000			03486ntm a2200337 i 4500
003			MY-KuUP
005			20251230175348.0
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007			ta
008			251230t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010194 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKEE .M34 2025 r Thesis
100	1		_aMd Mahmudul Hasan, _eauthor.
245	1	0	_aHybrid bigru-bilstm with multi-head attention for eeg-based microsleep detection / _cMd Mahmudul Hasan
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axv, 133 pages : _billustrations ; _c30 cm. + _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Electrical & Electronics Engineering Technology
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2025
504			_aInclude bibliographical reference
520	3		_aMicrosleep poses a serious risk to high-risk occupations like driving, flying, and industrial work. Microsleep can result in serious accidents and fatalities and are frequently brought on by exhaustion, irregular circadian rhythms, or tedious work. This emphasizes the critical need for precise and timely detection systems. The accuracy, versatility, and practicality of current detection techniques are still constrained, despite developments in related domains. By putting forth an intelligent EEG-based system for early microsleep identification and alarm production, this study overcomes these constraints. This study presents a thorough detection framework that uses the sensitivity of electroencephalogram (EEG) signals to identify unique cortical brain activity patterns. To guarantee robustness, the process combines both controlled experimental data and publicly available EEG datasets, starting with data gathering utilizing an eight-channel Unicorn Black EEG headset. Utilizing sophisticated feature extraction methods, significant insights are extracted from unprocessed EEG signals. To improve detection accuracy, both conventional machine learning models including Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) and deep learning models including Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and a new Hybrid BiGRU-BiLSTM with Multi-Head Attention are used. Comparative evaluations demonstrate the higher performance of the suggested hybrid design, which attains 98.89% classification accuracy, 99.16% specificity, 97.77% MCC, 99.05% precision, 98.58% recall, and 98.81% F1-score. Further evidence of its generalizability comes from validation on a publicly available dataset. Additionally, the study offers a hardware feedback system prototype and an intuitive graphical user interface (GUI) for real-time driver alertness monitoring and accident prevention. In summary, this study fills important gaps in existing microsleep detection techniques and offers a scalable solution that can be implemented in safety-critical areas. Future approaches include investigating cross-domain applications in industrial safety systems, transportation, and aviation, as well as diversifying datasets and improving hardware integration.
610	2	0	_aFaculty of Electrical & Electronics Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c103813 _d103819