Does a FAWT-based machine learning method improve detection of driver fatigue using EEG signals?
A novel FAWT-based machine learning approach using EEG signals accurately detects driver fatigue with high accuracy and AUC.
Globally, 14%–20% of road accidents are mainly due to driver fatigue, the causes of which are instance sickness, travelling for long distance, boredom as a result of driving along the same route consistently, lack of enough sleep, etc. This article presents a flexible analytic wavelet transform (FAWT) -based advanced machine learning method using single modality neurophysiological brain electroencephalogram signals to detect the driver fatigues (i. e. , FATIGUE and REST) and to alarm the driver at the earliest to prevent the risks during driving. First, signals of undertaking study groups are subjected to the FAWT that separates the signals into LP and HP channels. Subsequently, relevant subband frequency components with proper setting of tuning parameters are extracted. Then, comprehensive low order features which are statistically significant forp< 0. 05, are evaluated from the input subband searched space and embedded them to various ensemble methods under multiboost strategy. Results are evaluated in terms of various parameters including accuracy, F-score, AUC, and. Results show that the proposed approach is promising in classification and it achieves optimum individual accuracies of 97. 10% and 97. 90% in categorizing FATIGUE and REST states with F-score of 97. 50%, AUC of 0. 975, andof 0. 950. Comparison of the proposed method with the prior methods in the context of feature, accuracy, and modality profiles undertaken, indicates the effectiveness and reliability of the proposed method for real-world applications.
Subaşı et al. (Thu,) studied this question.
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