EEG-Based Driver Mental Fatigue Recognition in COVID-19 Scenario Using a Semi-Supervised Multi-View Embedding Learning Model

With the spread of COVID-19 in recent years, wearing masks has increased the difficulty of driver mental fatigue recognition. Electroencephalogram (EEG) signal has become an important physiological signal index to reflect the driver’s mental state. However, the drivers’ EEG data is plagued by inadequate labels and multi-view data, which makes classification difficult. To solve this problem, this study proposes a
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emi-supervised
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ulti-view
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parse regularization and
g
raph embedding learning (SMSG) model. To obtain discriminative feature representations of semi-supervised EEG data, SMSG fully mines diverse information from multiple views based on sparse regularization embedding and graph embedding technology. SMSG employs the graph embedding to capture the discriminative structure and local manifold structure on multi-view data. Furthermore, SMSG learns the common shared regularization embedding and private regularization embedding factors to preserve the consistency and diversity of the multi-view data. Through self-adaptive learning, the weights of each view can be directly solved adaptively. This works also introduces kernel trick to project the SMSG model into the nonlinear reproducing kernel Hilbert space (RKHS), which can obtain more approximate EEG feature representation. Experiments on the real dataset verify the effectiveness of the SMSG model for EEG-based driver mental fatigue recognition.