Addressing imbalanced EEG data for improved microsleep detection: An ADASYN, FFT and LDA-based approach
Microsleep, brief lapses in consciousness lasting less than 15 seconds, are often accompanied by feelings of fatigue and are detectable through a deceleration in electroencephalogram (EEG) signal frequencies. Accurate identification of microsleep is critical for assessing driver alertness and preven...
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| Format: | Article |
| Language: | English |
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University of Diyala
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43891/ http://umpir.ump.edu.my/id/eprint/43891/1/Addressing%20Imbalanced%20EEG%20Data%20for%20Improved%20Microsleep%20Detection.pdf |
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| author | Hasan, Md Mahmudul Khandaker, Sayma Norizam, Sulaiman Hossain, Mirza Mahfuj Islam, Ashraful |
| author_facet | Hasan, Md Mahmudul Khandaker, Sayma Norizam, Sulaiman Hossain, Mirza Mahfuj Islam, Ashraful |
| author_sort | Hasan, Md Mahmudul |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Microsleep, brief lapses in consciousness lasting less than 15 seconds, are often accompanied by feelings of fatigue and are detectable through a deceleration in electroencephalogram (EEG) signal frequencies. Accurate identification of microsleep is critical for assessing driver alertness and preventing accidents. This paper introduces a novel approach to detecting driver microsleep by leveraging EEG signals and advanced machine learning techniques. The methodology begins with preprocessing raw EEG data to improve quality and balance, utilizing the ADASYN algorithm to address dataset imbalances. After preprocessing, features are extracted using Fast Fourier Transform (FFT), which provides a comprehensive frequency domain analysis of the EEG signals. For classification, Linear Discriminant Analysis (LDA) is employed to effectively distinguish between microsleep events and normal wakefulness based on the extracted features. The proposed framework was rigorously validated using a well-established publicly available EEG dataset, which included recordings from 76 healthy individuals. The validation results revealed a high testing accuracy of 92.71% in detecting microsleep episodes, demonstrating the effectiveness of the proposed approach. These results underscore the potential of combining EEG signal analysis with machine learning models for practical applications in monitoring driver alertness. The framework could significantly enhance driver safety by providing an effective tool for detecting microsleep and thereby reducing the risk of accidents caused by drowsy driving. This research highlights the promising application of advanced signal processing and machine learning techniques in the field of driver alertness monitoring. |
| first_indexed | 2025-11-15T03:53:30Z |
| format | Article |
| id | ump-43891 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:53:30Z |
| publishDate | 2024 |
| publisher | University of Diyala |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-438912025-02-24T02:27:21Z http://umpir.ump.edu.my/id/eprint/43891/ Addressing imbalanced EEG data for improved microsleep detection: An ADASYN, FFT and LDA-based approach Hasan, Md Mahmudul Khandaker, Sayma Norizam, Sulaiman Hossain, Mirza Mahfuj Islam, Ashraful TK Electrical engineering. Electronics Nuclear engineering Microsleep, brief lapses in consciousness lasting less than 15 seconds, are often accompanied by feelings of fatigue and are detectable through a deceleration in electroencephalogram (EEG) signal frequencies. Accurate identification of microsleep is critical for assessing driver alertness and preventing accidents. This paper introduces a novel approach to detecting driver microsleep by leveraging EEG signals and advanced machine learning techniques. The methodology begins with preprocessing raw EEG data to improve quality and balance, utilizing the ADASYN algorithm to address dataset imbalances. After preprocessing, features are extracted using Fast Fourier Transform (FFT), which provides a comprehensive frequency domain analysis of the EEG signals. For classification, Linear Discriminant Analysis (LDA) is employed to effectively distinguish between microsleep events and normal wakefulness based on the extracted features. The proposed framework was rigorously validated using a well-established publicly available EEG dataset, which included recordings from 76 healthy individuals. The validation results revealed a high testing accuracy of 92.71% in detecting microsleep episodes, demonstrating the effectiveness of the proposed approach. These results underscore the potential of combining EEG signal analysis with machine learning models for practical applications in monitoring driver alertness. The framework could significantly enhance driver safety by providing an effective tool for detecting microsleep and thereby reducing the risk of accidents caused by drowsy driving. This research highlights the promising application of advanced signal processing and machine learning techniques in the field of driver alertness monitoring. University of Diyala 2024-09 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/43891/1/Addressing%20Imbalanced%20EEG%20Data%20for%20Improved%20Microsleep%20Detection.pdf Hasan, Md Mahmudul and Khandaker, Sayma and Norizam, Sulaiman and Hossain, Mirza Mahfuj and Islam, Ashraful (2024) Addressing imbalanced EEG data for improved microsleep detection: An ADASYN, FFT and LDA-based approach. Diyala Journal of Engineering Sciences, 17 (3). 45 -57. ISSN 1999-8716. (Published) https://doi.org/10.24237/djes.2024.17304 https://doi.org/10.24237/djes.2024.17304 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Hasan, Md Mahmudul Khandaker, Sayma Norizam, Sulaiman Hossain, Mirza Mahfuj Islam, Ashraful Addressing imbalanced EEG data for improved microsleep detection: An ADASYN, FFT and LDA-based approach |
| title | Addressing imbalanced EEG data for improved microsleep detection: An ADASYN, FFT and LDA-based approach |
| title_full | Addressing imbalanced EEG data for improved microsleep detection: An ADASYN, FFT and LDA-based approach |
| title_fullStr | Addressing imbalanced EEG data for improved microsleep detection: An ADASYN, FFT and LDA-based approach |
| title_full_unstemmed | Addressing imbalanced EEG data for improved microsleep detection: An ADASYN, FFT and LDA-based approach |
| title_short | Addressing imbalanced EEG data for improved microsleep detection: An ADASYN, FFT and LDA-based approach |
| title_sort | addressing imbalanced eeg data for improved microsleep detection: an adasyn, fft and lda-based approach |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/43891/ http://umpir.ump.edu.my/id/eprint/43891/ http://umpir.ump.edu.my/id/eprint/43891/ http://umpir.ump.edu.my/id/eprint/43891/1/Addressing%20Imbalanced%20EEG%20Data%20for%20Improved%20Microsleep%20Detection.pdf |