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

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.

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.

Analysis of participant data identified a discriminative subset of 65 features from the EC EEG. The model achieved an accuracy of 90.37% in classifying deeply fatigued versus fully recovered states, significantly outperforming the EO-based model (86.54%). The MMR demonstrated a significant negative correlation with SSS scores (r_s = -0.358, p = 0.020) and a positive correlation with sleep duration (r_s = 0.494, p < 0.001).

The results of this study demonstrate the superior efficacy of EC rs-EEG for monitoring cumulative fatigue, establishing a quantifiable EEG-sleep relationship and supporting the practical feasibility of this framework for occupational fatigue risk assessment.