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This study assessed the neural mechanisms and relative saliency of categorization for speech sounds and comparable graphemes (i.e., visual letters) of the same phonetic label. Given that linguistic experience shapes categorical processing, and letter-speech sound matching plays a crucial role during early reading acquisition, we hypothesized sound phoneme and visual grapheme tokens representing the same linguistic identity might recruit common neural substrates, despite originating from different sensory modalities. Behavioral and neuroelectric brain responses (ERPs) were acquired as participants categorized stimuli from sound (phoneme) and homologous letter (grapheme) continua each spanning a /da/-/ga/ gradient. Behaviorally, listeners were faster and showed stronger categorization of phoneme compared to graphemes. At the neural level, multidimensional scaling of the EEG revealed responses self-organized in a categorial fashion such that tokens clustered within their respective modality beginning ∼150-250 ms after stimulus onset. Source-resolved ERPs further revealed modality-specific and overlapping brain regions supporting phonetic categorization. Left inferior frontal gyrus and auditory cortex showed stronger responses for sound category members compared to phonetically ambiguous tokens, whereas early visual cortices paralleled this categorical organization for graphemes. Auditory and visual categorization also recruited common visual association areas in extrastriate cortex but in opposite hemispheres (auditory = left; visual = right). Our findings reveal both auditory and visual sensory cortex supports categorical organization for phonetic labels within their respective modalities. However, a partial overlap in phoneme and grapheme processing among occipital brain areas implies the presence of an isomorphic, domain-general mapping for phonetic categories in dorsal visual system., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Brain Research Organization (IBRO). Published by Elsevier Inc. All rights reserved.)

Background: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent years, advancements in the analysis of electroencephalogram (EEG) signals have garnered widespread attention, particularly in the area of seizure recognition., Methods: A novel hybrid deep learning approach that combines feature fusion for efficient seizure detection is proposed in this study. First, the Discrete Wavelet Transform (DWT) is applied to perform a five-level decomposition of the raw EEG signals, from which time-frequency and nonlinear features are extracted from the decomposed sub-bands. To eliminate redundant features, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) is employed to select the most distinctive features for fusion. Finally, seizure states are classified using Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM)., Results: The method was rigorously validated on the Bonn and New Delhi datasets. In the binary classification tasks, both the D-E group (Bonn dataset) and the Interictal-Ictal group (New Delhi dataset) achieved 100% accuracy, 100% sensitivity, 100% specificity, 100% precision, and 100% F1-score. In the three-class classification task A-D-E on the Bonn dataset, the model performed excellently, achieving 96.19% accuracy, 95.08% sensitivity, 97.34% specificity, 97.49% precision, and 96.18% F1-score. In addition, the proposed method was further validated on the larger and more clinically relevant CHB-MIT dataset, achieving average metrics of 98.43% accuracy, 97.84% sensitivity, 99.21% specificity, 99.14% precision, and an F1 score of 98.39%. Compared to existing literature, our method outperformed several recent studies in similar classification tasks, underscoring the effectiveness and advancement of the approach presented in this research., Conclusion: The findings indicate that the proposed method demonstrates a high level of effectiveness in detecting seizures, which is a crucial aspect of managing epilepsy. By improving the accuracy of seizure detection, this method has the potential to significantly enhance the process of diagnosing and treating individuals affected by epilepsy. This advancement could lead to more tailored treatment plans, timely interventions, and ultimately, better quality of life for patients., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

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.

Objective: 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., Methods: 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., Results: 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., Conclusions: 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., Significance: The optimal DLPFC-LICI effect was identified by the use of the sophisticated sham coil., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Preclinical behavioral testing is essential for drug discovery in neuropsychiatric disorders, yet translational challenges persist because of interspecies differences. Touchscreen-based behavioral tasks offer a promising solution for bridging this gap. These tasks provide flexibility across cognitive domains and species, facilitating rigorous comparisons. They complement traditional assays, offering improved face, predictive, and construct validity by mirroring human neuropsychological tests. Notably, nearly identical tasks have been validated in multiple species, enhancing translational potential. Recent studies demonstrate conserved neurocircuitry engagement in touchscreen tasks, supporting their relevance to human function and therapeutic development. The integration of electrophysiological measures, such as electroencephalography (EEG) and local field potential (LFP) recordings with touchscreen behavioral assays, enhances translational biomarker discovery and serves to elucidate neural circuit dynamics. Despite current limitations and the need for further validation, this approach offers a pathway to more efficient drug discovery. This review covers recent research describing the feasibility and benefits of EEG/LFP-touchscreen combination studies in rodents. While the field is still in its early stages, the promise of this research strategy is evident. Future efforts will likely focus on refining methodologies, identifying robust translational biomarkers, and expanding studies across species. Touchscreen-based platforms, integrated with electrophysiological measurements, hold significant potential to advance our understanding of neuropsychiatric disorders and accelerate the development of effective treatments., (© 2024 International Society for Neurochemistry.)

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., Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the Ethical Committee of the University Hospital of KU Leuven (UZ Leuven) under reference number S6254. All participants signed the informed consent form prior to their participation. Consent for publication: Consent for publication of individual data has been obtained from all participants of the study. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Objective. Signal denoising methods based on deep learning have been extensively adopted on electroencephalogram devices. However, they are unable to deploy on edge-based portable or wearable (P/W) electronics due to the high computational complexity of the existed models. To overcome such issue, we propose an edge-based lightweight Kalman filter network (EKFNet) that does not require manual prior knowledge estimation. Approach. Specifically, we construct a multi-scale feature fusion module to capture multi-scale feature information and implicitly compute the prior knowledge. Meanwhile, we design an adaptive gain estimation module that incorporates long short-term memory and sequential channel attention module to dynamically predict the Kalman gain. Furthermore, we present an optimization strategy utilizing operator fusion and constant folding to reduce the model's computational overhead and memory footprint. Main results . Experimental results show that the EKFNet reduces the sum of the square of the distances by at least 12% and improves the cosine similarity by at least 2.2% over the state-of-the-art methods. Besides, the model optimization shortens the inference time by approximately 3.3×. The code of our EKFNet is available athttps://github.com/cathnat/EKFNet. Significance. By integrating Kalman filter with deep learning, the approach addresses the parameter-setting challenges in traditional algorithms while reducing computational overhead and memory consumption, which exhibits a good tradeoff between algorithm performance and computing power., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Objective . Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test subjects. We improve performance using new regularization techniques during model training. Approach . We propose several graphical models to describe an EEG classification task. From each model, we identify statistical relationships that should hold true in an idealized training scenario (with infinite data and a globally-optimal model) but that may not hold in practice. We design regularization penalties to enforce these relationships in two stages. First, we identify suitable proxy quantities (divergences such as Mutual Information and Wasserstein-1) that can be used to measure statistical independence and dependence relationships. Second, we provide algorithms to efficiently estimate these quantities during training using secondary neural network models. Main results . We conduct extensive computational experiments using a large benchmark EEG dataset, comparing our proposed techniques with a baseline method that uses an adversarial classifier. We first show the performance of each method across a wide range of hyperparameters, demonstrating that each method can be easily tuned to yield significant benefits over an unregularized model. We show that, using ideal hyperparameters for all methods, our first technique gives significantly better performance than the baseline regularization technique. We also show that, across hyperparameters, our second technique gives significantly more stable performance than the baseline. The proposed methods require only a small computational cost at training time that is equivalent to the cost of the baseline. Significance . The high variability in signal distribution between subjects means that typical approaches to EEG signal modeling often require time-intensive calibration for each user, and even re-calibration before every use. By improving the performance of population models in the most stringent case of zero-shot subject transfer, we may help reduce or eliminate the need for model calibration., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Background and Objective: Numerous factors, including demographic characteristics, have been implicated in modulating individual sensitivity to propofol; however, substantial inter-individual differences persist even after accounting for these factors. This study thus aimed to explore whether pre-anesthesia brain functional network metrics correlate with an individual's sensitivity to propofol., Methods: A total of 54 subjects, including 30 patients and 24 healthy volunteers, were enrolled. Propofol was administered via a target-controlled infusion device, and anesthesia depth was monitored using a bispectral index monitor. Sensitivity to propofol was quantified using the induction time, measured from infusion onset to the bispectral index, which reached 60. Brain functional network metrics indicative of functional integration and segregation, centrality, and network resilience were computed from pre-anesthetic 60-channel EEG recordings. Linear regression analysis and machine learning predictive models were applied to evaluate the contribution of pre-anesthesia network metrics in predicting individual sensitivity to propofol., Results: Our analysis results revealed that subjects could be categorized into high- or low-sensitivity groups based on their induction time. Individuals with low sensitivity exhibited a greater network degree, clustering coefficient, global efficiency, and betweenness centrality, along with reduced modularity and assortativity coefficient in the alpha band. Furthermore, alpha band network metrics were significantly correlated with individual induction time. Leveraging these network metrics as features enabled the classification of individuals into high- or low-sensitivity groups with an accuracy of 94%., Conclusions: Using a clinically relevant endpoint that signifies the level of anesthesia suitable for surgical procedures, this study underscored the robust correlation between pre-anesthesia alpha-band network metrics and individual sensitivity to propofol in a cohort that included both patients and healthy volunteers. Our findings offer preliminary insights into the potential utility of pre-anesthetic brain status assessment to predict propofol sensitivity on an individual basis, which may help to develop a more accurate personalized anesthesia plan., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The intersection of neuroscience and technology hinges on the development of wearable devices and electrodes that can augment brain networks to improve cognitive capabilities such as learning and concentration. The capacity to enhance networks associated with these functions above baseline capabilities, holds the potential to benefit numerous individuals. The purpose of this study was to determine if electromagnetic field exposure modeled from physiological data would increase instances of flow in participants playing a computer game. The flow state refers to a subjective state of optimal performance experienced by individuals during a variety of tasks. For this study, participants (n = 39, 18-65 years, nfemale = 20) played the arcade game Snake for two ten-minute periods (each with a ten-minute rest period immediately following). For one of the trials, an electromagnetic field was applied bilaterally to the temporal lobes, with the other serving as the control. Brain activity was measured using quantitative electroencephalography, flow experience was measured using the Flow Short Scale and game play scores were also recorded. Results showed deceased beta 1 (12-16 Hz) activity in the left cuneus [t = 4.650, p < 0.01] and left precuneus [t = 4.603, p < 0.01], left posterior cingulate [t = 4.521, p < 0.05], insula [t = 4.234, p < 0.05], and parahippocampal gyrus [t = 4.113, p < 0.05] for trials when the field was active, compared to controls during rest periods. Results from the Flow Short Scale showed a statistically significant difference in mean "concentration ease" scores across electromagnetic field conditions, irrespective of difficulty [t = 2.131, p < 0.05]. In the EMF exposure trials, there was no discernible experience effect; participants with prior experience in the game Snake did not exhibit significantly better performance compared to those without prior experience. This anticipated effect was observed in control conditions. The comparable performance observed between novices and experienced players in the EMF condition indicate a noteworthy learning curve for novices. In all, these results provide evidence supporting the ability of EMF patterned from amygdaloid firing (6-20 Hz) to elicit neurological correlates of flow in brain regions previously reported in the literature, facilitate concentration, and subtly improve game scores. The possibility for wearable devices to support learning, concentration, and focus are discussed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Estimating intracranial current sources underlying the electromagnetic signals observed from extracranial sensors is a perennial challenge in non-invasive neuroimaging. Established solutions to this inverse problem treat time samples independently without considering the temporal dynamics of event-related brain processes. This paper describes current source estimation from simultaneously recorded magneto- and electro-encephalography (MEEG) using a recurrent neural network (RNN) that learns sequential relationships from neural data. The RNN was trained in two phases: (1) pre-training and (2) transfer learning with L1 regularization applied to the source estimation layer. Performance of using scaled labels derived from MEEG, magnetoencephalography (MEG), or electroencephalography (EEG) were compared, as were results from volumetric source space with free dipole orientation and surface source space with fixed dipole orientation. Exact low-resolution electromagnetic tomography (eLORETA) and mixed-norm L1/L2 (MxNE) source estimation methods were also applied to these data for comparison with the RNN method. The RNN approach outperformed other methods in terms of output signal-to-noise ratio, correlation and mean-squared error metrics evaluated against reference event-related field (ERF) and event-related potential (ERP) waveforms. Using MEEG labels with fixed-orientation surface sources produced the most consistent estimates. To estimate sources of ERF and ERP waveforms, the RNN generates temporal dynamics within its internal computational units, driven by sequential structure in neural data used as training labels. It thus provides a data-driven model of computational transformations from psychophysiological events into corresponding event-related neural signals, which is unique among MEEG source reconstruction solutions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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.

Background and Objective: Practicing mindfulness is a mental process toward interoceptive awareness, achieving stress reduction and emotion regulation through brain-function alteration. Literature has shown that electroencephalography (EEG)-derived connectivity possesses the potential to differentiate brain functions between mindfulness naïve and mindfulness experienced, where such quantitative differentiation could benefit telediagnosis for mental health. However, there is no prior guidance in model selection targeting on the mindfulness-experience prediction. Here we hypothesized that the EEG effective connectivity could reach a good prediction performance in mindfulness experiences with brain interpretability., Methods: We aimed at probing direct Directed Transfer Function (dDTF) to classify the participants' history of mindfulness-based stress reduction (MBSR), and aimed at optimizing the prediction accuracy by comparing multiple machine learning (ML) algorithms. Targeting the gamma-band effective connectivity, we evaluated the EEG-based prediction of the mindfulness experiences across 7 machine learning (ML) algorithms and 3 sessions (i.e., resting, focus-breathing, and body-scan)., Results: The support vector machine and naïve Bayes classifiers exhibited significant accuracies above the chance level across all three sessions, and the decision tree algorithm reached the highest prediction accuracy of 91.7 % with the resting state, compared to the classification accuracies with the other two mindful states. We further conducted the analysis on essential EEG channels to preserve the classification accuracy, revealing that preserving just four channels (F7, F8, T7, and P7) out of 19 yielded the accuracy of 83.3 %. Delving into the contribution of connectivity features, specific connectivity features predominantly located in the frontal lobe contributed more to classifier construction, which aligned well with the existing mindfulness literature., Conclusion: In the present study, we initiated a milestone of developing an EEG-based classifier to detect a person's mindfulness experience objectively. The prediction accuracy of the decision tree was optimal to differentiate the mindfulness experiences using the local resting-state EEG data. The suggested algorithm and key channels on the mindfulness-experience prediction may provide guidance for predicting mindfulness experiences using the EEG-based classification embedded in future wearable neurofeedback systems or plausible digital therapeutics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Nevertheless, the decoding of EEG signals presents considerable challenges owing to their inherent complexity, non-stationary characteristics, and low signal-to-noise ratio. Notably, deep learning-based classifiers have emerged as a prominent focus for addressing the EEG signal decoding process. This study introduces a novel deep learning classifier named the Attention Induced Dual Convolutional-Capsule Network (AIDC-CN) with the specific aim of accurately categorizing various motor imagination class labels. To enhance the classifier's performance, a dual feature extraction approach leveraging spectrogram and brain connectivity networks has been employed, diversifying the feature set in the classification task. The main highlights of the proposed AIDC-CN classifier includes the introduction of a dual convolution layer to handle the brain connectivity and spectrogram features, addition of a novel self-attention module (SAM) to accentuate the relevant parts of the convolved spectrogram features, introduction of a new cross-attention module (CAM) to refine the outputs obtained from the dual convolution layers and incorporation of a Gaussian Error Linear Unit (GELU) based dynamic routing algorithm to strengthen the coupling among the primary and secondary capsule layers. Performance analysis undertaken on four public data sets depict the superior performance of the proposed model with respect to the state-of-the-art techniques. The code for this model is available at https://github.com/RiteshSurChowdhury/AIDC-CN., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

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., Competing Interests: Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of the Chinese PLA General Hospital in accordance with the ethical principles of the Declaration of Helsinki (2023 − 460). Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

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., Competing Interests: Declarations Ethics approval and consent to participate This study was approved by the Research Ethics Committee of the National Health Research Institutes in Taiwan (EC1070401-F). Written informed consent from parents and assent from children were obtained prior to study entry. Consent for publication Written informed consents from participating parents and assents from children were obtained. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

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.

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., (© 2024. The Author(s).)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

High levels of test anxiety can cause negative emotional interference and have adverse effects in multiple aspects. It is currently unclear whether acute aerobic exercise can effectively reduce a series of negative emotions triggered by test anxiety. This study investigates the impact of 30 min of moderate-intensity aerobic exercise on the subjective emotional responses and frontal alpha asymmetry (FAA) in individuals with high test anxiety. Forty-four college students with high test anxiety were randomly assigned to an aerobic exercise group and a control group. Each group underwent two tests, involving subjective scale measurements and resting-state EEG recordings, with a seven-day interval between tests. Results showed that after 30 min of moderate-intensity acute aerobic exercise, the intervention group had significantly reduced scores in subjective negative emotional experiences, and FAA indicators significantly increased. The study suggests that acute aerobic exercise can enhance the emotional regulation ability of college students with high test anxiety and reduce their negative emotions., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: Predicting an individual's response to antidepressant medication remains one of the most challenging tasks in the treatment of major depressive disorder (MDD). Our objective was to use the large EMBARC study database to develop an electroencephalography (EEG)-based method to predict response to antidepressant treatment., Methods: Pre-treatment EEG data were collected from study participants treated with either sertraline (N = 105), placebo (N = 119), or bupropion (N = 35). After preprocessing, the robust exact low-resolution electromagnetic tomography (ReLORETA) brain source localization method was used to reconstruct the source signals in 54 brain regions. Connectivity between regions was determined using symbolic transfer entropy (STE). A convolutional neural network (CNN) classified participants as responders or non-responders to each treatment., Results: Classification accuracy was 91.0%, 95.4%, and 86.8% for sertraline, placebo, and bupropion, respectively. The most highly predictive features were connectivity between i) the anterior cingulate cortex and superior parietal lobule (alpha frequency), ii) the anterior cingulate cortex and orbitofrontal area (beta frequency), and iii) the orbitofrontal area and anterior cingulate cortex (gamma frequency)., Conclusion: CNN analysis of EEG connectivity may accurately predict response to sertraline, bupropion, and placebo., Significance: The suggested method may offer clinicians an accessible and cost-effective tool for speedy treatment and helps pharmaceutical firms to test new antidepressants efficiently., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

The late positive potential (LPP) is an ERP component commonly used to study emotional processes and has been proposed as a neuroaffective biomarker for research and clinical uses. These applications, however, require standardized procedures for elicitation and ERP data processing. We evaluated the impact of different EEG preprocessing steps on the LPP's data quality and statistical power. Using a diverse sample of 158 adults, we implemented a multiverse analytical approach to compare preprocessing pipelines that progressively incorporated more steps: artifact detection and rejection, bad channel interpolation, and bad segment deletion. We assessed each pipeline's effectiveness by computing the standardized measurement error (SME) and conducting simulated experiments to estimate statistical power in detecting significant LPP differences between emotional and neutral images. Our findings highlighted that artifact rejection is crucial for enhancing data quality and statistical power. Voltage thresholds to reject trials contaminated by artifacts significantly affected SME and statistical power. Once artifact detection was optimized, further steps provided minor improvements in data quality and statistical power. Importantly, different preprocessing pipelines yielded similar outcomes. These results underscore the robustness of the LPP's affective modulation to preprocessing choices and the critical role of effective artifact management. By refining and standardizing preprocessing procedures, the LPP can become a reliable neuroaffective biomarker, supporting personalized clinical interventions for affective disorders., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The classification of handwritten letters from invasive neural signals has lately been subject of research to restore communication abilities in people with limited movement capacities. This study explores the classification of ten letters (a,d,e,f,j,n,o,s,t,v) from non-invasive neural signals of 20 participants, offering new insights into the neural correlates of handwriting. Letters were classified with two methods: the direct classification from low-frequency and broadband electroencephalogram (EEG) and a two-step approach comprising the continuous decoding of hand kinematics and the application of those in subsequent classification. The two-step approach poses a novel application of continuous movement decoding for the classification of letters from EEG. When using low-frequency EEG, results show moderate accuracies of 23.1% for ten letters and 39.0% for a subset of five letters with highest discriminability of the trajectories. The two-step approach yielded significantly higher performances of 26.2% for ten letters and 46.7% for the subset of five letters. Hand kinematics could be reconstructed with a correlation of 0.10 to 0.57 (average chance level: 0.04) between the decoded and original kinematic. The study shows the general feasibility of extracting handwritten letters from non-invasively recorded neural signals and indicates that the proposed two-step approach can improve performances. As an exploratory investigation of the neural mechanisms of handwriting in EEG, we found significant influence of the written letter on the low-frequency components of neural signals. Differences between letters occurred mostly in central and occipital channels. Further, our results suggest movement speed as the most informative kinematic for the decoding of short hand movements., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Auditory Attention Detection (AAD) aims to detect the target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural networks designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0% and 79.6% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Studies using source localization results have shown that cortical involvement increased in treadmill walking with brain-computer interface (BCI) control. However, the reorganization of cortical functional connectivity in treadmill walking with BCI control is largely unknown. To investigate this, a public dataset, a mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface, was used. The electroencephalography (EEG)-coupling strength of the between-region and within-region during the continuous self-determinant movements of lower limbs were analyzed. The time-frequency cross-mutual information (TFCMI) method was used to calculate the coupling strength. The results showed the frontal-occipital connection increased in the gamma and delta bands (the threshold of the edge was >0.05) during walking with BCI, which may be related to the effective communication when subjects adjust their gaits to control the avatar. In walking with BCI control, the results showed theta oscillation within the left-frontal, which may be related to error processing and decision making. We also found that between-region connectivity was suppressed in walking with and without BCI control compared with in standing states. These findings suggest that walking with BCI may accelerate the rehabilitation process for lower limb stroke.

Engaging in dialog requires interlocutors to coordinate sending and receiving linguistic signals to build a discourse based upon interpretations and perceptions interconnected with a range of emotions. Conversing in a foreign language may induce emotions such as anxiety which influence the quality communication. The neural processes underpinning these interactions are crucial to understanding foreign language anxiety (FLA). Electroencephalography (EEG) studies reveal that anxiety is often displayed via hemispheric frontal alpha asymmetry (FAA). To examine the neural mechanisms underlying FLA, we collected self-reported data on the listening and speaking sections of the Second language skill specific anxiety scale (L2AS) over behavioral, cognitive, and somatic domains and recorded EEG signals during participation in word chain turn-taking activities in first (L1, Chinese) and second (L2, English) languages. Regression analysis showed FAA for the L2 condition was a significant predictor primarily of the behavioral and somatic domains on the L2AS speaking section. The results are discussed along with implications for improving communication during L2 interactions., Competing Interests: Conflict of Interest The authors declare no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Objective: Auditory-driven gamma synchrony (GS) is linked to the function of a specific cortical circuit based on a parvalbumin+ and pyramidal neuron loop. This circuit is impaired in neuropsychiatric conditions (i.e. schizophrenia, Alzheimer's disease, stroke etc.) and its relevance in clinical practice is increasingly being recognized. Auditory stimulation at a typical gamma frequency of 40 Hz can be applied as a 'stress test' of excitation/inhibition (E/I) of the entire cerebral cortex, to drive GS and record it with magnetoencephalography (MEG) or high-density electroencephalography (EEG). However, these two techniques are costly and not widely available. Therefore, we assessed whether a single EEG electrode is sufficient to provide an accurate estimate of the auditory-driven GS level of the entire cortical surface while expecting the highest correspondence in the auditory and somatosensory cortices., Methods: We measured simultaneous EEG-MEG in 29 healthy subjects, utilizing 3 EEG electrodes (C4, F4, O2) and a full MEG setup. Recordings were performed during binaural exposure to auditory gamma stimulation and during silence. We compared GS measurement of each of the three EEG electrodes separately against full MEG mapping. Time-resolved phase locking value (PLVt) was computed between EEG signals and cortex reconstructed MEG signals., Results: During auditory stimulation, but not at rest, EEG captures a significant amount of GS, especially from both auditory cortices and motor-premotor regions. This was especially true for frontal (C4) and central electrodes (F4)., Discussion and Conclusions: While hd-EEG and MEG are necessary for accurate spatial mapping of GS at rest and during auditory stimulation, a single EEG channel is sufficient to detect the global level of GS. These results have great translational potential for mapping GS in standard clinical settings., Competing Interests: Declaration of competing interest The authors have no conflict of interest to disclose., (Copyright © 2024. Published by Elsevier Inc.)

Schizophrenia (SZ) is a severe mental disorder characterised by disruptions in cognition, behaviour, and perception, significantly impacting an individual's life. Traditional SZ diagnosis methods are labour-intensive and prone to errors. This study presents an innovative automated approach for detecting SZ acquired through electroencephalogram (EEG) sensor signals, aiming to improve diagnostic efficiency and accuracy. We utilised Fast Independent Component Analysis to remove artefacts from raw EEG sensor data. A novel Automated Log Energy-based Empirical Wavelet Reconstruction (ALEEWR) technique was introduced to reconstruct decomposed modes based on their variability, ensuring effective extraction of meaningful EEG signatures. Cepstral-based features-cepstral activity, cepstral mobility, and cepstral complexity-were used to capture the power, rate of change, and irregularity of the cepstrum of preprocessed EEG signals. ANOVA-based feature selection was applied to refine these features before classification using the K-Nearest Neighbour (KNN) algorithm. Our approach achieved an exceptional accuracy of 99.4%, significantly surpassing previous methods. The proposed ALEEWR and cepstral analysis demonstrated high precision, sensitivity, and specificity in the automated diagnosis of schizophrenia. This study introduces a highly accurate and efficient method for SZ detection using EEG technology. The proposed techniques offer significant improvements in diagnostic accuracy, with potential implications for enhancing SZ diagnosis and patient care through automated systems.

Few studies have examined neural correlates of late talking in toddlers, which could aid in understanding etiology and improving diagnosis of developmental language disorder (DLD). Greater frontal gamma activity has been linked to better language skills, but findings vary by risk for developmental disorders, and this has not been investigated in late talkers. This study examined whether frontal gamma power (30-50 Hz), from baseline-state electroencephalography (EEG), was related to DLD risk (categorical late talking status) and a continuous measure of expressive language in n = 124 toddlers. Frontal gamma power was significantly associated with late talker status when controlling for demographic factors and concurrent receptive language (β = 1.96, McFadden's Pseudo R 2  = 0.21). Demographic factors and receptive language did not significantly moderate the association between frontal gamma power and late talker status. A continuous measure of expressive language ability was not significantly associated with gamma (r = -0.07). Findings suggest that frontal gamma power may be useful in discriminating between groups of children that differ in DLD risk, but not for expressive language along a continuous spectrum of ability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Social motivation, the human desire to engage with others, is likely to underlie higher levels of social cognition and the formation of interpersonal relationships. Yet, this topic has been understudied in adolescents despite the critical developmental and maturational changes that occur during this period and the relevance of social motivation to clinical and neurodevelopmental disorders. Using electroencephalography (EEG) and an implicit-association paradigm (Choose-A-Movie Task; Dubey et al., 2015), we examined how brain responses underlying socially motivated decisions informed future decisions in 54 youth (aged 10-14 years) and 50 young adults (aged 18-33 years). As the first study to use this task during EEG recording, we implemented time-frequency analyses and a trial-by-trial dynamic statistical approach. Results suggested that both age groups preferred low-effort choices and increasingly preferred nonsocial choices over time. P3 amplitude also increased over time and was sensitive to effortful decisions, particularly for adults, but not social content. Both groups showed larger leftward frontal alpha asymmetry (FAA) during nonsocial feedback, and FAA predicted future decisions differently for adults than youth. The current study highlights FAA and trial-by-trial analyses as useful tools in understanding the neural mechanisms underlying socially motivated decisions, which differ across development, time, and individuals., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Objective: Schizophrenia (SCZ) is a globally prevalent, severe chronic mental disorder, with cognitive dysfunction being one of its core symptoms. Notably, overweight is exceedingly common among individuals with SCZ, and overweight can also impact cognitive function. Therefore, the relationship between overweight and cognition in SCZ is a clinical issue that is in need of research attention., Methods: This study enrolled 77 patients with SCZ, including 36 overweight and 41 non-overweight patients. The Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity, while cognitive functions were evaluated using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Electroencephalography (EEG) testing was performed, with power spectral analysis conducted across various frequency bands (δ, θ, α, β, low γ, and high γ)., Results: Compared to non-overweight SCZ patients, those overweight exhibited significantly lower RBANS total and index scores in immediate memory, visuospatial/constructional abilities, and delayed memory. EEG spectral analysis revealed that overweight SCZ patients demonstrated significantly lower oscillation power ratios in the β, low γ, and high γ frequency bands compared to their non-overweight counterparts. Correlation analyses indicated a significant positive relationship between β wave activity and RBANS total scores among overweight SCZ patients, suggesting that reduced β power correlates with more severe cognitive dysfunction., Conclusion: Our findings indicate that overweight SCZ patients experience more severe cognitive impairments in a resting state than those who are not overweight, with significant differences in EEG spectrum observed in the β and γ frequency bands. Additionally, our study establishes a correlation between various EEG spectrum dimensions and cognition. This research highlights the effects of overweight on cognition in individuals with SCZ. Additionally, employing EEG technology to study cognitive function in overweight SCZ patients can offer valuable electrophysiological insights., Competing Interests: Declaration of competing interest The authors declare that this study was conducted in the absence of any economic and financial relationships and competing relationships that could be interpreted as potential conflicts of interest, etc., that might influence the work reported herein., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Understanding how spontaneous brain activity influences the response to neurostimulation is crucial for the development of neurotherapeutics and brain-computer interfaces. Localized brain activity is suggested to influence the response to neurostimulation, but whether fast-fluctuating (i.e., tens of milliseconds) large-scale brain dynamics also have any such influence is unknown. By stimulating the prefrontal cortex using combined transcranial magnetic stimulation (TMS) and electroencephalography, we examined how dynamic global brain state patterns, as defined by microstates, influence the magnitude of the evoked brain response. TMS applied during what resembled the canonical Microstate C was found to induce a greater evoked response for up to 80 ms compared with other microstates. This effect was found in a repeated experimental session, was absent during sham stimulation, and was replicated in an independent dataset. Ultimately, ongoing and fast-fluctuating global brain states, as probed by microstates, may be associated with intrinsic fluctuations in connectivity and excitation-inhibition balance and influence the neurostimulation outcome. We suggest that the fast-fluctuating global brain states be considered when developing any related paradigms., Competing Interests: D.M.B. has received in-kind equipment support for investigator-initiated studies from MagVenture; he has received research support and in-kind equipment support for an investigator-initiated study from BrainsWay and is the site principal investigator for three sponsor-initiated studies for BrainsWay; he has received medication supplies for an investigator-initiated trial from Indivior; and he has participated on an advisory board for Janssen. Z.J.D. has received research and equipment in-kind support for an investigator-initiated study through BrainsWay and MagVenture. A.K., P.D., M.D.G., S.M., and F.F. report no biomedical financial interests or potential competing financial interests., (Copyright © 2024 the authors.)

Musical emotions have received increasing attention over the years. To better recognize the emotions by brain-computer interface (BCI), the random music-playing and sequential music-playing experimental paradigms are proposed and compared in this paper. Two experimental paradigms consist of three positive pieces, three neutral pieces and three negative pieces of music. Ten subjects participate in two experimental paradigms. The features of electroencephalography (EEG) signals are firstly analyzed in the time, frequency and spatial domains. To improve the effect of emotion recognition, a recognition model is proposed with the optimal channels selecting by Pearson's correlation coefficient, and the feature fusion combining differential entropy and wavelet packet energy. According to the analysis results, the features of sequential music-playing experimental paradigm are more different among three emotions. The classification results of sequential music-playing experimental paradigm are also better, and its average results of positive, neutral and negative emotions are 78.53%, 72.81% and 77.35%, respectively. The more obvious the changes of EEG induced by the emotions, the higher the classification accuracy will be. After analyzing two experimental paradigms, a better way for music to induce the emotions can be explored. Therefore, our research offers a novel perspective on affective BCIs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective . Over the last decades, error-related potentials (ErrPs) have repeatedly proven especially useful as corrective mechanisms in invasive and non-invasive brain-computer interfaces (BCIs). However, research in this context exclusively investigated the distinction of discrete events into correct or erroneous to the present day. Due to this predominant formulation as a binary classification problem, classical ErrP-based BCIs fail to monitor tasks demanding quantitative information on error severity rather than mere qualitative decisions on error occurrence. As a result, fine-tuned and natural feedback control based on continuously perceived deviations from an intended target remains beyond the capabilities of previously used BCI setups. Approach. To address this issue for future BCI designs, we investigated the feasibility of regressing rather than classifying error-related activity non-invasively from the brain. Main results. Using pre-recorded data from ten able-bodied participants in three sessions each and a multi-output convolutional neural network, we demonstrated the above-chance regression of ongoing target-feedback discrepancies from brain signals in a pseudo-online fashion. In a second step, we used this inferred information about the target deviation to correct the initially displayed feedback accordingly, reporting significant improvements in correlations between corrected feedback and target trajectories across feedback conditions. Significance. Our results indicate that continuous information on target-feedback discrepancies can be successfully regressed from cortical activity, paving the way to increasingly naturalistic, fine-tuned correction mechanisms for future BCI applications., (Creative Commons Attribution license.)

Grounding, a therapeutic technique involving direct contact with the earth, has been proposed by various studies to potentially have beneficial effects on pressure, sleep quality, stress, inflammation, and mood. However, the scientific evidence supporting its sedative effects remains incomplete. This study examined the sedative effectiveness of an earthing mat on sleep quality and investigated the underlying neural mechanisms using electroencephalography (EEG) analysis in rodents, focusing on orexin and superoxide dismutase (SOD) levels in the brain. Rats were randomly assigned to four groups: the naïve normal group (Nor), the group exposed to an earthing mat for 7 days (A-7D), the group exposed to an earthing mat for 21 days (A-21D), and the group exposed to an electronic blanket for 21 days (EM). EEG results revealed that the A-21D group exhibited significantly reduced wake time and increased rapid eye movement (REM), non-rapid eye movement (NREM), and total sleep time compared to the Nor group ( p < 0.05). Moreover, the A-21D group demonstrated a significant increase in NREM sleep ( p < 0.001), REM sleep ( p < 0.01), and total sleep time ( p < 0.001), along with a decrease in wake time compared to the EM group ( p < 0.001). The orexin level in the A-21D group was significantly lower compared to the Nor group ( p < 0.01), while SOD1 expression was markedly elevated in the A-21D group compared to the Nor group ( p < 0.001). These results suggest that the earthing mat may represent a promising new method for promoting sleep quality and could serve as an effective therapeutic technique.

A brain-computer interface could control a bionic hand by interpreting electroencephalographic (EEG) signals associated with wrist extension (WE) and wrist flexion (WF) movements. Misinterpretations of the EEG may stem from variations in the force, speed and range of these movements. To address this, we designed, constructed and tested a novel dynamometer, the IsoReg, which regulates WE and WF movements during EEG recording experiments. The IsoReg restricts hand movements to isometric WE and WF, controlling their speed and range of motion. It measures movement force using a dual-load cell system that calculates the percentage of maximum voluntary contraction and displays it to help users control movement force. Linearity and measurement accuracy were tested, and the IsoReg's performance was evaluated under typical EEG experimental conditions with 14 participants. The IsoReg demonstrated consistent linearity between applied and measured forces across the required force range, with a mean accuracy of 97% across all participants. The visual force gauge provided normalised force measurements with a mean accuracy exceeding 98.66% across all participants. All participants successfully controlled the motor tasks at the correct relative forces (with a mean accuracy of 89.90%) using the IsoReg, eliminating the impact of inherent force differences between typical WE and WF movements on the EEG analysis. The IsoReg offers a low-cost method for measuring and regulating movements in future neuromuscular studies, potentially leading to improved neural signal interpretation.

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document., (© 2024. The Author(s).)

This study examined the effect of attention on auditory processing in autistic individuals. Electroencephalography data were recorded during two attention conditions (passive and active) from 24 autistic adults and 24 neurotypical controls, ages 17-30 years. The passive condition involved only listening to the clicks and the active condition involved a button press following single clicks in a modified paired-click paradigm. Participants completed the Adolescent/Adult Sensory Profile and the Social Responsiveness Scale 2. The autistic group showed delayed N1 latencies and reduced evoked and phase-locked gamma power compared to neurotypical peers across both clicks and conditions. Longer N1 latencies and reduced gamma synchronization predicted greater social and sensory symptoms. Directing attention to auditory stimuli may be associated with more typical neural auditory processing in autism., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

As depressive symptom is considered a prodrome, a risk factor for progression from mild cognitive impairment (MCI) to dementia, improving depressive symptoms should be considered a clinical priority in patients with MCI undergoing transcranial direct current stimulation (tDCS) intervention. We aimed to comprehensively evaluate the efficacy of the home-based and remotely monitored tDCS in patients with both MCI and depression, by integrating cognitive, psychological, and electrophysiological indicators. In a 6-week, randomized, double blind, and sham-controlled study, 37 community-dwelling patients were randomly assigned to either an active or a sham stimulation group, and received 30 home-based sessions of 2 mA tDCS for 30 min with the anode located over the left and cathode over the right dorsolateral prefrontal cortex. We measured depressive symptoms, neurocognitive function, and resting-state electroencephalography. In terms of effects of both depressive symptoms and cognitive functions, active tDCS was not significantly different from sham tDCS. However, compared to sham stimulation, active tDCS decreased and increased the activation of delta and beta frequencies, respectively. Moreover, the increase in beta activity was correlated with the cognitive enhancement only in the active group. It was not possible to reach a definitive conclusion regarding the efficacy of tDCS on depression and cognition in patients with both MCI and depression. Nevertheless, the relationship between the changes of electrophysiology and cognitive performance suggests potential neuroplasticity enhancement implicated in cognitive processes by tDCS., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: The therapeutic potential of phytoncide fragrances may be optimal for patients with mild cognitive impairment (MCI) that display complex symptomatology. This study aimed to explore the clinical value of phytoncide by evaluating its electrophysiological effects in patients with MCI. Materials and Methods: This was a double-blind, randomized controlled trial. A total of 24 community-dwelling patients were randomly assigned to either a phytoncide or no-odor group. Participants wore a dental mask, for 30 min at rest that had either the fragrance stimulus or water added to it. The quantitative electroencephalography (EEG) during the resting state was recorded before and after a single intervention. Results: There were significant interaction effects in absolute EEG-power values in the occipital ( F  = 6.52, p  = 0.018) and parietal ( F  = 5.41, p  = 0.030) left hemisphere at β frequency. Phytoncide odor significantly decreased low and high β activity in the occipital (corrected p  = 0.009) and parietal (corrected p  = 0.047) left hemisphere, respectively. In source localization, phytoncide odor significantly decreased deep source activation in the left inferior and middle frontal gyri at β 2 frequency band compared with the no-odor group (threshold = 4.25, p  < 0.05). Conclusions: Reductions in β, indicative of anxiety, depression, and stress, suggest relief from emotion-related symptoms that are common in patients with MCI. Trial Registration: Clinical Trials Registry Korea (registration: KCT0007317).

This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machine learning algorithms, our method autonomously extracts the necessary features for network training from EEG recordings. The proposed model is a ten-layered CNN that contains a max pooling layer, a Global Average Pooling layer, four convolution layers, two dropout layers for overfitting prevention, and two fully connected layers. The efficiency of the suggested method was assessed using the ten-fold-cross validation technique and the EEG records of 14 healthy subjects and 14 SZ patients. The obtained mean accuracy score was 99.18 %. To confirm the high mean accuracy attained, we tested the model on unseen data with a near-perfect accuracy score (almost 100 %). In addition, the results we obtained outperform numerous other comparable works., Competing Interests: Declaration of competing interest The author declares that they have no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This study aims to develop a detection method based on morphological features of spike-wave (SW) patterns in the EEG of epilepsy patients and evaluate the effect of cathodal transcranial direct current stimulation (ctDCS) treatment. The proposed method is based on several simple features describing the shape of SW patterns and their synchronous occurrence on at least two EEG channels. High sensitivity, specificity and selectivity values were achieved for each patient and condition. ctDCS resulted in a significant reduction in the number of detected patterns, a decrease in spike duration and amplitude, and an increased spike mobility. The proposed method allows efficient identification of SW patterns regardless of brain condition, although the recruitment of patterns may be modified by ctDCS. This method can be useful in the clinical evaluation of ctDCS effects.

Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility. In this paper, we developed a wearable monitoring system that integrates both EEGs and GSR. The novelty of our proposed device is that it only requires one channel to acquire both physiological signals. Through sensor fusion, we achieved an improved accuracy, lower cost, and improved ease of use. We tested the proposed system experimentally on twenty human subjects. We estimated the power spectrum of the EEG signals and utilized five machine learning classifiers to differentiate between two levels of mental stress. Furthermore, we investigated the optimum electrode location on the scalp when using only one channel. Our results demonstrate the system's capability to classify two levels of mental stress with a maximum accuracy of 70.3% when using EEGs alone and 84.6% when using fused EEG and GSR data. This paper shows that stress detection is reliable using only one channel on the prefrontal and ventrolateral prefrontal regions of the brain.

The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain-computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods.

Classic psychedelics are able to profoundly alter the state of consciousness and lead to acute experiences of ego dissolution - the blurring of the distinction between representations of self and the external world. However, whether repeated use of psychedelics is associated with more prolonged and permanent modifications to the concept of self remains to be investigated. Therefore, we conducted a preregistered, cross-sectional study in which experienced psychedelics users (15 or more lifetime experiences with psychedelics; N = 56) were compared to nonusers (N = 57) in terms of neural reactivity to a Self-name (i.e., each participant's own name) stimulus, which is known to robustly activate a representation of self. Two control stimuli were additionally used: an Other-name stimulus, as a passive control condition in which no reaction was required, and a Target-name stimulus, to which participants provided a manual response and which thus constituted an active control condition. Analysis of the amplitude of the P300 ERP component evoked by the Self- or Target-names revealed no difference between the psychedelics users and nonusers. However, psychedelic users exhibited increased P300 amplitude during perception of Other-names. In addition, in comparison to nonusers, psychedelics users exhibited a smaller increase in P300 amplitude when processing the task-relevant Target-names (in relation to both Self- and Other-names). Therefore, our data suggests that regular naturalistic use of psychedelics may not be related to long-term changes in the representation of self, but it might potentially affect the allocation of attentional resources to task-relevant stimuli., (© 2024 Society for Psychophysiological Research.)

The classification of motor imagery (MI) using Electroencephalography (EEG) plays a pivotal role in facilitating communication for individuals with physical limitations through Brain-Computer Interface (BCI) systems. Recent strides in Attention-Based Networks (ATN) have showcased remarkable performance in EEG signal classification, presenting a promising alternative to conventional Convolutional Neural Networks (CNNs). However, while CNNs have been extensively analyzed for their resilience against adversarial attacks, the susceptibility of ATNs in comparable scenarios remains largely unexplored. This paper aims to fill this gap by investigating the robustness of ATNs in adversarial contexts. We propose a high-performing attention-based deep learning model specifically designed for classifying Motor Imagery (MI) brain signals extracted from EEG data. Subsequently, we conduct a thorough series of experiments to assess various attack strategies targeting ATNs employed in EEG-based BCI tasks. Our analysis utilizes the widely recognized BCI Competition 2a dataset to demonstrate the effectiveness of attention mechanisms in BCI endeavors. Despite achieving commendable classification results in terms of accuracy (87.15%) and kappa score (0.8287), our findings reveal the vulnerability of attention-based models to adversarial manipulations (accuracy: 9.07%, kappa score: -0.21), highlighting the imperative for bolstering the robustness of attention architectures for EEG classification tasks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Transcranial magnetic stimulation (TMS) to the dorsolateral prefrontal cortex (dlPFC) is an effective treatment for depression, but the neural effects after TMS remains unclear. TMS paired with electroencephalography (TMS-EEG) can causally probe these neural effects. Nonetheless, variability in single pulse TMS-evoked potentials (TEPs) across dlPFC subregions, and potential artifact induced by muscle activation, necessitate detailed mapping for accurate treatment monitoring., Objective: To characterize early TEPs anatomically and temporally (20-50 ms) close to the TMS pulse (EL-TEPs), as well as associated muscle artifacts (<20 ms), across the dlPFC. We hypothesized that TMS location and angle influence EL-TEPs, and specifically that conditions with larger muscle artifact may exhibit lower observed EL-TEPs due to over-rejection during preprocessing. Additionally, we sought to determine an optimal group-level TMS target and angle, while investigating the potential benefits of a personalized approach., Methods: In 16 healthy participants, we applied single-pulse TMS to six targets within the dlPFC at two coil angles and measured EEG responses., Results: Stimulation location significantly influenced observed EL-TEPs, with posterior and medial targets yielding larger EL-TEPs. Regions with high EL-TEP amplitude had less muscle artifact, and vice versa. The best group-level target yielded 102% larger EL-TEP responses compared to other dlPFC targets. Optimal dlPFC target differed across subjects, suggesting that a personalized targeting approach might boost the EL-TEP by an additional 36%., Significance: EL-TEPs can be probed without significant muscle-related confounds in posterior-medial regions of the dlPFC. The identification of an optimal group-level target and the potential for further refinement through personalized targeting hold significant implications for optimizing depression treatment protocols., Competing Interests: Declaration of interest CJK holds equity in Alto Neuroscience, Inc. All other authors have nothing to disclose., (Copyright © 2024 International Federation of Clinical Neurophysiology. All rights reserved.)

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., (© 2024 IOP Publishing Ltd.)

Introduction: Transcranial alternating current stimulation (tACS) at 5-Hz to the right hemisphere can effectively alleviate anxiety symptoms. This study aimed to explore the neural mechanisms that drive the therapeutic benefits., Methods: We collected electroencephalography (EEG) data from 24 participants with anxiety disorders before and after a tACS treatment session. tACS was applied over the right hemisphere, with 1.0 mA at F4, 1.0 mA at P4, and 2.0 mA at T8 (10-10 EEG convention). With eLORETA, we transformed the scalp signals into the current source density in the cortex. We then assessed the differences between post- and pre-treatment brain maps across multiple spectra (delta to low gamma) with non-parametric statistics., Results: We observed a trend of heightened power in alpha and reduced power in mid-to-high beta and low gamma, in accord with the EEG markers of anxiolytic effects reported in previous studies. Additionally, we observed a consistent trend of de-synchronization at the stimulating sites across spectra., Conclusion: tACS 5-Hz over the right hemisphere demonstrated EEG markers of anxiety reduction. The after-effects of tACS on the brain are intricate and cannot be explained solely by the widely circulated entrainment theory. Rather, our results support the involvement of plasticity mechanisms in the offline effects of tACS., (Copyright © 2024 Elsevier B.V. All rights reserved.)