Key result
A single EEG channel (T6) using a gradient boosted decision tree ensemble classifier achieved an average classification accuracy of >94% for driver's fatigue detection.
Why the study?
Does an ensemble classifier using a single EEG channel accurately detect driver's fatigue in healthy adults?
Does an ensemble classifier using a single EEG channel accurately detect driver's fatigue in healthy adults?
A single EEG channel combined with an optimal ensemble classifier can accurately detect driver fatigue, offering potential for portable monitoring systems.
May enable real-time driver fatigue monitoring; leaves open large-scale validation before safety-system adoption.
Driver's fatigue detection, based on electroencephalography (EEG) signals, is a worthy field of research to study evidence regarding how to exactly pre‐warn and avoid casualties nowadays. In this study, an EEG‐based system of perfect performance and good stability for evaluating driver's fatigue with only one electrode by ensemble learning method is proposed. Given that EEG signals are unstable and non‐linear that using several common entropy measurements to analyse EEG signals is more appropriate including spectral entropy, approximate entropy, sample entropy and fuzzy entropy. In this study, unlike other methods using a single classifier, three ensemble approaches (bagging, random forest and boosting) based on three base classifiers were employed and compared. A driving simulator in this study was used for 12 healthy and adult subjects to perform a continuous simulated driving experiment for 1–2 h. The experimental results show that the proposed method can make use of only one electrode (T6) by gradient boosted DT for driver's fatigue detection, while the average classification accuracy is >94%. The findings of this study indicated that a single EEG channel with optimal ensemble classifier may be a good candidate for usage in the portable system for driver's fatigue detection.
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Wang et al. (2018) studied Driver's fatigue (n=12). Single EEG channel (T6) with gradient boosted DT ensemble classifier vs. Other ensemble approaches (bagging, random forest) and base classifiers was evaluated on Average classification accuracy for driver's fatigue detection. A single EEG channel (T6) using a gradient boosted decision tree ensemble classifier achieved an average classification accuracy of >94% for driver's fatigue detection.
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