# Review of algorithms for predicting fatigue using EEG

arXiv: https://arxiv.org/abs/2402.09443 · HTML: https://mlsanity.com/paper/2402.09443/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [Mental State Recognition via Wearable EEG](https://mlsanity.com/paper/1602.00985/) (2016)

## Abstract (arXiv)

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.