Search Results (485)

Epilepsy has diverse seizure types that challenge diagnosis and treatment, requiring automated and accurate classification to improve patient outcomes. Traditional electroencephalogram (EEG)-based diagnosis relies on manual interpretation, which is subjective and inefficient, particularly for multi-class differentiation in imbalanced datasets. This study aims to develop a hybrid framework for automated multi-class seizure type classification using segment-wise EEG processing and multi-band feature engineering to enhance precision and address data challenges. EEG signals from the TUSZ dataset were segmented into 1-s windows with 0.5-s overlaps, followed by the extraction of multi-band features, including statistical measures, sample entropy, wavelet energies, Hurst exponent, and Hjorth parameters. The mutual information (MI) approach was employed to select the optimal features, and seven machine learning models (SVM, KNN, DT, RF, XGBoost, CatBoost, LightGBM) were evaluated via 10-fold stratified cross-validation with a class balancing strategy. The results showed the following: (1) XGBoost achieved the highest performance (accuracy: 0.8710, F1 score: 0.8721, AUC: 0.9797), with $\gamma$-band features dominating importance. (2) Confusion matrices indicated robust discrimination but noted overlaps in focal subtypes. This framework advances seizure type classification by integrating multi-band features and the MI method, which offers a scalable and interpretable tool for supporting clinical epilepsy diagnostics. Full article

Children with Autism Spectrum Disorder (ASD) often experience cognitive difficulties that impact learning. This study explores the use of electroencephalogram data collected with the MUSE 2 headband during task-based cognitive sessions to understand how cognitive states in children with ASD change across three structured tasks: Shape Matching, Shape Sorting, and Number Matching. Following signal preprocessing using Independent Component Analysis (ICA), power across various frequency bands was extracted using the Welch method. These features were used to analyze cognitive states in children with ASD in comparison to typically developing (TD) peers. To capture dynamic changes in attention over time, Morlet wavelet transform was applied, revealing distinct brain signal patterns. Machine learning classifiers were then developed to accurately distinguish between ASD and TD groups using the EEG data. Models included Support Vector Machine, K-Nearest Neighbors, Random Forest, an Ensemble method, and a Neural Network. Among these, the Ensemble method achieved the highest accuracy at 0.90. Feature importance analysis was conducted to identify the most influential EEG features contributing to classification performance. Based on these findings, an ASD map was generated to visually highlight the key EEG regions associated with ASD-related cognitive patterns. These findings highlight the potential of EEG-based models to capture ASD-specific neural and attentional patterns during learning, supporting their application in developing more personalized educational approaches. However, due to the limited sample size and participant heterogeneity, these findings should be considered exploratory. Future studies with larger samples are needed to validate and generalize the results. Full article

Psychogenic non-epileptic seizures (PNES) are often misdiagnosed as epileptic seizures (ES), leading to inappropriate treatment and delayed psychological care. To address this challenge, we analysed electroencephalogram (EEG) data from 74 patients (46 PNES, 28 ES) using one-minute preictal and interictal recordings per subject. Nine entropy measures (Sample, Fuzzy, Permutation, Dispersion, Conditional, Phase, Spectral, Rényi, and Wavelet entropy) were evaluated individually to classify PNES from ES using k-nearest neighbours, Naïve Bayes, linear discriminant analysis, logistic regression, support vector machine, random forest, multilayer perceptron, and XGBoost within a leave-one-subject-out cross-validation framework. In addition, a dynamic state, defined as the entropy difference between interictal and preictal periods, was examined. Sample, Fuzzy, Conditional, and Dispersion entropy were higher in PNES than in ES during interictal recordings (not significant), but significantly lower in the preictal (p < 0.05) and dynamic states (p < 0.01). Spatial mapping and permutation-based importance analyses highlighted O1, O2, T5, F7, and Pz as key discriminative channels. Classification performance peaked in the dynamic state, with Fuzzy entropy and support vector machine achieving the best results (balanced accuracy = 72.4%, F1 score = 77.8%, sensitivity = 74.5%, specificity = 70.4%). These results demonstrate the potential of entropy features for differentiating PNES from ES. Full article

In evaluating classroom teaching quality, students’ attention assessment is a critical indicator in education management, as it holds significant practical value for improving teaching methods and instructional quality. Electroencephalogram (EEG) signals can monitor dynamic neural activity in the brain in real time. Their objectivity and non-invasive nature make them particularly suitable for attention assessment in classroom environments. This article first provides a brief overview of existing attention assessment methods, and then presents a comprehensive review of the current research status and methodologies in EEG-based attention assessment, including signal acquisition, preprocessing, feature extraction and selection, classification, and evaluation. Subsequently, the challenges in EEG-based teaching attention assessment are discussed, including the acquisition of high-quality signals, multimodal data fusion, complexity of data, and hardware setups for deep learning method implementation. Finally, a multimodal classroom attention assessment method, which integrates EEG and eye movement signals, is proposed to enhance teaching management. Full article

Background: Stroke is a leading cause of seizures and epilepsy, both of which are linked to increased mortality, disability, and hospital readmissions. Early recognition and management of seizures in acute stroke are crucial for improving outcomes. Electroencephalogram (EEG) is not routinely used for post-stroke seizure monitoring and is typically initiated only after clinical suspicion arises, making bedside recognition essential. This scoping review aimed to map the existing literature on clinical methods used for identifying and observing early post-stroke seizures (EPSSs) at the bedside. Methods: We included literature involving adults with acute ischaemic stroke or primary intracerebral haemorrhage who were diagnosed or suspected of having inpatient EPSS. Searches were conducted in Medline, CINAHL, Embase, and the Cochrane Library for English-language publications up to April 2023. Eligible sources included primary research, case reports, systematic reviews, clinical guidelines, consensus statements, and expert opinion. Reference lists of included articles were also reviewed. Data were charted and synthesised to assess the scope, type, and gaps in the evidence. Results: Thirty papers met inclusion criteria: 17 research studies, six expert opinions, four case reports, and three clinical guidelines. Empirical evidence on clinical methods for seizure recognition and monitoring in acute stroke was limited. No studies evaluated the effectiveness of different approaches, and existing recommendations lacked detail and consensus. Conclusions: Accurate EPSS diagnosis is vital due to its impact on outcomes. This review highlights inconsistency in monitoring methods and a clear need for targeted research into effective clinical identification strategies in acute stroke care. Full article

Emotion recognition using electroencephalogram (EEG) signals has gained significant attention due to its potential applications in human–computer interaction (HCI), brain computer interfaces (BCIs), mental health monitoring, etc. Although deep learning (DL) techniques have shown impressive performance in this domain, they often require large datasets and high computational resources and offer limited interpretability, limiting their practical deployment. To address these issues, this paper presents a novel frequency-driven ensemble framework for electroencephalogram-based emotion recognition (FREQ-EER), an ensemble of lightweight machine learning (ML) classifiers with a frequency-based data augmentation strategy tailored for effective emotion recognition in low-data EEG scenarios. Our work focuses on the targeted analysis of specific EEG frequency bands and brain regions, enabling a deeper understanding of how distinct neural components contribute to the emotional states. To validate the robustness of the proposed FREQ-EER, the widely recognized DEAP (database for emotion analysis using physiological signals) dataset, SEED (SJTU emotion EEG dataset), and GAMEEMO (database for an emotion recognition system based on EEG signals and various computer games) were considered for the experiment. On the DEAP dataset, classification accuracies of up to 96% for specific emotion classes were achieved, while on the SEED and GAMEEMO, it maintained 97.04% and 98.6% overall accuracies, respectively, with nearly perfect AUC values confirming the frameworks efficiency, interpretability, and generalizability. Full article

Diagnosing dementia and recognizing substantial cognitive decline are challenging tasks. Thus, the objective of this study was to classify electroencephalograms (EEGs) recorded during a working memory task in 15 patients with mild cognitive impairment (MCogImp), 5 patients with vascular dementia (VasD), and 15 healthy controls (NC). Before creating spectrogram pictures from the EEG dataset, the data were subjected to preprocessing, which included preprocessing using conventional filters and the discrete wavelet transformation. The convolutional neural network (CNN) MobileNetV2 was employed in our investigation to identify features and assess the severity of dementia. The features were extracted from five layers of the MobileNetV2 CNN architecture—convolutional layers (‘Conv-1’), batch normalization (‘Conv-1-bn’), clipped ReLU (‘out-relu’), 2D Global Average Pooling (‘global-average-pooling2d1’), and fully connected (‘Logits’) layers. This was carried out to find the efficient features layer for dementia severity from EEGs. Feature extraction from MobileNetV2’s five layers was carried out using a decision tree (DT) and k-nearest neighbor (KNN) machine learning (ML) classifier, in conjunction with a MobileNetV2 deep learning (DL) network. The study’s findings show that the DT classifier performed best using features derived from MobileNetV2 with the 2D Global Average Pooling (global-average-pooling2d-1) layer, achieving an accuracy score of 95.9%. Second place went to the characteristics of the fully connected (Logits) layer, which achieved a score of 95.3%. The findings of this study endorse the utilization of deep processing algorithms, offering a viable approach for improving early dementia identification with high precision, hence facilitating the differentiation among NC individuals, VasD patients, and MCogImp patients. Full article

Background/Objectives: Mental fatigue during driving, whether passive (arising from monotony) or active (caused by cognitive overload), is a critical factor for road safety. Despite the growing interest in monitoring techniques based on neurophysiological signals, current biomarkers are primarily validated only for detecting passive mental fatigue under monotonous conditions. The objective of this study is to evaluate the sensitivity of the MDrow index, which is based on EEG Alpha band activity, previously validated for detecting passive mental fatigue, with respect to active mental fatigue, i.e., the mental fatigue occurring in cognitively demanding driving scenarios. Methods: A simulated experimental protocol was developed featuring three driving scenarios with increasing complexity: monotonous, urban, and urban with dual tasks. Nineteen participants took part in the experiment, during which electroencephalogram (EEG), photoplethysmogram (PPG), and electrodermal activity (EDA) data were collected in addition to subjective assessments, namely the Karolinska Sleepiness Scale (KSS) and the Driving Activity Load Index (DALI) questionnaires. Results:The findings indicate that MDrow shows sensitivity to both passive and active mental fatigue (p < 0.001), thereby demonstrating stability even in the presence of additional cognitive demands. Furthermore, Heart Rate (HR) and Heart Rate Variability (HRV) increased significantly during the execution of more complex tasks, thereby suggesting a heightened response to mental workload in comparison to mental fatigue alone. Conversely, electrodermal measures evidenced no sensitivity to mental fatigue-related changes. Conclusions: These findings confirm the MDrow index’s validity as an objective and continuous marker of mental fatigue, even under cognitively demanding conditions. Full article

This study aims to develop a personal comfort model driven by real-time electroencephalogram (EEG) signals for constructing built environments customized to individual emotional states and preferences. EEG signals from a single subject were collected at regular intervals under controlled environmental conditions—temperature, humidity, and illumination. Real-time deep learning methods processed the sensor data, enabling effective prediction of the user’s preferred conditions. Model evaluation showed reliable predictions on the personal dataset, allowing for optimized lighting that enhanced concentration and reduced stress. These findings indicate that EEG can inform personalized environmental modifications. This integration of EEG and deep learning provides objective, precise comfort assessment and supports immediate environmental adaptation. Full article

Ship waterway tunnels are a new and special type of navigation facility that has emerged in the construction of complex hubs in high mountain valleys and rivers, and they have demonstrated broad applications worldwide. Due to their characteristics of long length, a dim visual background, and enclosed space, waterway tunnels are prone to causing tension and cognitive fatigue in ship officers on watch, affecting their decision-making and control abilities. This study constructs the visual navigation environment of a typical waterway tunnel in China using a ship maneuvering simulator. By monitoring the physiological data of ship officers, such as through electroencephalograms (EEGs) and electrocardiograms (ECGs), the temporal and spatial patterns of their physiological and psychological characteristics are analyzed systematically. Based on this, a quantitative model of the cognitive load of a ship officer working in a waterway tunnel is constructed. At the same time, the navigation risk of waterway tunnels of different lengths is quantized based on the entropy weight TOPSIS method, and finally, high-risk sections in waterway tunnels are identified and visualized, providing theoretical support for the management of safety in waterway tunnels. Full article

Alexithymia is regarded as one of the risk factors for several prevalent mental disorders, and there is a growing need for convenient and objective methods to assess alexithymia. Therefore, this study proposes a method for constructing models to assess alexithymia using machine learning and electroencephalogram (EEG) signals. The explanatory variables for the models were functional connectivity calculated from resting-state EEG data, reflecting the default mode network (DMN). The functional connectivity was computed for each frequency band in brain regions estimated by source localization. The objective variable was defined as either low or high alexithymia severity. Explainable artificial intelligence (XAI) was used to analyze which features the models relied on for their assessments. The results indicated that the classification model suggested effective assessment depending on the threshold used to define low and high alexithymia. The maximum receiver operating characteristic area under the curve (ROC-AUC) score was 0.70. Furthermore, analysis of the classification model indicated that functional connectivity in the theta and gamma frequency bands, and specifically in the Left Hippocampus, was effective for alexithymia assessment. This study demonstrates the potential applicability of EEG signals and machine learning in alexithymia assessment. Full article

Driving fatigued is a prevalent issue frequently contributing to traffic accidents, prompting the development of automated fatigue detection methods based on various data sources, particularly reliable physiological signals. However, challenges in accuracy, robustness, and practicality persist, especially for cross-subject detection. Multimodal data fusion can enhance the effective estimation of driver fatigue. In this work, we leverage the advantages of multimodal signals to propose a novel Multimodal Attention Network (TMU-Net) for driver fatigue detection, achieving precise fatigue assessment by integrating electroencephalogram (EEG) and electrooculogram (EOG) signals. The core innovation of TMU-Net lies in its unimodal feature extraction module, which combines causal convolution, ConvSparseAttention, and Transformer encoders to effectively capture spatiotemporal features, and a multimodal fusion module that employs cross-modal attention and uncertainty-weighted gating to dynamically integrate complementary information. By incorporating uncertainty quantification, TMU-Net significantly enhances robustness to noise and individual variability. Experimental validation on the SEED-VIG dataset demonstrates TMU-Net’s superior performance stability across 23 subjects in cross-subject testing, effectively leveraging the complementary strengths of EEG (2 Hz full-band and five-band features) and EOG signals for high-precision fatigue detection. Furthermore, attention heatmap visualization reveals the dynamic interaction mechanisms between EEG and EOG signals, confirming the physiological rationality of TMU-Net’s feature fusion strategy. Practical challenges and future research directions for fatigue detection methods are also discussed. Full article

This study presents a novel deep learning framework for classifying visual images based on brain responses recorded through electroencephalogram (EEG) signals. The primary challenge in EEG-based visual pattern recognition lies in the inherent spatiotemporal variability of neural signals across different individuals and recording sessions, which severely limits the generalization capabilities of classification models. Our work specifically addresses the task of identifying which image category a person is viewing based solely on their recorded brain activity. The proposed methodology incorporates three primary components: first, a brain hemisphere asymmetry-based dimensional reduction approach to extract discriminative lateralization features while addressing high-dimensional data constraints; second, an advanced channel selection algorithm utilizing Fisher score methodology to identify electrodes with optimal spatial representativeness across participants; and third, a Dynamic Temporal Warping (DTW) alignment technique to synchronize temporal signal variations with respect to selected reference channels. Comprehensive experimental validation on a visual image classification task using a publicly available EEG-based visual classification dataset, ImageNet-EEG, demonstrates that the proposed disambiguation framework substantially improves classification accuracy while simultaneously enhancing model convergence characteristics. The integrated approach not only outperforms individual component implementations but also accelerates the learning process, thereby reducing training data requirements for EEG-based applications. These findings suggest that systematic spatiotemporal disambiguation represents a promising direction for developing robust and generalizable EEG classification systems across diverse neurological and brain–computer interface applications. Full article

The influence of seafarers’ emotions on work performance can lead to severe marine accidents. However, research on emotion recognition (ER) of seafarers remains insufficient, and existing studies only deploy single models and disregard the model’s uncertainty, which might lead to unreliable recognition. In this paper, a novel fusion framework for seafarer ER is proposed. Firstly, feature-level fusion using Electroencephalogram (EEG) and navigation data collected in a real navigation environment was conducted. Then, calibration is employed to mitigate the uncertainty of the outcomes. Secondly, a weight combination strategy for decision fusion was designed. Finally, we conduct a series of evaluations of the proposed model. The results showed that the average recognition performance across the three emotional dimensions, as measured by accuracy, precision, recall, and F1 score, reaches 85.14%, 84.43%, 86.27%, and 85.33%, respectively. The results demonstrate that the use of physiological and navigation data can effectively identify seafarers’ emotional states. Additionally, the fusion model compensates for the uncertainty of single models and enhances the performance of ER for seafarers, which provides a feasible path for the ER of seafarers. The findings of this study can be used to promptly identify the emotional state of seafarers and develop early warnings for bridge systems for shipping companies and help inform policy-making on human factors to enhance maritime safety. Full article

Electroencephalogram (EEG)-based identification offers a promising biometric solution by leveraging the uniqueness of individual brain activity patterns. This study proposes a framework based on a convolutional autoencoder (CAE) along with a traditional classifier for identifying individuals using EEG brainprints. The convolutional autoencoder extracts a compact and discriminative representation from the topographic data cubes that capture both spatial and temporal dynamics of neural oscillations. The latent tensor features extracted by the CAE are subsequently classified by a machine learning module utilizing Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) models. EEG data were collected under three conditions—resting state, music stimuli, and cognitive task—to investigate a diverse range of neural responses. Training and testing datasets were extracted from separate sessions to enable a true longitudinal analysis. The performance of the framework was evaluated using the Area Under the Curve (AUC) and accuracy (ACC) metrics. The effect of subject identifiability was also investigated. The proposed framework achieved a performance score up to a maximum AUC of 99.89% and ACC of 96.98%. These results demonstrate the effectiveness of the proposed automated subject-specific patterns in capturing stable EEG brainprints and support the potential of the proposed framework for reliable, session-independent EEG-based biometric identification. Full article