Volta

Assessing Cumulative Mental Fatigue via EEG-Based Machine Learning in a Multiday High-Intensity Contest

Background:Cumulative mental fatigue poses a significant threat to safety, productivity, and health in the workplace. In this study, we aimed to establish a robust machine learning framework using optimized resting-state electroencephalography (rs-EEG) features to detect such fatigue and to validate a 4-day high-stress cognitive competition paradigm for its induction.Methods:EEG signals were recorded from participants under eyes-closed (EC) and eyes-open (EO) conditions during fatigue and recovery phases. We extracted 544 features spanning power spectral density, entropy, and nonlinear complexity. Support Vector Machine Recursive Feature Elimination (SVM-RFE) was used for feature selection. The derived model index (Mean Model Result, MMR) was correlated with a subjective sleepiness index (the Stanford Sleepiness Scale, SSS) and sleep duration.Results:Analysis of participant data identified a discriminative subset …

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