Real-time EEG signal analysis for microsleep detection: Hyper-Opt-ANN as a key solution

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)

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Abstract

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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: EEG signal; hyper-Opt-ANN; Microsleep detection; Parameter optimization; Time-window selection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QP Physiology
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Institute of Postgraduate Studies
Faculty of Electrical and Electronic Engineering Technology
Depositing User: Mrs. Nurul Hamira Abd Razak
Date Deposited: 08 Jul 2025 01:30
Last Modified: 08 Jul 2025 01:30
URI: http://umpir.ump.edu.my/id/eprint/45035
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