Review of algorithms for predicting fatigue using EEG
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913234940854272 |
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| author | Rakhmatulin, Ildar |
| author_facet | Rakhmatulin, Ildar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09443 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Review of algorithms for predicting fatigue using EEG Rakhmatulin, Ildar Signal Processing Artificial Intelligence Machine Learning 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. |
| title | Review of algorithms for predicting fatigue using EEG |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.09443 |