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)
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Abstract
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.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Channel selection; Correlation coefficient; Driver fatigue; Electroencephalogram (EEG); Ensemble classifier; Random subspace k-NN |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 28 Apr 2022 06:21 |
| Last Modified: | 29 Apr 2022 07:47 |
| URI: | http://umpir.ump.edu.my/id/eprint/33117 |
| Download Statistic: | View Download Statistics |
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