Review of algorithms for predicting fatigue using EEG

2024/01/30 by Ildar Rakhmatulin, Rakhmatulin, Ildar
Engineering · #Advanced Sensor and Control Systems #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Fault Detection and Control Systems #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2402.09443

openalex publication_date 2024/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Fatigue detection is of paramount importance in enhancing safety, productivity, and well-being across diverse domains, including transportation, healthcare, and industry. This scientific paper presents a comprehensive investigation into the application of machine learning algorithms for the detection of physiological fatigue using Electroencephalogram (EEG) signals. The primary objective of this study was to assess the efficacy of various algorithms in predicting an individual's level of fatigue based on EEG data.

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