Search Results (714)

Background/Objectives: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively capture the spatiotemporal dynamics of brain activity during affective cognition, and their combination is promising for improving emotion recognition. However, multi-modal cross-subject emotion recognition remains challenging due to heterogeneous signal characteristics that hinder effective fusion and substantial inter-subject variability that degrades generalization to unseen subjects. Methods: To address these issues, this paper proposes DC-AGIN, a dual-contrastive learning attention graph isomorphism network for EEG–fNIRS emotion recognition. DC-AGIN employs an attention-weighted AGIN encoder to adaptively emphasize informative brain-region topology while suppressing redundant connectivity noise. For cross-modal fusion, a cross-modal contrastive learning module projects EEG and fNIRS representations into a shared latent semantic space, promoting semantic alignment and complementarity across modalities. Results: To further enhance cross-subject generalization, a supervised contrastive learning mechanism is introduced to explicitly mitigate subject-specific identity information and encourage subject-invariant affective representations. Experiments on a self-collected dataset are conducted under both subject-dependent five-fold cross-validation and subject-independent leave-one-subject-out (LOSO) protocols. The proposed method achieves 96.98% accuracy in four-class classification in the subject-dependent setting and 62.56% under LOSO. Compared with existing models, DC-AGIN achieves SOTA performance. Conclusions: These results demonstrate that the work on attention aggregation, cross-modal and cross-subject contrastive learning enables more robust EEG-fNIRS emotion recognition, thus supporting the effectiveness of DC-AGIN in generalizable emotion representation learning. Full article

Brain electrical activity, as recorded through electroencephalography (EEG), displays scale-free temporal fluctuations indicative of fractal behavior and complex dynamics. This study explores the use of the Higuchi Fractal Dimension (HFD) as a proxy of two complementary aspects of EEG temporal organization: local signal irregularity, interpreted within a Kolmogorov-type framework, and persistence related to temporal structure, associated with statistical complexity. The latter can be used to evidence persistence in the EEG signal, serving as an alternative to previously used approaches for estimating the Hurst exponent. Thirty-eight healthy participants underwent resting-state EEG recordings in open- and closed-eyes conditions. HFD was computed for the original signals to assess Kolmogorov complexity and for the signals’ cumulative envelopes to evaluate statistical complexity and, consequently, persistence. The results confirmed that HFD values align with theoretical expectations: higher for random noise in the Kolmogorov model (~2) and lower in the statistical model (~1.5). EEG data showed condition-dependent and topographically specific variations in HFD, with parieto-occipital regions exhibiting greater complexity and persistence. The HFD values in the statistical model fall within the 1–1.5 range, indicating long-term correlation. These findings support HFD as a reliable tool for assessing both the local roughness and global temporal structure of brain activity, with implications for physiological modeling and clinical applications. Full article

Background: Perceptual decision-making requires transforming sensory inputs into goal-directed actions under uncertainty. Neural oscillations in the theta band (3–7 Hz), particularly within frontal regions, have been implicated in cognitive control and decision confidence. However, whether changes in theta oscillations reflect greater effort during ambiguous decisions or more efficient control during clear conditions remains debated, and theta’s relationship to stimulus clarity is incompletely understood. Purpose: This study’s purpose was to examine how task difficulty modulates theta activity and how theta dynamics evolve across the decision-making process using two complementary analytical approaches. Methods: Electroencephalography (EEG) data were acquired from 26 healthy adults performing a face/house categorization task with images containing three levels of scrambled phase and Gaussian noise: clear (0%), moderate (40%), and high (55%). Theta dynamics were assessed from current source density (CSD) time courses of event-related potentials (ERPs) and single-trials. Statistical comparisons used Wilcoxon signed-rank tests with false discovery rate (FDR) correction for multiple comparisons. Results: Frontal theta power was greater for clear than noisy face stimuli (corrected p < 0.001), suggesting that theta activity reflects cognitive control effectiveness and decision confidence rather than processing difficulty. Connectivity decomposition revealed that frontoparietal theta coupling was modulated by stimulus clarity through both phase-locked (evoked: corrected p = 0.0085, dz = −0.61) and ongoing (induced: corrected p = 0.049, dz = −0.36) synchronization, with phase-locked coordination dominating the effect and showing opposite directionality to the induced components. Conclusions: Theta oscillations support perceptual decision-making through stimulus clarity modulation of both phase-locked and ongoing synchronization, with evoked component dominating. These findings underscore the importance of methodological choices in EEG-based connectivity research, as different analytical approaches capture different aspects of the same neural dynamics. The pattern of stronger theta activity for clear stimuli is consistent with neural processes related to decision confidence, though confidence was not measured behaviorally. Full article

Introduction: Massage therapy delivers structured mechanosensory input that can influence brain function, yet the central mechanisms and potential for neuroplastic change have not been synthesized across neuroimaging modalities. This mechanistic review integrates evidence from electroencephalography (EEG), functional MRI (fMRI), and functional near-infrared spectroscopy (fNIRS) to map how massage alters human brain activity acutely and over time and to identify signals of longitudinal adaptation. Materials and Methods: We conducted a scoping, mechanistic review informed by PRISMA/PRISMA-ScR principles. PubMed/MEDLINE, Cochrane Library, Google Scholar, and ResearchGate were queried for English-language human trials (January 1990–July 2025) that (1) delivered a practitioner-applied manual massage (e.g., Swedish, Thai, shiatsu, tuina, reflexology, myofascial techniques) and (2) measured brain activity with EEG, fMRI, or fNIRS pre/post or between groups. Non-manual stimulation, structural-only imaging, protocols, and non-English reports were excluded. Two reviewers independently screened and extracted study, intervention, and neuroimaging details; heterogeneity precluded meta-analysis, so results were narratively synthesized by modality and linked to putative mechanisms and longitudinal effects. Results: Forty-seven studies met the criteria: 30 EEG, 12 fMRI, and 5 fNIRS. Results: Regarding EEG, massage commonly increased alpha across single sessions with reductions in beta/gamma, alongside pressure-dependent autonomic shifts; moderate pressure favored a parasympathetic/relaxation profile. Connectivity effects were state- and modality-specific (e.g., reduced inter-occipital alpha coherence after facial massage, preserved or reorganized coupling with hands-on vs. mechanical delivery). Frontal alpha asymmetry frequently shifted leftward (approach/positive affect). Pain cohorts showed decreased cortical entropy and a shift toward slower rhythms, which tracked analgesia. Somatotopy emerged during unilateral treatments (contralateral central beta suppression). Adjuncts (e.g., binaural beats) enhanced anti-fatigue indices. Longitudinally, repeated programs showed attenuation of acute EEG/cortisol responses yet improvements in stress and performance; in one program, BDNF increased across weeks. In preterm infants, twice-daily massage accelerated EEG maturation (higher alpha/beta, lower delta) in a dose-responsive fashion; the EEG background was more continuous. In fMRI studies, in-scanner touch and reflexology engaged the insula, anterior cingulate, striatum, and periaqueductal gray; somatotopic specificity was observed for mapped foot areas. Resting-state studies in chronic pain reported normalization of regional homogeneity and/or connectivity within default-mode and salience/interoceptive networks after multi-session tuina or osteopathic interventions, paralleling symptom improvement; some task-based effects persisted at delayed follow-up. fNIRS studies generally showed increased prefrontal oxygenation during/after massage; in motor-impaired cohorts, acupressure/massage enhanced lateralized sensorimotor activation, consistent with use-dependent plasticity. Some reports paired hemodynamic changes with oxytocin and autonomic markers. Conclusions: Across modalities, massage reliably modulates central activity acutely and shows convergent signals of neuroplastic adaptation with repeated dosing and in developmental windows. Evidence supports (i) rapid induction of relaxed/analgesic states (alpha increases, network rebalancing) and (ii) longer-horizon changes—network normalization in chronic pain, EEG maturation in preterm infants, and neurotrophic up-shifts—consistent with trait-level recalibration of stress, interoception, and pain circuits. These findings justify integrating massage into rehabilitation, pain management, mental health, and neonatal care and motivate larger, standardized, multimodal longitudinal trials to define dose–response relationships, durability, and mechanistic mediators (e.g., connectivity targets, neuropeptides). Full article

Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges remain: (1) how to design an end-to-end EEG-based identification pipeline; (2) how to perform automatic electrode selection for each user to reduce redundancy and improve discriminative capacity; (3) how to enhance the backbone network’s feature extraction capability by suppressing irrelevant information and better leveraging informative patterns; and (4) how to leverage higher-level information in EEG signals to achieve intent recognition (i.e., EEG-based task/activity recognition under controlled paradigms) on top of person identification. To address these issues, this article proposes, for the first time, a unified deep learning framework that integrates automatic electrode selection, person identification, and intent recognition. We introduce a novel backbone network, AES-MBE, which integrates automatic electrode selection (AES) and intent recognition. The network combines a channel-attention mechanism with a multi-scale bidirectional encoder (MBE), enabling adaptive capture of fine-grained local features while modeling global temporal dependencies in both forward and backward directions. We validate our approach using the PhysioNet EEG Motor Movement/Imagery Dataset (EEGMMIDB), which contains EEG recordings from 109 subjects performing 4 tasks. Compared with state-of-the-art methods, our framework achieves superior performance. Specifically, our method attains a person identification accuracy of 98.82% using only 4 electrodes and an average intent recognition accuracy of 91.58%. In addition, our approach demonstrates strong stability and robustness as the number of users varies, offering insights for future research and practical applications. Full article

The use of virtual reality (VR) has made significant advancements, and now it is widely used across a range of applications. However, consumers’ capacity to fully enjoy VR experiences continues to be limited by a chronic problem known as cybersickness (CS). This study explores the feasibility of mitigating CS through geometric scene simplification combined with electroencephalography (EEG)-based monitoring. According to the sensory conflict theory, this issue is caused by the discrepancy between the visually induced self-motion (VIMS) through immersive displays and the real motion the vestibular system detects. While prior mitigation strategies have largely relied on hardware modifications or visual field restrictions, this paper introduces a novel framework that integrates geometric scene simplification with EEG-based neurophysiological activity to reduce VIMS during VR immersion. The proposed framework combines EEG neurophysiology, allowing us to monitor users’ brainwave activity and cognitive states during virtual immersion experience. The empirical evidence from our investigation shows a correlation between CS manifestation and neural activation in the parietal and temporal lobes. As an experiment with 15 subjects, statistical differences were significantly different with $P= 0.001$ and large effect size $\eta 2=0.28$, while preliminary trends suggest lower neural activation during simplified scenes. Notably, a decrease in neural activation corresponding to reduced optic flow (OF) suggests that VR environment simplification may help attenuate CS symptoms, providing preliminary support for the proposed strategy. Full article

Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding therapeutic and prognostic decisions. Electroencephalography (EEG) microstate analysis is a promising, non-invasive method for tracking large-scale brain dynamics, but research in pDOC has predominantly relied on a canonical 4-class model. This methodological constraint may limit the ability to capture the full complexity of neural alterations present in these patients. Objective: This pilot study aimed to offer an objective method for assessing consciousness, complementing and enhancing the existing approaches established in the literature. The classical 4-class and an extended 7-class microstate model were compared to determine which more accurately characterizes the complexity of resting-state brain dynamics across different levels of consciousness in pDOC patients and healthy controls (HCs). Methods: Retrospective resting-state EEG (rsEEG) data from a cohort of pDOC patients and HC subjects were analyzed. Microstate analysis was performed using both 4-class and 7-class templates. The models were evaluated and compared based on three criteria: spatial correspondence with canonical maps (shared variance), the number of significant intra-group correlations between temporal features (Spearman test), and their ability to discriminate between the pDOC and HC groups (Wilcoxon test). Results: The 7-class microstate model provided a more accurate description of brain activity for most participants, with a greater number of microstate classes exceeding the 50% shared variance threshold compared to the 4-class model. In the pDOC group, both the 4-class and 7-class models showed a mean shared variance <50% in class D, which is associated with executive functioning across both templates. For the HC group, a prevalence of classes B and D emerged in both models, indicating higher engagement of executive functions. Furthermore, the 7-class model allowed for a group-specific analysis, which demonstrated that microstates A and F were consistently shared among 86% of pDOC patients. This suggests the potential preservation of specific intrinsic brain networks, particularly the sensory and default networks, even in the presence of severely impaired consciousness. Moreover, the 7-class model yielded a higher number of significant correlations within both groups and identified a broader set of temporal features that were significantly different between pDOC patients and HCs. These results highlight the enhanced sensitivity of the 7-class model in distinguishing subtle brain dynamics and improving the diagnostic capability for pDOC. Conclusions: The 7-class microstate model provides a more fine-grained and sensitive characterization of brain activity in both pDOC patients and healthy individuals. It demonstrated better performance in capturing individual brain dynamics, identifying shared network patterns, and discriminating between clinical populations. These findings suggest that the extended 7-class model holds greater potential for clinical utility and could lead to the development of more robust biomarkers for assessing consciousness. Full article

Finding a correlation between physiological measures and subjective well-being (SWB) or comfort has been an active research area in recent years. We focus on short-term SWB measures and their correlation to electroencephalography (EEG) signals in an office environment. We recorded EEG from 30 participants and asked them to report their SWB every 30 s. We analyzed the correlation between the relative power of different frequency bands at various sensor locations and SWB via k-nearest neighbor (k-NN) classification and linear regression. We also analyzed the correlation of the time series themselves at different sensor locations and how they can be classified into different SWB values via k-NN. Then, we tried to cluster participants into subgroups that had a similar correlation between their EEG recordings and their reported SWB. We found that a correlation between relative power and SWB also holds for short terms. However, the results of every single participant of all analyses vary substantially, and we could not find any consistent clustering into subgroups. That implies a huge individuality when it comes to EEG measures and reported short-term SWB. Full article

Due to the damage happening in the nervous system, neuropathic pain occurs and it affects the quality of life of the patient to a great extent. Therefore, some clinical evaluations are required to assess the diagnostic outcomes precisely. A lot of information about the activities of the brain is provided by Electroencephalography (EEG) signals and neuropathic pain can be assessed and classified with the aid of EEG and machine learning. In this work, two approaches are proposed in terms of efficient blended models for the classification of neuropathic pain through EEG signals. In the first blended model, once the features are extracted using Discrete Wavelet Transform (DWT), statistical features, and Fuzzy C-Means (FCM) clustering techniques, the features are selected using Grey Wolf Optimization (GWO), Feature Correlation Clustering Technique (FCCT), F-test, and Bayesian Optimization Algorithm (BOA) and it is classified with the help of three hybrid classification models like Spider Monkey Optimization-based Gradient Boosting Machine (SMO-GBM) classifier, hybrid deep kernel learning with Support Vector Machine (DKL-SVM) classifier, and CatBoost classifier. In the second blended model, once the features are extracted, the features are selected using Hybrid Feature Selection—Majority Voting System (HFS-MVS), Hybrid Salp Swarm Optimization—Particle Swarm Optimization (SSO-PSO), Pearson Correlation Coefficient (PCC), and Mutual Information (MI) and it is classified with the help of three hybrid classification models like Partial Least Squares (PLS) variant classification models combined with Kernel-based SVM, ensemble classification model with soft voting strategy, and Extreme Gradient Boosting (XGBoost) classifier. The proposed blended models are evaluated on a publicly available dataset and the best results are shown when the FCM features are selected with SSO-PSO feature selection technique and classified with Polynomial Kernel-based PLS-SVM Classifier, reporting a high classification accuracy of 92.68% in this work. Full article

Driver fatigue detection based on electroencephalography (EEG) signals has gained increasing attention for enhancing road safety. However, existing deep learning models often treat EEG data as generic time-series inputs, neglecting the inherent hierarchical and spatial–temporal structure of brain activity, which limits their interpretability and generalization. To address this, we propose a novel Residual Polynomial Network (RPN) that explicitly models the positive and negative activation patterns in EEG signals through a polarity-aware architecture. The RPN integrates polarity decomposition, residual learning, and hierarchical feature fusion to capture discriminative neurophysiological dynamics while maintaining model transparency. Extensive experiments are conducted on a real-world driving fatigue dataset using a subject-wise 10-fold cross-validation protocol. Results show that the proposed RPN achieves an average classification accuracy of 97.65%, outperforming conventional machine learning and deep learning baselines including SVM, KNN, DT, and LSTM. Ablation studies confirm the effectiveness of each component, and Sankey diagram analysis provides interpretable insights into feature-to-class mappings. This work not only advances the state of the art in EEG-based fatigue detection but also offers a more transparent and physiologically plausible deep learning framework for brain signal analysis. Full article

Functional constipation (FC) is a common functional gastrointestinal disorder thought to arise from the brain–gut axis dysfunction, yet direct human neurophysiological evidence is lacking. We recorded high-density electroencephalography (EEG) data in 21 FC patients and 37 healthy controls across resting, cognitive, and defecation-related tasks. We observed that FC patients displayed a consistent, task-dependent signature compared with healthy controls. At the regional level, FC patients exhibited increased alpha during both resting and defecation-related tasks, reduced temporal gamma during defecation-related tasks, as well as elevated temporal theta during the cognitive task. At the global level, we found altered network properties, such as global efficiency in the delta and beta band networks during resting and defecation-related tasks. These findings establish a direct neurophysiological link between specific, condition-dependent perturbations in cortical rhythm activity and FC pathophysiology. Our work implicates the brain–gut axis in symptom generation and opens a path toward EEG-based biomarkers and targeted neuromodulatory therapies. Full article

Background/Objectives: Obesity and insulin resistance (IR) increase the risk of mood disorders, which often manifest during young adulthood. However, neuroelectrophysiological investigations of whether adiposity and IR modify electrocortical activity and emotional processing outcomes remain underexplored, particularly in young adults. Therefore, this study used electroencephalography (EEG) to investigate whether obesity and/or IR moderate the relationships between brain potentials and affective processing in younger adults. Methods: Thirty younger adults completed a passive picture-viewing task utilizing the International Affective Picture System while real-time electroencephalography was simultaneously recorded. Two event-related potentials—early posterior negativity (EPN) and late positive potential (LPP)—were quantified. Affective processing parameters included mean valence ratings and stimulus-to-response-onset reaction times in response to unpleasant, pleasant, and neutral images. Body fat percentage and Homeostatic Model Assessment for Insulin Resistance values were measured. Hierarchical moderated regression analysis was utilized to test the interrelationships between brain potentials, adiposity, IR, and affective processing. Results: In the Negative−Neutral condition, lean and insulin-sensitive participants gave less negative valence ratings to unpleasant versus neutral images when late-window LPP amplitudes were larger, whereas this relationship was reversed in participants with obesity and absent in those with IR. Contrariwise, neither obesity nor IR moderated LPP responses to affective processing parameters in the Positive−Neutral or Negative−Positive valence conditions. Additionally, obesity and IR did not moderate the links between EPN responses and affective processing parameters in any of the valence conditions. Conclusions: Lean, insulin-sensitive young adults showed attenuated affective processing of unpleasant stimuli through stronger neural responses, whereas neural responses to pleasant stimuli did not vary across levels of body fat or IR. These preliminary findings suggest that both obesity and IR increase the vulnerability to mood disorders in young adulthood. Full article

Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition that is typically identified through behavioral assessments and subjective clinical reports. However, electroencephalography (EEG) offers a cost-effective and non-invasive alternative for capturing neural activity patterns closely associated with this disorder. Despite this potential, EEG-based ADHD classification remains challenged by overfitting, dependence on extensive preprocessing, and limited interpretability. Here, we propose a novel neural architecture that integrates transformer-based temporal attention with Gaussian mixture functional connectivity modeling and a cross-entropy loss regularized through $\alpha$-Rényi mutual information, termed T-GARNet. The multi-scale Gaussian kernel functional connectivity leverages parallel Gaussian kernels to identify complex spatial dependencies, which are further stabilized and regularized by the $\alpha$-Rényi term. This design enables direct modeling of long-range temporal dependencies from raw EEG while enhancing spatial interpretability and reducing feature redundancy. We evaluate T-GARNet on a publicly available ADHD EEG dataset using both leave-one-subject-out (LOSO) and stratified group k-fold cross-validation (SGKF-CV), where groups correspond to control and ADHD, and compare its performance against classical and modern state-of-the-art methods. Results show that T-GARNet achieves competitive or superior performance (82.10% accuracy), particularly under the more challenging SGKF-CV setting, while producing interpretable spatial attention patterns consistent with ADHD-related neurophysiological findings. These results underscore T-GARNet’s potential as a robust and explainable framework for objective EEG-based ADHD detection. Full article

This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles. Full article

Background/Objectives: Regular physical activity (PA) benefits mood and cognition, yet the neural markers associated with free-living PA remain unclear. Alpha asymmetry (AA), a neural marker of affective and motivational states, may help predict individuals’ preferred activity intensity and duration. To examine the relationship between resting-state AA in frontal and parietal regions, positive affect, and accelerometer-derived PA metrics were measured. Methods: Fifty-nine participants (age = 21.8 years) wore wrist accelerometers for 7 days, completed resting-state electroencephalography (EEG; alpha power 8–13 Hz), and completed the Positive and Negative Affect Schedule (PANAS). PA metrics included sedentary time (ST), light PA (LPA), moderate-to-vigorous PA (MVPA), average acceleration (AvAcc), intensity gradient (IG), and the most active X minutes (M2–M120). Multiple regression models tested AA to PA associations while accounting for sex and positive affect. Results: Although frontal AA was included as a key neural candidate, the observed associations emerged only at parietal sites. Greater right parietal AA power was associated with the most active M60, M30, M15, M10, and M5. For IG, greater AA power was observed in the left parietal region. No significant associations emerged for LPA, MVPA, AvAcc, M120, or M2. Across models, higher positive affect consistently predicted greater PA engagement. Conclusions: While resting frontal AA is theoretically relevant to motivational processes, the findings indicate that parietal AA more strongly differentiates individuals’ tendencies toward specific PA intensities and durations. Positive affect is associated with PA engagement. These findings identify parietal AA as a promising neural correlate for tailoring PA strategies aimed at sustaining active lifestyles. Full article