Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal
Fatigued drivers can often cause long-distance accidents worldwide. Fatigue states are the primary cause of highway accidents. This study is conducted to provide a comprehensive and reliable fatigue state detection system to avoid accidents and make a good decision. Three machine learning algorithms...
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| Format: | Article |
| Language: | English |
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Arqii Publication
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40473/1/AMS-480_Published_3_December_2023.pdf |
| _version_ | 1796996258446966784 |
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| author | Hasan, Md Mahmudul Hossain, Mirza Mahfuj Norizam, Sulaiman |
| author_facet | Hasan, Md Mahmudul Hossain, Mirza Mahfuj Norizam, Sulaiman |
| author_sort | Hasan, Md Mahmudul |
| collection | UMP |
| description | Fatigued drivers can often cause long-distance accidents worldwide. Fatigue states are the primary cause of highway accidents. This study is conducted to provide a comprehensive and reliable fatigue state detection system to avoid accidents and make a good decision. Three machine learning algorithms were applied to seventy-six subjects' electroencephalogram (EEG) readings to test their performance. A preprocessing stage extracts relevant information before applying machine learning algorithms to the signal. Three analytical methods were employed in this study, specifically the Decision Tree, the K-Nearest Neighbors and the Random Forest. The study revealed that employing all the classifiers resulted in a satisfactory accuracy rate compared to existing state-of-the-art methods for detecting fatigue states. The classification accuracy using Decision Tree for four classes and two classes were achieved at 88.61% and 88.21% respectively, which can make this EEG-based technology a practical and dependable solution for real-time applications. |
| first_indexed | 2024-03-06T13:14:01Z |
| format | Article |
| id | UMPir40473 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:14:01Z |
| publishDate | 2023 |
| publisher | Arqii Publication |
| record_format | dspace |
| spelling | UMPir404732024-02-22T06:56:29Z http://umpir.ump.edu.my/id/eprint/40473/ Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal Hasan, Md Mahmudul Hossain, Mirza Mahfuj Norizam, Sulaiman TK Electrical engineering. Electronics Nuclear engineering Fatigued drivers can often cause long-distance accidents worldwide. Fatigue states are the primary cause of highway accidents. This study is conducted to provide a comprehensive and reliable fatigue state detection system to avoid accidents and make a good decision. Three machine learning algorithms were applied to seventy-six subjects' electroencephalogram (EEG) readings to test their performance. A preprocessing stage extracts relevant information before applying machine learning algorithms to the signal. Three analytical methods were employed in this study, specifically the Decision Tree, the K-Nearest Neighbors and the Random Forest. The study revealed that employing all the classifiers resulted in a satisfactory accuracy rate compared to existing state-of-the-art methods for detecting fatigue states. The classification accuracy using Decision Tree for four classes and two classes were achieved at 88.61% and 88.21% respectively, which can make this EEG-based technology a practical and dependable solution for real-time applications. Arqii Publication 2023 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40473/1/AMS-480_Published_3_December_2023.pdf Hasan, Md Mahmudul and Hossain, Mirza Mahfuj and Norizam, Sulaiman (2023) Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal. Applications of Modelling and Simulation, 7. pp. 178-189. ISSN 2600-8084. (Published) https://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/480 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Hasan, Md Mahmudul Hossain, Mirza Mahfuj Norizam, Sulaiman Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal |
| title | Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal |
| title_full | Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal |
| title_fullStr | Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal |
| title_full_unstemmed | Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal |
| title_short | Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal |
| title_sort | fatigue state detection through multiple machine learning classifiers using eeg signal |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/40473/1/AMS-480_Published_3_December_2023.pdf |
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