Key result
The LightFD classifier achieved better classification performance and decision efficiency for EEG driver mental states identification compared to traditional classifiers like SVM, CNN, GRU, and LMNN.
Why the study?
Fatigue driving causes road traffic accidents, but finding an effective method or model to timely and efficiently detect driver mental states remains a challenge.
The LightFD classifier demonstrates improved performance and efficiency for real-time EEG-based driver fatigue detection.
May support efficient EEG fatigue monitoring; leaves open prospective validation before practice adoption.
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. The comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI).
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Zeng et al. (2019) studied Driver fatigue. LightFD classifier vs. Traditional classifiers (SVM, CNN, GRU, LMNN) was evaluated on Classification performance and decision efficiency. The LightFD classifier achieved better classification performance and decision efficiency for EEG driver mental states identification compared to traditional classifiers like SVM, CNN, GRU, and LMNN.
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