion: This novel integration of ET and EEG metrics highlights the potential of RIs as diagnostic markers for ASD. The observed neural and attentional distinctions underscore the utility of multimodal biomarkers for early diagnosis and personalized intervention strategies. Future work should validate findings across broader age ranges and diverse populations. [ABSTRACT FROM AUTHOR]

We introduce an open-source Python package for the analysis of large-scale electrophysiological data, named SyNCoPy, which stands for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g., time-lock analysis), frequency (e.g., power spectrum), and connectivity (e.g., coherence) domains. It enables user-friendly data analysis on both laptop-based and high-performance computing systems. SyNCoPy is designed to facilitate trial-parallel workflows (parallel processing of trials), making it an ideal tool for large-scale analysis of electrophysiological data. Based on parallel processing of trials, the software can support very large-scale datasets via innovative out-of-core computation techniques. It also provides seamless interoperability with other standard software packages through a range of file format importers and exporters and open file formats. The naming of the user functions closely follows the well-established FieldTrip framework, which is an open-source MATLAB toolbox for advanced analysis of electrophysiological data. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to explore longitudinal relationships between neurophysiological biomarkers and upper limb motor function recovery in stroke patients, focusing on electroencephalography (EEG) and transcranial magnetic stimulation (TMS) metrics. Methods: This longitudinal cohort study analyzed neurophysiological, clinical, and demographic data from 102 stroke patients enrolled in the DEFINE cohort. We investigated the associations between baseline and post-intervention changes in the EEG theta/alpha ratio (TAR) and TMS metrics with upper limb motor functionality, assessed using the outcomes of five tests: the Fugl-Meyer Assessment (FMA), Handgrip Strength Test (HST), Pinch Strength Test (PST), Finger Tapping Test (FTT), and Nine-Hole Peg Test (9HPT). Results: Our multivariate models identified that a higher baseline TAR in the lesioned hemisphere was consistently associated with poorer motor outcomes across all five assessments. Conversely, a higher improvement in the TAR was positively associated with improvements in FMA and 9HPT. Additionally, an increased TMS motor-evoked potential (MEP) amplitude in the non-lesioned hemisphere correlated with greater FMA-diff, while a lower TMS Short Intracortical Inhibition (SICI) in the non-lesioned hemisphere was linked to better PST improvements. These findings suggest the potential of the TAR and TMS metrics as biomarkers for predicting motor recovery in stroke patients. Conclusion: Our findings highlight the significance of the TAR in the lesioned hemisphere as a predictor of motor function recovery post-stroke and also a potential signature for compensatory oscillations. The observed relationships between the TAR and motor improvements, as well as the associations with TMS metrics, underscore the potential of these neurophysiological measures in guiding personalized rehabilitation strategies for stroke patients. [ABSTRACT FROM AUTHOR]

Background: Among all professions, teaching is significantly affected by psycho-social risks with approximately 33.33% of educators reporting work-related fatigue. Suggestopedia, an effective pedagogical approach developed in Bulgaria, claims to induce positive psychological and cognitive benefits in both teachers and students. In order to gather scientific evidence, given the above statement, we designed a methodology to detect fatigue in Suggestopedia teachers based on neurocognitive analysis and psychological assessment. Methods: An increase in the EEG theta and alpha band powers is considered among the most reliable markers of fatigue. The proposed methodology introduces a robust framework for fatigue analysis. Initially, the changes in EEG band powers using the resting state EEG activity before and after teaching are measured. Subsequently, validated psychological questionnaires are used to gain subjective feedback on fatigue. The study participants include a control group (traditional teachers) and the test group (suggestopedia teachers) to assess whether suggestopedia practice mitigates fatigue among teachers. Observations: In a pilot study, the EEG data was analyzed by evaluating the interrelations between EEG bands and the alpha–beta ratio. The results of the proposed study are expected to provide comprehensive analysis for the fatigue levels of teachers. In future research, our goal is to position the described methodology as a robust approach for evaluating cognitive and emotional states. [ABSTRACT FROM AUTHOR]

Nonverbal connection is an important aspect of everyday communication. For romantic partners, nonverbal connection is essential for establishing and maintaining feelings of closeness. EEG hyperscanning offers a unique opportunity to examine the link between nonverbal connection and neural synchrony among romantic partners. This current study used an EEG hyperscanning paradigm to collect frontal alpha asymmetry (FAA) signatures from 30 participants (15 romantic dyads) engaged in five different types of nonverbal connection that varied based on physical touch and visual contact. The results suggest that there was a lack of FAA while romantic partners were embracing and positive FAA (i.e., indicating approach) while they were holding hands, looking at each other, or doing both. Additionally, partners' FAA synchrony was greatest at a four second lag while they were holding hands and looking at each other. Finally, there was a significant association between partners' weekly negative feelings and FAA such that as they felt more negative their FAA became more positive. Taken together, this study further supports the idea that fleeting moments of interpersonal touch and gaze are important for the biological mechanisms that may underlie affiliative pair bonding in romantic relationships. [ABSTRACT FROM AUTHOR]

• Spatiotemporal standardization is proposed for the Kalman filter. • Standardized Kalman filter (SKF) tracked better than compared methods in simulations. • SKF localizes known originators of SEP accurately with numerical and real data. We introduce standardized Kalman filtering (SKF) as a new spatiotemporal method for tracking brain activity. Via the Kalman filtering scheme, the computational workload is low, and by spatiotemporal standardization, we reduce the depth bias of non-standardized Kalman filtering (KF). We describe the standardized KF methodology for spatiotemporal tracking from the Bayesian perspective. We construct a realistic simulation setup that resembles activity due to somatosensory evoked potential (SEP) to validate the proposed methodology before we run our tests using real SEP data. In the experiments, SKF was compared with standardized low-resolution brain electromagnetic tomography (sLORETA) and the non-standardized KF. SKF localized the cortical and subcortical SEP originators appropriately and tracked P20/N20 originators for investigated signal-to-noise ratios (25, 15, and 5 dB). sLORETA distinguished those for 25 and 15 dB suppressing the subcortical originators. KF tracked only the evolution of cortical activity but mislocalized it. The numerical results suggest that SKF inherits the estimation accuracy of sLORETA and traceability of KF while producing focal estimates for SEP originators. SKF could help study time-evolving brain activities and localize landmarks with a deep contributor or when there is no prior knowledge of evolution. [ABSTRACT FROM AUTHOR]

Despite numerous studies highlighting the impact of smells on humans, there has been a notable lack of attention to ambient smell within the discipline of architecture. To address the gaps, a systematic methodology was designed to investigate the impact of the indoor-built environment's smell on human emotions. In this experiment, 14 adults (7 women and 7 men) are exposed to the smell of jasmine and rotten orange peel in two identical rooms. The EEG analysis revealed increased theta band power in frontal regions and increased alpha power in frontal and occipital areas when participants were exposed to the jasmine scent. Gamma activity in the frontal regions increases with exposure to the smell of rotten orange peel. Participants reported that the smell of jasmine was pleasant, leading to pleasant emotions, interest, enjoyment and a sleepy state. However, the smell of rotten orange peel led to unpleasant emotions, discomfort and annoyance for participants. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) represents a multifaceted neuro-developmental state that presents significant difficulties in its early identification and intervention. This survey explores the recent advancements and methodologies in ASD detection leveraging Machine learning (ML), Deep Learning (DL), and Neuroimaging techniques. An extensive survey of literature between 2018 and 2023 reveals a paradigm shift in diagnostic approaches, emphasizing the integration of ML algorithms, like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and decision-making models, in conjunction with various neuro-imaging modalities like Magnetic Resonance Imaging (MRI), Electroencephalography (EEG), and Functional Near-Infrared Spectroscopy (fNIRS). These modalities facilitate the identification of distinctive biomarkers, behavioral patterns, and neural correlates associated with ASD. The survey also looks at potential ethical issues, the importance of early detection using ML-driven methodologies, and the changing diagnostic tool landscape that aims to offer timely and individualized interventions for people with ASD. The combination of these data demonstrates the revolutionary effect of ML, DL, and neuro-imaging in improving the accuracy of ASD detection, allowing access to additional potent intervention methods and a more thorough understanding of the neurobiology underlying the condition. [ABSTRACT FROM AUTHOR]

Situation awareness (SA) is important in many demanding tasks (e.g. driving). Assessing SA during training can indicate whether someone is ready to perform in the real world. SA is typically assessed by interrupting the task to ask questions about the situation or asking questions after task completion, assessing only momentary SA. An objective and continuous means of detecting SA is needed. We examined whether neurophysiological sensors are useful to objectively measure Level 3 SA (projection of events into the future) during a driving task. We measured SA by the speed at which participants responded to SA questions and the accuracy of responses. For EEG, beta and theta power were most sensitive to SA response time. For fNIRS, oxygenated haemoglobin (HbO) was most sensitive to accuracy. This is the first evidence to our knowledge that neurophysiological measures are useful for assessing Level 3 SA during an ecologically valid task. PRACTITIONER SUMMARY: We examine whether neurophysiological sensors are useful to objectively measure Level 3 situation awareness (SA) prediction during a driving task. EEG theta and beta, and fNIRS oxygenated haemoglobin were most sensitive to SA accuracy. This is evidence that neurophysiological measures can be used to assess hazard prediction (Level 3 SA). [ABSTRACT FROM AUTHOR]

(1) Background: Gamma-aminobutyric acid (GABA) is an amino acid and the primary inhibitory neurotransmitter in the brain. GABA has been shown to reduce stress and promote sleep. GABALAGEN (GBL) is the product of fermented fish collagen by Lactobacillus brevis BJ20 and Lactobacillus plantarum BJ21, naturally enriched with GABA through the fermentation process and characterized by low molecular weight. (2) Methods: The present study evaluated the GABAA affinity of GBL through receptor binding assay. The sedative effects of GBL were investigated through electroencephalography (EEG) analysis in an animal model of electro foot shock (EFS) stress-induced sleep disorder, and then we examined the expression of orexin and the GABAA receptor in the brain region using immunohistochemistry and an enzyme-linked immunosorbent assay (ELISA). (3) Results: We found that on the binding assay, GBL displayed high affinity to the GABAA receptor. Also, after treatment with GBL, the percentage of the total time in rapid eye movement (REM) and non-rapid eye movement (NREM) sleep was significantly and dose-dependently increased in EFS-induced rats. Consistent with behavioral results, the GBL-treated groups showed that the expression of GABAA receptor immune-positive cells in the VLPO was markedly and dose-dependently increased. Also, the GBL-treated groups showed that the expression of the orexin-A level in LH was significantly decreased. (4) Conclusions: GBL showed efficacy and potential to be used as an anti-stress therapy to treat sleep deprivation through the stimulation of GABAA receptors and the consequent inhibition of orexin activity. [ABSTRACT FROM AUTHOR]

Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective. It forms the basis for Brain-Computer Interfaces (BCIs) that provide a communication channel for individuals with neurological impairments, thereby empowering them to express themselves effectively. EEG-based BCIs, especially those adapted to decode imagined speech from EEG signals, represent a significant advancement in enabling individuals with speech disabilities to communicate through text or synthesized speech. By utilizing cognitive neurodevelopmental insights, researchers have been able to develop innovative approaches for interpreting EEG signals and translating them into meaningful communication outputs. To aid researchers in effectively addressing this complex challenge, this review article synthesizes key findings from state-of-the-art significant studies. It investigates into the methodologies employed by various researchers, including preprocessing techniques, feature extraction methods, and classification algorithms utilizing Deep Learning and Machine Learning approaches and their integration. Furthermore, the review outlines the potential avenues for future research, with the goal of advancing the practical implementation of EEG-based BCI systems for decoding imagined speech from a cognitive neurodevelopmental perspective. [ABSTRACT FROM AUTHOR]

Social attention, an important mechanism that orients people to social cues, is critical for the development of higher-ordered features of social cognition. Both endogenous (i.e. automatic and undirected) and exogenous (i.e. purposeful and directed) social attention is important for processing social features, yet there is limited work systematically addressing how different experimental manipulations modulate social attention. This study examined how endogenous and exogenous manipulations of a classic face inversion task influence ERP activity in adults (n = 71) and adolescent youth (n = 65). Results from Study 1 indicated a lack of task differences for P1 and N170 but a larger inversion effect for P3 when a social perceptual decision was required. Study 2 demonstrated developmental differences in the youth, such that youth and adults had opposite inversion effects for N170 and youth had no effect for the P3. These findings indicate that face perception neural markers are sensitive to exogenous decisions, with development still active in adolescence. This is important to consider when designing future studies, as task-based decisions may alter the neural responses to faces differentially by age. [ABSTRACT FROM AUTHOR]

Emerging evidence from neurophysiological brain vital sign studies show repeatable sensitivity to cumulative subconcussive impairments over a season of contact sports. The current study addressed the need for research comparing a low-contact control group to high-contact group. Importantly, the study also expanded the scope of neurophysiological changes related to repetitive head impacts to include female athletes in addition to male athletes. In total, 89 high school student athletes underwent 231 brain vital sign scans over a full calendar year. The results replicated prior subconcussive cognitive impairments (N400 delays) and sensory impairments (N100 amplitude reductions) in male athletes and demonstrated similar subconcussive impairments for the first time in female athletes. While there was no significant subconcussive difference between female and male athletes, female athletes show overall larger responses in general. The findings demonstrated that subconcussive impairments are detectable in a controlled experimental comparison for both female and male high school athletes. The study highlights the opportunity to monitor subconcussive changes in cognitive processing for both female and male athletes to help advance prevention, mitigation and management efforts aimed at reducing athletes' risk of potential long-term negative health outcomes related to cumulative exposure to repetitive head impacts. [ABSTRACT FROM AUTHOR]

In music ensemble performance, perception–action coupling enables the processing of auditory feedback from oneself and other players. However, improvised actions may affect this coupling differently from predetermined actions. This study used two‐person EEG to examine how pianists responded to altered pitch feedback to their own or their partner's actions while they alternated scores or improvised melodies. Feedback‐related negativity (FRN) response for self‐action was greater in scored than improvised conditions, indicating the enhanced action encoding by playing the score. However, subsequent P3a and P3b responses for self‐action were not different across score and improvisation. Further, the P3b response was greater when the two pianists exchanged similar types of melodies (i.e., both improvised or both scores) compared with different types of melodies, suggesting that later cognitive processes may be associated with the task relevance or level of jointness. The presence of the FRN and P3 complex in self‐generated improvised action points to the dynamic nature of performance monitoring even without preconceived action plans. In contrast, the FRN and P3 complex in partner‐generated improvised actions were subdued compared to the baseline, likely due to the unpredictable nature of the improvised actions of others. Finally, we found a tendency that higher trait empathy was associated with smaller self‐action FRN, possibly implying musicians' prioritization of joint goals. Overall, our results suggest that improvisation in a musical turn‐taking task may be distinct from score‐playing for the earlier processes of performance monitoring, whereas later processes might involve updating a joint representation of the musical context. [ABSTRACT FROM AUTHOR]

EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups. [ABSTRACT FROM AUTHOR]

Neuroimaging studies have reported the possibility of semantic neural decoding to identify specific semantic concepts from neural activity. This offers promise for brain-computer interfaces (BCIs) for communication. However, translating these findings into a BCI paradigm has proven challenging. Existing EEG-based semantic decoding studies often rely on neural activity recorded when a cue is present, raising concerns about decoding reliability. To address this, we investigate the effects of cue presentation on EEG-based semantic decoding. In an experiment with a clear separation between cue presentation and mental task periods, we attempt to differentiate between semantic categories of animals and tools in four mental tasks. By using state-of-the-art decoding analyses, we demonstrate significant mean classification accuracies up to 71.3% during cue presentation but not during mental tasks, even with adapted analyses from previous studies. These findings highlight a potential issue when using neural activity recorded during cue presentation periods for semantic decoding. Additionally, our results show that semantic decoding without external cues may be more challenging than current state-of-the-art research suggests. By bringing attention to these issues, we aim to stimulate discussion and drive advancements in the field toward more effective semantic BCI applications. [ABSTRACT FROM AUTHOR]

Background: Patients with migraine often experience not only headache pain but also cognitive dysfunction, particularly in attention, which is frequently overlooked in both diagnosis and treatment. The influence of these attentional deficits on the pain-related clinical characteristics of migraine remains poorly understood, and clarifying this relationship could improve care strategies. Methods: This study included 52 patients with migraine and 34 healthy controls. We employed the Attentional Network Test for Interactions and Vigilance–Executive and Arousal Components paradigm, combined with electroencephalography, to assess attentional deficits in patients with migraine, with an emphasis on phasic alerting, orienting, executive control, executive vigilance, and arousal vigilance. An extreme gradient boosting binary classifier was trained on features showing group differences to distinguish patients with migraine from healthy controls. Moreover, an extreme gradient boosting regression model was developed to predict clinical characteristics of patients with migraine using their attentional deficit features. Results: For general performance, patients with migraine presented a larger inverse efficiency score, a higher prestimulus beta-band power spectral density and a lower gamma-band event-related synchronization at Cz electrode, and stronger high alpha-band activity at the primary visual cortex, compared to healthy controls. Although no behavior differences in three basic attentional networks were found, patients showed magnified N1 amplitude and prolonged latency of P2 for phasic alerting-trials as well as an increased orienting evoked-P1 amplitude. For vigilance function, improvements in the hit rate of executive vigilance-trials were exhibited in controls but not in patients. Besides, patients with migraine exhibited longer reaction time as well as larger variability in arousal vigilance-trials than controls. The binary classifier developed by such attentional deficit features achieved an F1 score of 0.762 and an accuracy of 0.779 in distinguishing patients with migraine from healthy controls. Crucially, the predicted value available from the regression model involving attentional deficit features significantly correlated with the real value for the frequency of headache. Conclusions: Patients with migraine demonstrated significant attentional deficits, which can be used to differentiate migraine patients from healthy populations and to predict clinical characteristics. These findings highlight the need to address cognitive dysfunction, particularly attentional deficits, in the clinical management of migraine. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) poses a significant challenge in mental healthcare due to difficulties in accurate diagnosis and timely identification. This study explores the potential of machine learning models trained on EEG-based features for depression detection. Six models and six feature selection techniques were compared, highlighting the crucial role of feature selection in enhancing classifier performance. This study investigates the six feature selection methods: Elastic Net, Mutual Information (MI), Chi-Square, Forward Feature Selection with Stochastic Gradient Descent (FFS-SGD), Support Vector Machine-based Recursive Feature Elimination (SVM-RFE), and Minimal-Redundancy-Maximal-Relevance (mRMR). These methods were combined with six diverse classifiers: Logistic Regression, Support Vector Machine (SVM), Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM). The results demonstrate the substantial impact of feature selection on model performance. SVM-RFE with SVM achieved the highest accuracy (93.54%) and F1 score (95.29%), followed by Logistic Regression with an accuracy of 92.86% and F1 score of 94.84%. Elastic Net also delivered strong results, with SVM and Logistic Regression both achieving 90.47% accuracy. Other feature selection methods yielded lower performance, emphasizing the importance of selecting appropriate feature selection and machine learning algorithms. These findings suggest that careful selection and application of feature selection techniques can significantly enhance the accuracy of EEG-based depression detection. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience to diagnose neural disorders and analyse brain activity. However, ocular and myogenic artifacts from eye movements and facial muscle activity often contaminate EEG signals, compromising signal analysis accuracy. While deep learning models are a popular choice for denoising EEG signals, most focus on removing either ocular or myogenic artifacts independently. This paper introduces a novel EEG denoising model capable of handling the simultaneous occurrence of both artifacts. The model uses convolutional layers to extract spatial features and a fully connected layer to reconstruct clean signals from learned features. The model integrates the Adam optimiser, average pooling, and ReLU activation to effectively capture and restore clean EEG signals. It demonstrates superior performance, achieving low training and validation losses with a significantly reduced R R M S E value of 0.35 in both the temporal and spectral domains. A high cross-correlation coefficient of 0.94 with ground-truth EEG signals confirms the model's fidelity. Compared to the existing architectures and models (FPN, UNet, MCGUNet, LinkNet, MultiResUNet3+, Simple CNN, Complex CNN) across a range of signal-to-noise ratio values, the model shows superior performance for artifact removal. It also mitigates overfitting, underscoring its robustness in artifact suppression. [ABSTRACT FROM AUTHOR]

Background: A multi-method, multi-informant approach is crucial for evaluating attention-deficit/hyperactivity disorders (ADHD) in preschool children due to the diagnostic complexities and challenges at this developmental stage. However, most artificial intelligence (AI) studies on the automated detection of ADHD have relied on using a single datatype. This study aims to develop a reliable multimodal AI-detection system to facilitate the diagnosis of ADHD in young children. Methods: 78 young children were recruited, including 43 diagnosed with ADHD (mean age: 68.07 ± 6.19 months) and 35 with typical development (mean age: 67.40 ± 5.44 months). Machine learning and deep learning methods were adopted to develop three individual predictive models using electroencephalography (EEG) data recorded with a wearable wireless device, scores from the computerized attention assessment via Conners' Kiddie Continuous Performance Test Second Edition (K-CPT-2), and ratings from ADHD-related symptom scales. Finally, these models were combined to form a single ensemble model. Results: The ensemble model achieved an accuracy of 0.974. While individual modality provided the optimal classification with an accuracy rate of 0.909, 0.922, and 0.950 using the ADHD-related symptom rating scale, the K-CPT-2 score, and the EEG measure, respectively. Moreover, the findings suggest that teacher ratings, K-CPT-2 reaction time, and occipital high-frequency EEG band power values are significant features in identifying young children with ADHD. Conclusions: This study addresses three common issues in ADHD-related AI research: the utility of wearable technologies, integrating databases from diverse ADHD diagnostic instruments, and appropriately interpreting the models. This established multimodal system is potentially reliable and practical for distinguishing ADHD from TD, thus further facilitating the clinical diagnosis of ADHD in preschool young children. [ABSTRACT FROM AUTHOR]

Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary & non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database & 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods. [ABSTRACT FROM AUTHOR]

Electroencephalographic (EEG) source localization is a fundamental tool for clinical diagnoses and brain-computer interfaces. We investigate the impact of model complexity on reconstruction accuracy by comparing the widely used three-layer boundary element method (BEM) as an inverse method against a five-layer BEM accelerated by the fast multipole method (BEM-FMM) and coupled with adaptive mesh refinement (AMR) as forward solver. Modern BEM-FMM with AMR can solve high-resolution multi-tissue models efficiently and accurately. We generated noiseless 256-channel EEG data from 15 subjects in the Connectome Young Adult dataset, using four anatomically relevant dipole positions, three conductivity sets, and two head segmentations; we mapped localization errors across the entire grey matter from 4000 dipole positions. The average location error among our four selected dipoles is ∼5 m m (±2 m m) with an orientation error of ∼ 12 ∘ (± 7 ∘ ). The average source localization error across the entire grey matter is ∼9 m m (±4 m m), with a tendency for smaller errors on the occipital lobe. Our findings indicate that while three-layer models are robust under noiseless conditions, substantial localization errors (10–20 m m) are common. Therefore, models of five or more layers may be needed for accurate source reconstruction in critical applications involving noisy EEG data. [ABSTRACT FROM AUTHOR]

Mixed Reality (MR) technology possesses profound and extensive potential across a multitude of domains, including, but not limited to industry, healthcare, and education. However, prolonged use of MR devices to watch stereoscopic content may lead to visual fatigue. Since visual fatigue involves multiple brain regions, our study aims to explore the topological characteristics of brain networks derived from electroencephalogram (EEG) data. Because the Phase-Locked Value (PLV) is capable of effectively measuring the phase synchronization relationship between brain regions, it was calculated between all pairs of channels in both comfort and fatigue states. Subsequently, a sparse brain network was constructed based on PLV by applying an appropriate threshold. The node properties (betweenness centrality, clustering coefficient, node efficiency) and edge properties (characteristic path length) were calculated based on the corresponding brain network within specific frequency bands for both comfort and fatigue states. In analyzing the PLV of brain connectivity in comfort and fatigue states, a notable enhancement in brain connectivity is observed within the alpha, theta, and delta frequency bands during fatigue status. By analyzing the node and edge properties of brain networks, it is evident that the mean values of these properties in the fatigue state were higher than those in the comfort state. By analyzing the node and edge properties at a local level, the average difference in betweenness centrality, clustering coefficients, and nodal efficiency across the three EEG frequency bands was computed to find significant brain regions. The main findings are as follows: Betweenness centrality primarily differs in frontal and parietal regions, with minor involvement in temporal and central regions. The clustering Coefficient mainly varies in the frontal region, with slight differences being seen in the temporal and occipital regions. Nodal efficiency primarily varies in the frontal, temporal, and central regions, with minor differences being seen in the parietal and occipital regions. Edge property analysis indicates that there is a higher occurrence of long-distance connections among brain regions during the fatigue state, which reflects a loss of synaptic transmission efficiency on a global level. Our study plays a crucial role in understanding the neural mechanisms underlying visual fatigue, potentially providing insights that could be applied to high-demand cognitive fields where prolonged use of MR devices leads to visual fatigue. [ABSTRACT FROM AUTHOR]