CROSS-SUBJECT EEG-BASED FATIGUE CLASSIFICATION USING MACHINE LEARNING, RIEMANNIAN GEOMETRY, AND COMPACT DEEP NEURAL NETWORKS
DOI:
https://doi.org/10.37943/25EJPK6829Keywords:
electroencephalogram, fatigue detection, drowsiness, deep learning, Riemannian geometryAbstract
Drowsiness reduces efficiency in perceptual processing, reaction time, and executive control, posing risks in safety-critical domains such as driving and long-duration monitoring tasks. EEG-based fatigue detection has emerged as a powerful approach for quantifying early neurophysiological signs of vigilance decline, yet many proposed algorithms are insufficiently evaluated in strictly subject-independent conditions. To address this gap, we systematically compare classical machine learning models, Riemannian geometry-based classification, and compact deep neural architectures on a publicly available electroencephalography (EEG) dataset containing 11 subjects. We employ a rigorous leave-one-subject-out (LOSO) protocol, ensuring that no individual contributes information simultaneously to the training and test sets.
The study evaluates logistic regression, support vector machines with radial-basis kernels, random forests, a Log-Euclidean Riemannian classifier, EEGNet, a transformer encoder, and a bidirectional long short-term memory (BiLSTM) with temporal attention. Across folds, accuracy and macro-F1 scores were calculated and summarized with mean and standard deviation. The BiLSTM-attention model achieved the highest performance (accuracy ; macro-F1 ) but only moderately exceeded EEGNet and the classical baselines. Wilcoxon signed-rank tests revealed no significant difference between EEGNet and BiLSTM (), although BiLSTM significantly outperformed the transformer model (). Analysis of error structure demonstrated a notable asymmetry with 295 false positives and 184 false negatives aggregated across folds.
Band-specific analysis revealed theta activity as the strongest contributor to class separation, followed by delta and alpha rhythms. Channel-importance analysis indicated that posterior and paracentral regions were consistently more informative. These findings highlight that model complexity does not guarantee superior performance in small datasets with large inter-subject variability. The study provides a transparent, fully reproducible baseline for future fatigue-classification research and demonstrates the practical relevance of compact architectures and Riemannian geometry in low-data conditions.
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