Search Results (207)

Optically pumped magnetometer OPM-MEG has the potential to replace the traditional low-temperature superconducting quantum interference device SQUID-MEG. Coherence analysis can be used to evaluate the functional connectivity and reflect the information transfer process between brain regions. In this paper, a finger tapping movement paradigm based on auditory cues was used to measure the functional signals of the brain using OPM-MEG, and the coherence between the primary motor cortex (M1) and the primary motor area (PM) was calculated and analyzed. The results demonstrated that the coherence of the three frequency bands of Alpha (8–13 Hz), Beta (13–30 Hz), and low Gamma (30–45 Hz) and the selected reference signal showed roughly the same position, the coherence strength and coherence range decreased from Alpha to low Gamma, and the coherence coefficient changed with time. It was inferred that the change in coherence indicated different neural patterns in the contralateral motor cortex, and these neural patterns also changed with time, thus reflecting the changes in the connection between different functional areas in the time-frequency domain. In summary, OPM-MEG has the ability to measure brain coherence during finger movements and can characterize connectivity between brain regions. Full article

Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string (“fear detection” AND “artificial intelligence” OR “machine learning” AND NOT “fnirs OR mri OR ct OR pet OR image”). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing. Full article

Background: Biomechanical and neuromuscular differences between falls and recoveries have been well-studied; however, the cortical correlations remain unclear. Using mobile brain imaging via electroencephalography (EEG), we examined differences in sensorimotor beta frequencies between falls and recoveries during an unpredicted slip in walking. Methods: We recruited 22 young adults (15 female; 18–35 years) who experienced a slip (65 cm) during walking. Raw EEG signals were band-pass filtered, and independent component analysis was performed to remove non-neural sources, eventually three participants were excluded due to excessive artifacts. Peak beta power was extracted from three time-bins: 400 milliseconds pre-, 0–150 milliseconds post and 150–300 milliseconds post-perturbation from the midline (Cz) electrode. A 2 × 3 Analysis of Covariance assessed the interaction between time-bins and group on beta power, followed by Independent and Paired t-tests for between and within-group post hoc comparisons. Results: All participants (n = 19) experienced a balance loss, seven experienced a fall. There was a time × group interaction on beta power (p < 0.05). With no group differences pre-perturbation, participants who experienced a fall exhibited higher beta power during 0–150 milliseconds post-perturbation than those who recovered (p < 0.001). However, there were no group differences in beta power during 150–300 milliseconds post-perturbation. Conclusions: Young adults exhibiting a greater increase in beta power during the early post-perturbation period experienced a fall, suggesting a higher cortical error detection due to a larger mismatch in the expected and ongoing postural state and greater cortical dependence for sensorimotor processing. Our study results provide an overview of the possible cortical governance to modulate slip-fall/recovery outcomes. Full article

Conventional space vector pulse width modulation (CSVPWM) with the fixed switching frequency generates significant sideband harmonics in the three-phase voltage. Discrete random switching frequency SVPWM (DRSF-SVPWM) methods have been widely applied in motor control systems for the suppression of tone harmonic energy. To further reduce the amplitude of the high-frequency harmonic with a limited switching frequency variation range, this paper proposes a time-division subbands beta distribution random SVPWM (TSBDR-SVPWM) method. The overall frequency band of the switching frequency is equally divided into N subbands, and each fundamental cycle of the line voltage is segmented into 2*(N-1) equal time intervals. Additionally, within each time segment, the switching frequency is randomly selected from the corresponding subband and follows the optimal discrete beta distribution. The switching frequency harmonic energy in the line voltage spectrum spreads across multiple frequency subbands and discrete frequency components, thereby forming a more uniform power spectrum of the line voltage. Both simulation and experimental results validate that, compared with CSVPWM, the sideband harmonic amplitude is reduced by more than 8.5 dB across the entire range of speed and torque conditions in the TSBDR-SVPWM. Furthermore, with the same variation range of the switching frequency, the proposed method achieves the lowest switching frequency harmonic amplitude and flattest line voltage spectrum compared with several state-of-the-art random modulation methods. Full article

Background/Objectives: Emotion and cognition, two essential components of human mental processes, have traditionally been studied independently. The exploration of emotion and cognition is fundamental for gaining an understanding of human mental functioning. Despite the availability of various methods to measure and evaluate emotional states and cognitive processes, physiological measurements are considered to be one of the most reliable methods due to their objective approach. In particular, electroencephalography (EEG) provides unique insight into emotional and cognitive activity through the analysis of event-related potentials (ERPs). In this study, we discriminated pleasant/unpleasant emotional moods and low/high cognitive states using graph-theoretic features extracted from spatio-temporal components. Methods: Emotional data were collected at the Physiology Department of Istanbul Medical Faculty at Istanbul University, whereas cognitive data were obtained from the DepositOnce repository of Technische Universität Berlin. Wavelet coherence values for the N100, N200, and P300 single-trial ERP components in the delta, theta, alpha, and beta frequency bands were investigated individually. Then, graph-theoretic analyses were performed using wavelet coherence-based connectivity maps. Global and local graph metrics such as energy efficiency, strength, transitivity, characteristic path length, and clustering coefficient were used as features for classification using support vector machines (SVMs), k-nearest neighbor(K-NN), and linear discriminant analysis (LDA). Results: The results show that both pleasant/unpleasant emotional moods and low/high cognitive states can be discriminated, with average accuracies of up to 92% and 89%, respectively. Conclusions: Graph-theoretic metrics based on wavelet coherence of ERP components in the delta band with the SVM algorithm allow for the discrimination of emotional and cognitive states with high accuracy. Full article

This paper presents an analysis of the severity of alcohol use disorder (AUD) based on electroencephalogram (EEG) signals and alcohol drinking experiments by utilizing power spectral density (PSD) and the transitions that occur as individuals drink alcohol in increasing amounts. We use data from brain—computer interface (BCI) experiments using alcohol as a stimulus recorded from a group of seventeen alcohol-drinking male participants and the assessment scores of the alcohol use disorders identification test (AUDIT). This method investigates the mild, moderate, and severe symptoms of AUD using the three key domains of AUDIT, which are hazardous alcohol use, dependence symptoms, and severe alcohol use. We utilize the EEG spectral power of the theta, alpha, and beta frequency bands by observing the transitions from the initial to the final phase of alcohol consumption. Our results are compared for people with low-risk alcohol consumption, harmful or hazardous alcohol consumption, and lastly a likelihood of AUD based on the individual assessment scores of the AUDIT. We use Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) to cluster the results of the transitions in EEG signals and the overall brain activity of all the participants for the entire duration of the alcohol-drinking experiments. This study can be useful in creating an automatic AUD severity level detection tool for alcoholics to aid in early intervention and supplement evaluations by mental health professionals. Full article

Empathy has been linked to enhanced processing of social information, yet the neurophysiological correlates of such individual differences remain underexplored. Objectives: The aim of this study was to investigate how individual differences in trait empathy are reflected in oscillatory brain activity during the perception of non-verbal social cues. Methods: In this EEG study involving 30 participants, we examined spectral and time–frequency dynamics associated with trait empathy during a visual task requiring the interpretation of others’ body gestures. Results: FFT Power spectral analyses (applied to alpha/mu, beta, high beta, and gamma bands) revealed that individuals with high empathy quotients (High-EQ) exhibited a tendency for increased beta-band activity over frontal regions and markedly decreased alpha-band activity over occipito-parietal areas compared to their low-empathy counterparts (Low-EQ), suggesting heightened attentional engagement and reduced cortical inhibition during social information processing. Similarly, time–frequency analysis using Morlet wavelets showed higher alpha power in Low-EQ than High-EQ people over occipital sites, with no group differences in mu suppression or desynchronization (ERD) over central sites, challenging prior claims linking mu ERD to mirror neuron activity in empathic processing. These findings align with recent literature associating frontal beta oscillations with top-down attentional control and emotional regulation, and posterior alpha with vigilance and sensory disengagement. Conclusions: Our results indicate that empathic traits are differentially reflected in anterior and posterior oscillatory dynamics, supporting the notion that individuals high in empathy deploy greater cognitive and attentional resources when decoding non-verbal social cues. These neural patterns may underlie their superior ability to interpret body language and mental states from visual input. Full article

Background/Objectives: This study aimed to examine the influence of brain activity during motor preparation on walking ability, focusing on motor control during active ankle dorsiflexion. Methods: Participants were classified into high- and low-corticomuscular coherence (CMC), an index of neuromuscular control based on the median value. Biomechanical and neurophysiological indices of active ankle dorsiflexion and walking ability were compared between the two groups. Additionally, a machine learning model was developed to accurately predict the CMC classification using brain neural activity during motor preparation. Results: The Cz-TA CMC (beta frequency band) during active ankle dorsiflexion successfully detected significant differences in the maximum dorsiflexion angle, inversion angular velocity, brain activity localization, and variations in Cz beta power values during the transition from motor preparation to execution. Furthermore, CMC identified significant differences in dorsiflexion angle changes after toe-off and inversion angles at initial contact during gait. A support-vector machine model predicting high or low CMC demonstrated high accuracy (Accuracy: 0.96, Precision: 0.92–1.00, Recall: 0.91–1.00, F1 Score: 0.95–0.96) during motor execution based on beta power values from −500 to 0 ms prior to the initiation of active ankle dorsiflexion (representing motor preparation). Conclusions: These findings highlight that the motor preparation processes of the brain during active ankle dorsiflexion are involved in walking ability and can be used to predict it. This indicator is independent of disease severity and holds the potential to provide a clinically versatile evaluation method. Full article

Background/Objectives: Gamma oscillations (30–100 Hz), which are essential for memory, attention, and cortical synchronization, remain underexplored in Alzheimer’s disease (AD) research. While resting-state EEG studies have predominantly examined lower frequency bands (delta to beta), gamma activity may more accurately reflect early synaptic dysfunction and other mechanisms relevant to AD pathophysiology. AD is a common age-related neurodegenerative disorder frequently associated with altered resting-state EEG (rEEG) patterns. This study analyzed gamma power spectral density (PSD) during eyes-open (EOR) and eyes-closed (ECR) resting-state EEG in AD patients compared to cognitively normal (CN) individuals. Methods: rEEG data from 534 participants (269 CN, 265 AD) aged 40–90 were analyzed. Quantitative EEG (qEEG) analysis focused on the gamma band (30–100 Hz) using PSD estimation with the Welch method, coherence matrices, and coherence-based functional connectivity. Data preprocessing and analysis were performed using EEGLAB and Brainstorm in MATLAB R2024b. Group comparisons were conducted using ANOVA for unadjusted models and linear regression with age adjustment using log₁₀-transformed PSD values in Python (version 3.13.2, 2025). Results: AD patients exhibited significantly elevated gamma PSD in frontal and temporal regions during EOR and ECR states compared to CN. During ECR, gamma PSD was markedly higher in the AD group (Mean = 0.0860 ± 0.0590) than CN (Mean = 0.0042 ± 0.0010), with a large effect size (Cohen’s d = 1.960, p < 0.001). Conversely, after adjusting for age, the group difference was no longer statistically significant (β = −0.0047, SE = 0.0054, p = 0.391), while age remained a significant predictor of gamma power (β = −0.0008, p = 0.019). Pairwise coherence matrix and coherence-based functional connectivity were increased in AD during ECR but decreased in EOR relative to CN. Conclusions: Gamma oscillatory activity in the 30–100 Hz range differed significantly between AD and CN individuals during resting-state EEG, particularly under ECR conditions. However, age-adjusted analyses revealed that these differences are not AD-specific, suggesting that gamma band changes may reflect aging-related processes more than disease effects. These findings contribute to the evolving understanding of gamma dynamics in dementia and support further investigation of gamma PSD as a potential, age-sensitive biomarker. Full article

Introduction: Combat sport athletes are exposed to repetitive head impacts yet also develop distinct performance-related brain adaptations. Electroencephalography (EEG) provides millisecond-level insight into both processes; however, findings are dispersed across decades of heterogeneous studies. This mechanistic review consolidates and interprets EEG evidence to elucidate how participation in combat sports shapes brain function and to identify research gaps that impede clinical translation. Methods: A structured search was conducted in March 2025 across PubMed/MEDLINE, Scopus, Cochrane Library, ResearchGate, Google Scholar, and related databases for English-language clinical studies published between January 1980 and March 2025. Eligible studies recorded raw resting or task-related EEG in athletes engaged in boxing, wrestling, judo, karate, taekwondo, kickboxing, or mixed martial arts. Titles, abstracts, and full texts were independently screened by two reviewers. Twenty-three studies, encompassing approximately 650 combat sport athletes and 430 controls, met the inclusion criteria and were included in the qualitative synthesis. Results: Early visual EEG and perfusion studies linked prolonged competitive exposure in professional boxers to focal hypoperfusion and low-frequency slowing. More recent quantitative studies refined these findings: across boxing, wrestling, and kickboxing cohorts, chronic participation was associated with reduced alpha and theta power, excess slow-wave activity, and disrupted small-world network topology—alterations that often preceded cognitive or structural impairments. In contrast, elite athletes in karate, fencing, and kickboxing consistently demonstrated neural efficiency patterns, including elevated resting alpha power, reduced task-related event-related desynchronization (ERD), and streamlined cortico-muscular coupling during cognitive and motor tasks. Acute bouts elicited transient increases in frontal–occipital delta and high beta power proportional to head impact count and cortisol elevation, while brief judo chokes triggered short-lived slow-wave bursts without lasting dysfunction. Methodological heterogeneity—including variations in channel count (1 to 64), reference schemes, and frequency band definitions—limited cross-study comparability. Conclusions: EEG effectively captures both the adverse effects of repetitive head trauma and the cortical adaptations associated with high-level combat sport training, underscoring its potential as a rapid, portable tool for brain monitoring. Standardizing acquisition protocols, integrating EEG into longitudinal multimodal studies, and establishing sex- and age-specific normative data are essential for translating these insights into practical applications in concussion management, performance monitoring, and regulatory policy. Full article

Background: This study investigates the electrophysiological (EEG) correlates underlying negotiation dynamics in dyads engaged in a shared decision-making process. Methods: Using EEG hyperscanning, we examined single-brain and inter-brain neural activity in 26 participants (13 dyads) during a structured negotiation task. The participants, selected for their group-oriented decision-making preference, discussed a realistic group decisional scenario while their EEG activity was recorded. EEG frequency bands (delta, theta, alpha, beta, and gamma) were analyzed and Euclidean Distances were computed for measuring dissimilarity at the inter-brain neural level. Results: At the single-brain level, the results show increased delta and theta power in frontal regions, reflecting emotional engagement and goal-directed control, alongside heightened beta and gamma activity in parieto-occipital areas, linked to cognitive integration and decision-monitoring during the negotiation process. At the inter-brain neural level, we observed significant dissimilarity in frontal delta activity compared to temporo-central and parieto-occipital one, suggesting that negotiation involves independent cognitive regulation within the members of the dyads rather than complete neural synchrony. Conclusions: These findings highlight the dual role of negotiation as both a cooperative and cognitively demanding process, requiring emotional alignment and strategic adaptation. This study advances our understanding of the neurophysiological bases of negotiation and provides insights into how inter-brain dynamics shape collaborative decision-making. Full article

Precise analysis of electroencephalogram (EEG) signals is critical for advancing the understanding of neurological conditions and mapping brain activity. However, accurately visualizing brain regions and behavioral patterns from neural signals remains a significant challenge. The present study proposes a novel methodology for the automated calculation of the average power of EEG signals, with a particular focus on the beta frequency band which is known for its pronounced activity during cognitive tasks such as 2D content engagement. An optimization algorithm is employed to determine the most appropriate digital filter type and order for EEG signal processing, thereby enhancing both signal clarity and interpretability. To validate the proposed methodology, an experiment was conducted with 22 students, during which EEG data were recorded while participants engaged in cognitive tasks. The collected data were processed using MATLAB (version R2023a) and the EEGLAB toolbox (version 2022.1) to evaluate various filters, including finite impulse response (FIR) and infinite impulse response (IIR) Butterworth and IIR Chebyshev filters with a 0.5% passband ripple. Results indicate that the IIR Chebyshev filter, configured with a 0.5% passband ripple and a fourth-order design, outperformed the alternatives by effectively reducing average power while preserving signal fidelity. This optimized filtering approach significantly improves the accuracy of neural signal visualizations, thereby facilitating the creation of detailed brain activity maps. By refining the analysis of EEG signals, the proposed method enhances the detection of specific neural behaviors and deepens the understanding of functional brain regions. Moreover, it bolsters the reliability of real-time brain activity monitoring, potentially advancing neurological diagnostics and insights into cognitive processes. These findings suggest that the technique holds considerable promise for future applications in brain–computer interfaces and advanced neurological assessments, offering a valuable tool for both clinical practice and research exploration. Full article

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that disrupts functional brain connectivity, leading to cognitive and functional decline. Electroencephalography (EEG), a noninvasive and cost-effective technique, has gained attention as a promising tool for studying brain network alterations in AD. This study aims to leverage EEG-derived connectivity metrics to differentiate between healthy controls (HC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD, offering insights into disease progression. Methods: Using graph theory-based analysis, we extracted key connectivity metrics from resting-state EEG signals, focusing on the betweenness centrality and clustering coefficient. Statistical analysis was conducted across multiple EEG frequency bands, and discriminant analysis was applied to evaluate the classification performance of connectivity metrics. Results: Our findings revealed a progressive increase in theta-band betweenness centrality and a concurrent decrease in alpha- and beta-band centrality, reflecting AD-related network reorganization. Among the examined metrics, theta-band betweenness centrality exhibited the highest discriminative power in distinguishing AD stages. Additionally, classification performance using connectivity metrics was comparable to advanced deep learning models, highlighting their potential as predictive biomarkers. Conclusions: EEG-derived connectivity metrics demonstrate strong potential as noninvasive biomarkers for the early detection and monitoring of AD progression. Their effectiveness in capturing network alterations underscores their value in clinical diagnostic workflows, offering a scalable and interpretable alternative to deep learning-based models for AD classification. Full article

Background/Objectives: Motivation is a key factor for improving motor function and cognitive control in patients. Motivation for rehabilitation is influenced by the relationship between the therapist and patient, wherein appropriate voice encouragement is necessary to increase motivation. Therefore, we examined the differences between mothers and other individuals, such as physical therapists (PTs), in their verbal interactions with children with cerebral palsy who have poor communication abilities, as well as the neurological and physiological effects of variations in the tone of their speech. Methods: The three participants were children with cerebral palsy (Participant A: boy, 3 years; Participant B: girl, 7 years; Participant C: girl, 9 years). Participants’ mothers and the assigned PTs were asked to speak under three conditions. During this, the brain activity of the participants was measured using a 19-channel electroencephalogram. The results were further analyzed using Independent Component Analysis frequency analysis with exact Low-Resolution Brain Electromagnetic Tomography, allowing for the identification and visualization of neural activity in three-dimensional brain functional networks. Results: The results of the ICA frequency analysis for each participant revealed distinct patterns of brain activity in response to verbal encouragement from the mother and PT, with differences observed across the theta, alpha, and beta frequency bands. Conclusions: Our study suggests that the children were attentive to their mothers’ inquiries and focused on their internal experiences. Furthermore, it was indicated that when addressed by the PT, the participants found it easier to grasp the meanings and intentions of the words. Full article

In the field of neuroeconomics, the assessment of cognitive workload is a crucial issue with significant implications for real-world applications. Previous research has made progress in task-based germane cognitive load classification, but decentralized studies focusing on task-independent assessment have often produced less than optimal results. In this study, we present a stacked graph attention convolutional networks (SGATCNs) model to tackle the challenges related to task-independent cognitive workload assessment using EEG spatial information. The model employs the differential entropy (DE) and power spectral density (PSD) features of each EEG channel across four frequency bands (delta, theta, alpha, and beta) as node information. For the construction of the network structure, phase-locked values (PLVs), phase-lag indices (PLIs), Pearson correlation coefficients (PCCs), and mutual information (MI) are utilized and evaluated to generate a functional brain network. Specifically, the model aggregates spatial information on the dynamic map by stacking the graph attention layers and utilizes the convolution module to extract the frequency domain information from between the networks under each frequency band. We conducted a cognitive workload experiment with 15 subjects and selected three representative psychological experimental task paradigms (N-back, mental arithmetic, and Sternberg) to induce different levels of cognitive workload (low, medium, and high). Our framework achieved an average accuracy of 65.11% in recognizing the task-independent cognitive workload across the three scenarios. Full article