Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels
Nowadays, many studies have been conducted to assess driver fatigue, as it has become one of the leading causes of traffic crashes. However, with the use of advanced features and machine learning approaches, EEG signals may be processed in an effective way, allowing fatigue to be detected promptly a...
| Автори: | , , , , |
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| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
International Information and Engineering Technology Association
2021
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| Предмети: | |
| Онлайн доступ: | http://umpir.ump.edu.my/id/eprint/33117/1/Random%20subspace%20K-NN%20based%20ensemble%20classifier%20for%20driver%20fatigue%20detection%20utilizing%20selected%20EEG%20channels.pdf |
| _version_ | 1825814151019102208 |
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| author | Rashid, Mamunur Mahfuzah, Mustafa Norizam, Sulaiman Nor Rul Hasma, Abdullah Rosdiyana, Samad |
| author_facet | Rashid, Mamunur Mahfuzah, Mustafa Norizam, Sulaiman Nor Rul Hasma, Abdullah Rosdiyana, Samad |
| author_sort | Rashid, Mamunur |
| collection | UMP |
| description | Nowadays, many studies have been conducted to assess driver fatigue, as it has become one of the leading causes of traffic crashes. However, with the use of advanced features and machine learning approaches, EEG signals may be processed in an effective way, allowing fatigue to be detected promptly and efficiently. An optimal channel selection approach and a competent classification algorithm might be viewed as a critical aspect of efficient fatigue detection by the driver. In the present framework, a new channel selection algorithm based on correlation coefficients and an ensemble classifier based on random subspace k-nearest neighbour (k-NN) has been presented to enhance the classification performance of EEG data for driver fatigue detection. Moreover, power spectral density (PSD) was used to extract the feature, confirming the presented method's robustness. Additionally, to make the fatigue detection system faster, we conducted the experiment in three different time windows, including 0.5s, 0.75s, and 1s. It was found that the proposed method attained classification accuracy of 99.99% in a 0.5 second time window to identify driver fatigue by means of EEG. The outstanding performance of the presented framework can be used effectively in EEG-based driver fatigue detection. |
| first_indexed | 2024-03-06T12:54:37Z |
| format | Article |
| id | UMPir33117 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:54:37Z |
| publishDate | 2021 |
| publisher | International Information and Engineering Technology Association |
| record_format | dspace |
| spelling | UMPir331172022-04-29T07:47:28Z http://umpir.ump.edu.my/id/eprint/33117/ Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels Rashid, Mamunur Mahfuzah, Mustafa Norizam, Sulaiman Nor Rul Hasma, Abdullah Rosdiyana, Samad T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Nowadays, many studies have been conducted to assess driver fatigue, as it has become one of the leading causes of traffic crashes. However, with the use of advanced features and machine learning approaches, EEG signals may be processed in an effective way, allowing fatigue to be detected promptly and efficiently. An optimal channel selection approach and a competent classification algorithm might be viewed as a critical aspect of efficient fatigue detection by the driver. In the present framework, a new channel selection algorithm based on correlation coefficients and an ensemble classifier based on random subspace k-nearest neighbour (k-NN) has been presented to enhance the classification performance of EEG data for driver fatigue detection. Moreover, power spectral density (PSD) was used to extract the feature, confirming the presented method's robustness. Additionally, to make the fatigue detection system faster, we conducted the experiment in three different time windows, including 0.5s, 0.75s, and 1s. It was found that the proposed method attained classification accuracy of 99.99% in a 0.5 second time window to identify driver fatigue by means of EEG. The outstanding performance of the presented framework can be used effectively in EEG-based driver fatigue detection. International Information and Engineering Technology Association 2021-10 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33117/1/Random%20subspace%20K-NN%20based%20ensemble%20classifier%20for%20driver%20fatigue%20detection%20utilizing%20selected%20EEG%20channels.pdf Rashid, Mamunur and Mahfuzah, Mustafa and Norizam, Sulaiman and Nor Rul Hasma, Abdullah and Rosdiyana, Samad (2021) Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels. Traitement du Signal, 38 (5). pp. 1259-1270. ISSN 0765-0019. (Published) https://doi.org/10.18280/ts.380501 https://doi.org/10.18280/ts.380501 |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Rashid, Mamunur Mahfuzah, Mustafa Norizam, Sulaiman Nor Rul Hasma, Abdullah Rosdiyana, Samad Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels |
| title | Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels |
| title_full | Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels |
| title_fullStr | Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels |
| title_full_unstemmed | Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels |
| title_short | Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels |
| title_sort | random subspace k nn based ensemble classifier for driver fatigue detection utilizing selected eeg channels |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/33117/1/Random%20subspace%20K-NN%20based%20ensemble%20classifier%20for%20driver%20fatigue%20detection%20utilizing%20selected%20EEG%20channels.pdf |
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