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Related Experiment Video

Updated: Jul 8, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
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Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

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

Xiaodong Yang1, Jie Zhou1, Zhan Chen1,2

  • 1Naval Medical Center of PLA, Second Military Medical University, 200433 Shanghai, China.

Journal of Integrative Neuroscience
|July 7, 2026
PubMed
Summary

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This summary is machine-generated.

This study developed a machine learning model using resting-state electroencephalography (rs-EEG) to detect mental fatigue with 90.37% accuracy. The findings support using rs-EEG for workplace fatigue assessment.

Area of Science:

  • Neuroscience
  • Machine Learning
  • Occupational Health

Background:

  • Cumulative mental fatigue is a significant workplace hazard impacting safety and productivity.
  • Developing objective measures for fatigue detection is crucial for risk management.

Purpose of the Study:

  • To create a machine learning framework for detecting mental fatigue using optimized resting-state electroencephalography (rs-EEG) features.
  • To validate a high-stress cognitive competition paradigm for inducing and studying fatigue.

Main Methods:

  • EEG signals were recorded under eyes-closed (EC) and eyes-open (EO) conditions.
  • 544 features were extracted, and Support Vector Machine Recursive Feature Elimination (SVM-RFE) was used for selection.
  • The model index (MMR) was correlated with the Stanford Sleepiness Scale (SSS) and sleep duration.
Keywords:
electroencephalography (EEG)machine learningmental fatiguesleep durationsupport vector machine

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Main Results:

  • A subset of 65 EC EEG features achieved 90.37% accuracy in classifying fatigue states.
  • The EC model outperformed the EO model (86.54% accuracy).
  • MMR showed significant negative correlation with SSS and positive correlation with sleep duration.

Conclusions:

  • Eyes-closed resting-state EEG is highly effective for monitoring cumulative mental fatigue.
  • A quantifiable relationship between EEG markers and sleep was established.
  • The framework is practically feasible for occupational fatigue risk assessment.