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 |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10962219/ |
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| author | Md Mahmudul Hasan Md Nahidul Islam Norizam Sulaiman Mirza Mahfuj Hossain Jorge M. Mendes |
| author_facet | Md Mahmudul Hasan Md Nahidul Islam Norizam Sulaiman Mirza Mahfuj Hossain Jorge M. Mendes |
| author_sort | Md Mahmudul Hasan |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-db188b9f304642e2b34ce6790221f80d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-db188b9f304642e2b34ce6790221f80d2025-08-20T03:18:24ZengIEEEIEEE Access2169-35362025-01-0113663546637210.1109/ACCESS.2025.355961910962219Real-Time EEG Signal Analysis for Microsleep Detection: Hyper-Opt-ANN as a Key SolutionMd Mahmudul Hasan0https://orcid.org/0009-0004-6865-3785Md Nahidul Islam1https://orcid.org/0000-0003-1552-0335Norizam Sulaiman2https://orcid.org/0000-0002-0625-2327Mirza Mahfuj Hossain3https://orcid.org/0009-0009-7391-5635Jorge M. Mendes4https://orcid.org/0000-0003-2251-3803Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, MalaysiaFaculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, MalaysiaFaculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, MalaysiaDepartment of Computer Science and Engineering, Jashore University of Science and Technology, Jashore, BangladeshComprehensive Health Research Centre (CHRC), NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, PortugalMicrosleeps 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.https://ieeexplore.ieee.org/document/10962219/Microsleep detectionEEG signalhyper-Opt-ANNparameter optimizationtime-window selection |
| spellingShingle | Md Mahmudul Hasan Md Nahidul Islam Norizam Sulaiman Mirza Mahfuj Hossain Jorge M. Mendes Real-Time EEG Signal Analysis for Microsleep Detection: Hyper-Opt-ANN as a Key Solution IEEE Access Microsleep detection EEG signal hyper-Opt-ANN parameter optimization time-window selection |
| 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 | Microsleep detection EEG signal hyper-Opt-ANN parameter optimization time-window selection |
| url | https://ieeexplore.ieee.org/document/10962219/ |
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