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. ,e 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)
