000			03916ntm a2200337 i 4500
003			MY-KuUP
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008			260525t20262026my a|||fr|||| 000 0 eng d
020			_aTHE0010526 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKEE .R34 2025 r Thesis
100	1		_aRafiuddin Bin Abdubrani, _eauthor.
245	1	0	_aDriver fatigue classification based on electroencephalogram signal / _cRafiuddin Bin Abdubrani
264		1	_aKuantan, Pahang : _bUMPSA, _c2026
264		4	_c© 2026
300			_axiv, 164 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 (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2026
504			_aInclude bibliographical reference
520	3		_aDriver fatigue is a critical risk factor for road accidents, underscoring the need for accurate and reliable detection systems. This research proposes an advanced EEG-based driver fatigue classification framework that integrates enhanced signal pre-processing with a hybrid deep learning architecture. The novelty of this research lies in the integration of an ICA–CWT pre-processing pipeline with enhanced modified z-score normalisation to produce high-fidelity EEG features, combined with a hybrid DBN–DQN architecture that significantly improves fatigue detection performance under crosssubject variability. The methodology employs independent component analysis combined with continuous wavelet transform (ICA-CWT) using the Morlet mother wavelet to improve artefact removal and time-frequency resolution. An enhanced modified z-score (EMZS) normalisation technique is introduced to stabilise feature distributions by reducing the influence of outliers. Extracted features from delta, theta, and alpha sub-bands are processed by a hybrid model combining a deep belief network (DBN) and a deep Q-network (DQN). The DBN performs hierarchical feature learning, while the DQN enhances decision-making through reinforcement learning principles, enabling improved classification accuracy. Experimental evaluation was conducted using stratified 5-fold cross-validation on a publicly available EEG dataset, with performance compared against traditional deep learning baselines (CNN and RNN) under identical conditions. The proposed method addresses key limitations of CNN/RNN models, such as poor frequency resolution and high sensitivity to noise, resulting in lower robustness in EEG-based fatigue studies. The proposed DBN-DQN framework achieved an average accuracy of 98.21%, precision of 98.15%, sensitivity (recall) of 98.24%, specificity of 98.18%, and an F1-score of 98.19%. These results significantly outperform CNN and RNN baselines, demonstrating the superiority of the proposed hybrid approach in EEGbased fatigue detection. The findings validate that integrating ICA-CWT, EMZS normalisation, and a hybrid DBN-DQN architecture substantially enhances fatigue classification performance. Future research should focus on real-time implementation, multimodal integration with additional physiological signals, and deployment of lightweight models for embedded systems. Overall, the study strengthens the link between methodology, results, and application by showing that ICA–CWT improves feature clarity, EMZS enhances data robustness, and the hybrid DBN–DQN architecture delivers highly reliable classification suitable for practical deployment in future intelligent transportation systems.
610	2	0	_aFaculty of Electrical & Electronics Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c104891 _d104897