A new hand-modeled learning framework for driving fatigue detection using EEG signals

Abstract

Fatigue detection is a critical application area for machine learning, and variable input data have been utilized to detect fatigue. One of the most commonly used inputs for fatigue detection is electroencephalography (EEG) signals. The main objective of this study is to accurately detect fatigue using a hand-crafted framework. To achieve this, a new signal classification framework has been proposed, and its performance has been tested on an EEG fatigue detection dataset. Wavelet packet decomposition with 16 mother wavelet functions has been utilized to extract features from the frequency domain and create a multilevel feature extraction method to calculate frequency subbands. To generate classification results, two validation techniques, tenfold cross-validation and leave-one-subject-out (LOSO) validation, have been applied to attain robust classification results. The proposed framework achieved high classification performance with 99.90% and 82.08% classification accuracies using tenfold CV and LOSO CV, respectively. Furthermore, the classification performance of each used method in our framework has been analyzed to understand the driving fatigue classification effect of the machine learning functions used. The proposed framework attained superior classification results, demonstrating its efficacy in accurately detecting fatigue.