Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution
Microsleeps are brief lapses in awareness that pose significant risks, particularly in activities requiring continuous attention, such as driving. These episodes are common in sleep-deprived individuals and can lead to catastrophic outcomes. Electroencephalography (EEG) is a promising technique for...
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| Format: | Article |
| Language: | English |
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IEEE
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/45035/ http://umpir.ump.edu.my/id/eprint/45035/1/Real-time%20EEG%20signal%20analysis%20for%20microsleep%20detection.pdf |
| _version_ | 1848827240340520960 |
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| author | Hassan, Md Mahmudul Islam, Md Nahidul Norizam, Sulaiman Hossain, Mirza Mahfuj Mendes, Jorge M. |
| author_facet | Hassan, Md Mahmudul Islam, Md Nahidul Norizam, Sulaiman Hossain, Mirza Mahfuj Mendes, Jorge M. |
| author_sort | Hassan, Md Mahmudul |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Microsleeps are brief lapses in awareness that pose significant risks, particularly in activities requiring continuous attention, such as driving. These episodes are common in sleep-deprived individuals and can lead to catastrophic outcomes. Electroencephalography (EEG) is a promising technique for detecting microsleeps due to its high temporal resolution, allowing real-time brain activity monitoring. The study aims to develop a lightweight version of the model to reduce computational costs and provide faster detection, enabling quicker intervention to prevent accidents in safety-critical environments. We propose a customized deep learning model, Hyper-Opt-ANN, designed to detect microsleep episodes from EEG signals. The model is evaluated across five time windows (1 second, 2 seconds, 3 seconds, 4 seconds, and 5 seconds), with the 4 seconds window showing the best performance. The Hyper-Opt-ANN model achieved a significant accuracy of 97.33%, demonstrating its efficacy and potential for accurate microsleep detection using EEG signals. This method significantly outperforms traditional approaches and has potential applications in safety-critical domains. This study demonstrates the feasibility of using EEG signals and advanced deep learning models for detecting microsleep and enhancing safety in high-risk environments. |
| first_indexed | 2025-11-15T03:57:34Z |
| format | Article |
| id | ump-45035 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:57:34Z |
| publishDate | 2025 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-450352025-07-08T01:30:22Z http://umpir.ump.edu.my/id/eprint/45035/ Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution Hassan, Md Mahmudul Islam, Md Nahidul Norizam, Sulaiman Hossain, Mirza Mahfuj Mendes, Jorge M. QA75 Electronic computers. Computer science QP Physiology TK Electrical engineering. Electronics Nuclear engineering Microsleeps are brief lapses in awareness that pose significant risks, particularly in activities requiring continuous attention, such as driving. These episodes are common in sleep-deprived individuals and can lead to catastrophic outcomes. Electroencephalography (EEG) is a promising technique for detecting microsleeps due to its high temporal resolution, allowing real-time brain activity monitoring. The study aims to develop a lightweight version of the model to reduce computational costs and provide faster detection, enabling quicker intervention to prevent accidents in safety-critical environments. We propose a customized deep learning model, Hyper-Opt-ANN, designed to detect microsleep episodes from EEG signals. The model is evaluated across five time windows (1 second, 2 seconds, 3 seconds, 4 seconds, and 5 seconds), with the 4 seconds window showing the best performance. The Hyper-Opt-ANN model achieved a significant accuracy of 97.33%, demonstrating its efficacy and potential for accurate microsleep detection using EEG signals. This method significantly outperforms traditional approaches and has potential applications in safety-critical domains. This study demonstrates the feasibility of using EEG signals and advanced deep learning models for detecting microsleep and enhancing safety in high-risk environments. IEEE 2025 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/45035/1/Real-time%20EEG%20signal%20analysis%20for%20microsleep%20detection.pdf Hassan, Md Mahmudul and Islam, Md Nahidul and Norizam, Sulaiman and Hossain, Mirza Mahfuj and Mendes, Jorge M. (2025) Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution. IEEE Access, 13. pp. 66354-66372. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2025.3559619 https://doi.org/10.1109/ACCESS.2025.3559619 |
| spellingShingle | QA75 Electronic computers. Computer science QP Physiology TK Electrical engineering. Electronics Nuclear engineering Hassan, Md Mahmudul Islam, Md Nahidul Norizam, Sulaiman Hossain, Mirza Mahfuj Mendes, Jorge M. Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution |
| title | Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution |
| title_full | Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution |
| title_fullStr | Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution |
| title_full_unstemmed | Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution |
| title_short | Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution |
| title_sort | real-time eeg signal analysis for microsleep detection: hyper-opt-ann as a key solution |
| topic | QA75 Electronic computers. Computer science QP Physiology TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/45035/ http://umpir.ump.edu.my/id/eprint/45035/ http://umpir.ump.edu.my/id/eprint/45035/ http://umpir.ump.edu.my/id/eprint/45035/1/Real-time%20EEG%20signal%20analysis%20for%20microsleep%20detection.pdf |