Search Results (32)

Attention Deficit Hyperactivity Disorder (ADHD) is a heterogeneous neurodevelopmental disorder whose diagnosis is mainly based on behavioral assessment and is often delayed due to clinical complexity and limited availability of specialists. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable source of information for supporting automated and objective diagnosis. However, existing studies often do not fully capture the complex interactions of functional connectivity between different brain regions. To address this limitation, this work proposes a graph-based deep learning framework for ADHD classification from rs-fMRI that combines functional connectivity modeling with graph representation learning. The approach used Phase-Locking Value (PLV)-based connectivity estimation and Graph Sample and Aggregate (GraphSAGE) to jointly capture regional brain activity and inter-regional interactions in a scalable and efficient manner. GraphSAGE improves robustness to noise and inter-subject variability by aggregating information from stable local graph neighborhoods. This integration allows the model to learn discriminative connectivity-aware representations while remaining robust to signal variability and adaptable to multi-site data. The proposed framework was evaluated on the publicly available ADHD-200 dataset across multiple acquisition sites as well as on a combined multi-site dataset. The results indicate consistent performance across individual sites and on the combined dataset. The model achieved an Accuracy of 0.89, an AUC of 0.96, and a Specificity of 0.96 on the combined dataset, outperforming several existing methods in this setting. By integrating PLV-based connectivity with GraphSAGE learning, the approach provides an effective and scalable solution for automated ADHD classification from rs-fMRI data, contributing to data-driven approaches for the analysis of neurodevelopmental disorders. Full article

Objective: The purpose of this research is to examine how motivational music immediately impacts the brain’s functional connectivity patterns in male athletes following a single session of intense endurance exercise, utilizing resting-state electroencephalography (EEG) and brain network analysis methods. Methods: The study involved 34 healthy male athletes who were tasked with performing incremental cycling exercises until exhaustion, both with and without music. Their resting-state EEG was recorded before and after the exercise. Brain functional networks were analyzed in the theta, alpha, and beta frequency bands based on changes in phase locking value (PLV). Specifically, the study examined the central executive network (CEN), default mode network (DMN), salience network (SN), sensorimotor network (SMN), and dorsal attention network (DAN), assessing their topological properties using graph theory methods. Results: Music significantly prolonged the time to exhaustion. Across frequency bands, the music condition exhibited higher global and local efficiency compared with the no-music condition. Following exhaustion without music, beta-band connectivity significantly increased, suggesting compensatory hyper-synchronization under fatigue. In contrast, music led to reduced alpha- and beta-band global connectivity post-exercise, accompanied by selective strengthening of functionally relevant couplings, particularly between SMN and CEN, and enhanced DAN–DMN coordination. Additionally, music prevented maladaptive connectivity shifts observed under fatigue, including excessive SN–CEN coupling. Conclusions: Exhaustive exercise without music induces widespread beta-band hyper-connectivity, reflecting increased neural cost under central fatigue. Music, however, promotes a more efficient and selectively integrated network configuration, supporting the neural efficiency hypothesis. These findings provide neurophysiological evidence that music optimizes large-scale brain network organization under physical stress, thereby contributing to enhanced endurance performance. Full article

This pilot EEG study examined the feasibility of a soundscape-based 40 Hz auditory stimulation format by using a soundscape-only condition as a contrast control. We tested whether a nature-based soundscape with an additively layered pure 40 Hz sine component (40 Hz ON; not amplitude-modulated) yields a more pronounced narrowband response centered at 40 Hz than the same soundscape without the 40 Hz layer (40 Hz OFF). Participants completed both conditions in a single-blind, randomized-order, within-participant crossover session with a washout interval. EEG outcomes included 40 Hz power, frequency-domain SNR around 40 Hz, scalp distribution of 40 Hz power, and phase-based connectivity in the gamma range. This study evaluates EEG-level detectability of 40 Hz–centered neural signatures and does not assess cognitive/clinical efficacy or therapeutic benefit. Across metrics, the 40 Hz ON soundscape showed a consistent ON > OFF directionality, including localized electrode-level signals and a temporal-region summary measure under nominal, uncorrected testing, accompanied by a clearer narrowband feature near 40 Hz in spectral profiles. Overall, the observed trends are consistent with the feasibility of embedding an additive 40 Hz layer into a naturalistic soundscape in a manner that yields EEG-quantifiable, 40 Hz centered signatures; however, because this is an exploratory pilot without multiplicity control, all effects should be interpreted as hypothesis-generating and warrant confirmation in larger, preregistered studies with multiplicity-aware inference. Full article

Background: Multiple-object tracking (MOT) is a cognitively demanding task involving sustained attention and interhemispheric integration. While previous studies have revealed that gamma-band coherence mediates interhemispheric integration in MOT tasks with visible internal boundaries, the neural mechanisms supporting integration without such boundaries remain unclear. This study investigated brain functional connectivity during a boundary-free MOT task. Methods: Thirty-eight healthy participants completed the task under four experimental conditions, defined by two load levels (two and four targets) and two movement configurations (within hemifield and between hemifield). Electroencephalography (EEG) activity was recorded in both the task and resting states. The phase locking value (PLV) and network properties were analyzed. Results: The behavioral results demonstrated greater accuracy under the two-target conditions than under the four-target conditions and significantly worse performance under the four-target between-hemifield condition. EEG analyses revealed increased theta-band PLV under the four-target between-hemifield condition, reflecting enhanced interhemispheric synchronization. The PLV difference between the four-target within-hemifield and between-hemifield conditions was positively correlated with the accuracy difference, suggesting that increased theta-band phase synchronization is associated with better task performance. Moreover, sex-related differences were observed, with males showing better performance, shorter click times, and higher theta-band PLV than females. Conclusions: Our study provides evidence that theta-band phase synchronization plays a critical role in interhemispheric integration during boundary-free MOT, extending previous findings on gamma-band coherence under visible-boundary conditions and offering new insights into the neural mechanisms of interhemispheric coordination. Full article

Brain–computer interfaces (BCIs) translate electroencephalography (EEG) signals into control commands, offering potential solutions for motor-impaired individuals. While traditional BCI studies often focus solely on amplitude variations or inter-channel connectivity, movement-related brain activity is inherently dynamic, involving interactions across regions and frequency bands. We propose that combining amplitude-based (filter bank common spatial patterns, FBCSP) and phase-based connectivity features (phase-locking value, PLV) improves decoding accuracy. EEG signals from ten healthy subjects were recorded during arm movements, with electromyography (EMG) as ground truth. After preprocessing (resampling, normalization, bandpass filtering), FBCSP and multi-lag PLV features were fused, and the ReliefF algorithm selected the most informative subset. A feedforward neural network achieved average metrics of: Pearson correlation 0.829 ± 0.077, R-squared value 0.675 ± 0.126, and root mean square error (RMSE) 0.579 ± 0.098 in predicting EMG amplitudes indicative of arm movement angles. Analysis highlighted contributions from both FBCSP and PLV, particularly in the 4–8 Hz and 24–28 Hz bands. This fusion approach, augmented by data-driven feature selection, significantly enhances movement decoding accuracy, advancing robust neuroprosthetic control systems. Full article

Background/Objectives: Epilepsy is a common neurological disorder that not only severely impacts patients’ health but also imposes a significant burden on families and society. However, its pathogenesis remains unclear. Astrocytes play a crucial role in epileptic seizures and may serve as potential therapeutic targets. Establishing a network model of epileptic seizures based on the astrocyte–neuron cell coupling and the clinical electroencephalographic (EEG) characteristics of epilepsy can facilitate further research on refractory epilepsy and the development of treatment strategies. Methods: This study constructs a neuronal network dynamic model of epileptic seizures based on the Watts–Strogatz small-world network, with a particular emphasis on the biological mechanisms of astrocyte–neuron coupling. The phase-locking value (PLV) is used to quantify the degree of network synchronization and to identify the key nodes or connections influencing synchronous seizures, such that two epilepsy treatment strategies are proposed: seizure suppression through stimulation and surgical resection simulation therapy. The therapeutic effects are evaluated based on the PLV-quantified network synchronization. Results: The results indicate that the desynchronization effect of random noise and sinusoidal wave stimulation is limited, while square wave stimulation is the most effective. Among the four surgical resection strategies, the effectiveness is the highest when resecting nodes exhibiting epileptic discharges. These findings contribute to the development of rational seizure suppression strategies and provide insights into precise epileptic focus localization and personalized treatment approaches. 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

Insufficient sleep causes muscle fatigue, impacting performance. The mechanism of brain–muscle signaling remains uncertain. In this study, we examined the impact of sleep deprivation on muscle endurance during isometric contractions and explored the changes in brain–muscle connectivity. Methods: The research involved 35 right-handed male participants who took part in an exercise test that included isometric contractions of the left and right biceps in both sleep-deprived and well-rested states. Muscle contraction duration and electroencephalogram (EEG) and electromyography (EMG) signals were recorded. Functional connectivity between brain regions was assessed using the phase locking value (PLV), while partial directed coherence (PDC) was used to analyze signal directionality between motor centers and muscles. Results: The connectivity strength between Brodmann areas (BAs) 1-5 and the right BA6, 8 regions was significantly decreased in the isometric contractions after sleep deprivation. Insufficient sleep enhanced the PDC signals from the motor center of the right brain to the left biceps, and it decreased the PDC signals from both biceps to their opposite motor centers. Conclusions: Sleep deprivation shortened muscle isometric contraction duration by affecting the interaction between the somatosensory motor cortex and the right premotor cortex, reducing biceps feedback signal connectivity to the contralateral motor center in the brain. Full article

Classification of neurocognitive states from Electroencephalography (EEG) data is complex due to inherent challenges such as noise, non-stationarity, non-linearity, and the high-dimensional and sparse nature of connectivity patterns. Graph-theoretical approaches provide a powerful framework for analysing the latent state dynamics using connectivity measures across spatio-temporal-spectral dimensions. This study applies the graph Koopman embedding kernels (GKKE) method to extract latent neuro-markers of seizures from epileptiform EEG activity. EEG-derived graphs were constructed using correlation and mean phase locking value (mPLV), with adjacency matrices generated via threshold-binarised connectivity. Graph kernels, including Random Walk, Weisfeiler–Lehman (WL), and spectral-decomposition (SD) kernels, were evaluated for latent space feature extraction by approximating Koopman spectral decomposition. The potential of graph Koopman embeddings in identifying latent metastable connectivity structures has been demonstrated with empirical analyses. The robustness of these features was evaluated using classifiers such as Decision Trees, Support Vector Machine (SVM), and Random Forest, on Epilepsy-EEG from the Children’s Hospital Boston’s (CHB)-MIT dataset and cognitive-load-EEG datasets from online repositories. The classification workflow combining mPLV connectivity measure, WL graph Koopman kernel, and Decision Tree (DT) outperformed the alternative combinations, particularly considering the accuracy (91.7%) and F1-score (88.9%), The comparative investigation presented in results section convinces that employing cost-sensitive learning improved the F1-score for the mPLV-WL-DT workflow to 91% compared to 88.9% without cost-sensitive learning. This work advances EEG-based neuro-marker estimation, facilitating reliable assistive tools for prognosis and cognitive training protocols. Full article

Background: Sexual characteristics in brain neurophysiological activity are a significant area of research in cognitive neuroscience. As a sport that involves minimal physical movement, shooters remain largely stationary during aiming, facilitating the collection of their neural activity compared to athletes in other sports. Objectives: To investigate the neural characteristics of novice shooters of different genders under competitive conditions. Methods: Sixteen subjects participated in a shooting competition following four weeks of training. Electroencephalogram (EEG) data and behavioral data (shooting scores, aiming curves, and pressure curves) were recorded during the competition, and the power spectral density (PSD) and phase-locking value (PLV) network features were extracted to explore further the correlation between the shooting scores and neural activity. Results: In our sample, (1) there were no significant differences in shooting scores between males and females; (2) there were differences in PSD values across the theta, alpha, alpha-2, beta, and gamma frequency bands between males and females; and (3) there were differences in PLV network properties in the theta, alpha, beta, and gamma frequency bands between males and females. Correlation analysis revealed associations between shooting scores and neural activity in male and female novices. Conclusions: The case study demonstrated that males and females exhibited different neural activity characteristics in the shooting competition, providing a foundation for further investigation into the sex differences in neural activity in shooting competition. Full article

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

Background: With the rapid expansion of the generative AI market, conducting in-depth research on cognitive conflicts in human–computer interaction is crucial for optimizing user experience and improving the quality of interactions with AI systems. However, existing studies insufficiently explore the role of user cognitive conflicts and the explanation of stance attribution in the design of human–computer interactions. Methods: This research, grounded in mental models theory and employing an improved version of the oddball paradigm, utilizes Event-Related Spectral Perturbations (ERSP) and functional connectivity analysis to reveal how task types and stance attribution explanations in generative AI influence users’ unconscious cognitive processing mechanisms during service failures. Results: The results indicate that under design stance explanations, the ERSP and Phase Locking Value (PLV) in the theta frequency band were significantly lower for emotional task failures than mechanical task failures. In the case of emotional task failures, the ERSP and PLV in the theta frequency band induced by intentional stance explanations were significantly higher than those induced by design stance explanations. Conclusions: This study found that stance attribution explanations profoundly affect users’ mental models of AI, which determine their responses to service failure. Full article

Monitoring the driver’s physical and mental state based on wearable EEG acquisition equipment, especially the detection and early warning of fatigue, is a key issue in the research of the brain–computer interface in human–machine intelligent fusion driving. Comparing and analyzing the waking (alert) state and fatigue state by simulating EEG data during simulated driving, this paper proposes a brain functional network construction method based on a phase locking value (PLV) and phase lag index (PLI), studies the relationship between brain regions, and quantitatively analyzes the network structure. The characteristic parameters of the brain functional network that have significant differences in fatigue status are screened out and constitute feature vectors, which are then combined with machine learning algorithms to complete classification and identification. The experimental results show that this method can effectively distinguish between alertness and fatigue states. The recognition accuracy rates of 52 subjects are all above 70%, with the highest recognition accuracy reaching 89.5%. Brain network topology analysis showed that the connectivity between brain regions was weakened under a fatigue state, especially under the PLV method, and the phase synchronization relationship between delta and theta frequency bands was significantly weakened. The research results provide a reference for understanding the interdependence of brain regions under fatigue conditions and the development of fatigue driving detection systems. Full article

EEG-based biometrics represent a relatively recent research field that aims to recognize individuals based on their recorded brain activity using electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, using the phase lag index (PLI) and the phase locking value (PLV) methods, we investigate how the performance of a connectivity-based EEG biometric system varies with respect to different time windows (using epochs of different lengths ranging from 0.5 s to 12 s with a step of 0.5 s) to understand if it is possible to define the optimal duration of the EEG signal required to extract those distinctive features. All the analyses were performed on two freely available EEG datasets, including 109 and 23 subjects, respectively. Overall, as expected, the results have shown a pronounced effect of the time window length on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase in the biometric performance as the time window increases. Furthermore, our initial findings strongly suggest that enlarging the window size beyond a specific maximum threshold fails to enhance the performance of biometric systems. In conclusions, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as an EEG fingerprint and that, in this context, it is essential to establish an adequate time window capable of capturing subject-specific features. Furthermore, we speculate that the poor performance obtained with short time windows mainly depends on the difficulty of correctly estimating the connectivity metrics from very small EEG epochs (shorter than 8 s). Full article

With the development of large language model technologies, the capability of social robots to interact emotionally with users has been steadily increasing. However, the existing research insufficiently examines the influence of robot stance attribution design cues on the construction of users’ mental models and their effects on human–robot interaction (HRI). This study innovatively combines mental models with the associative–propositional evaluation (APE) model, unveiling the impact of the stance attribution explanations of this design cue on the construction of user mental models and the interaction between the two types of mental models through EEG experiments and survey investigations. The results found that under the influence of intentional stance explanations (compared to design stance explanations), participants displayed higher error rates, higher θ- and β-band Event-Related Spectral Perturbations (ERSPs), and phase-locking value (PLV). Intentional stance explanations trigger a primarily associatively based mental model of users towards robots, which conflicts with the propositionally based mental models of individuals. Users might adjust or “correct” their immediate reactions caused by stance attribution explanations after logical analysis. This study reveals that stance attribution interpretation can significantly affect users’ mental model construction of robots, which provides a new theoretical framework for exploring human interaction with non-human agents and provides theoretical support for the sustainable development of human–robot relations. It also provides new ideas for designing robots that are more humane and can better interact with human users. Full article