Frequency-based Incremental Feature Extraction and Machine Learning in EEG for Better Driver Fatigue Detection

This empirical research study mainly evaluates the effectiveness of three frequency-based incremental type of feature extraction and machine learning classification and prediction approaches, i.e. LSTM, RNN, and AdaBoostM2 in detecting driver’s fatigue while monitoring driver’s vigilance using EEG data from the SEED-VIG dataset. This dataset comprises EEG signaling data samples which are categorized into three states of driver’s alertness, i.e. drowsiness, tiredness and awake state. The study aims to determine which model can most accurately detect driver fatigue, a critical factor in preventing road accidents. By starting from the lowest and second lowest frequency bands in EEG data for feature selection, and through incremental feature extraction and deep learning approaches in detecting the EEG data, AdaBoostM2 outperformed the other two prediction models. It achieved the highest prediction accuracy of 97.03%, compared with 95.85% and 94.28% for LSTM and RNN respectively. These results showed that AdaBoostM2 is a superior tool for real-time driver vigilance monitoring.