A Semi-Supervised Wasserstein Generative Adversarial Network for Classifying Driving Fatigue from EEG signals

Predicting driver's cognitive states using deep learning from electroencephalography (EEG) signals is considered this paper. To address the challenge posed by limited labeled training samples, a semi-supervised Wasserstein Generative Adversarial Network with gradient penalty (sWGAN-GP) is proposed. The proposed sWGAN-GP includes a classifier with the shared architecture with the discriminator in GAN and its loss function enables the augmentation of limited training samples with generated EEG samples during training, thus resulting in improved classification performance. The several modeling challenges including frequency artifacts and training instability, are also considered. The test results on predicting the alert and drowsy states from a simulated driving experiment demonstrate improved prediction performance and training stability over the baseline semi-supervised GAN and a convolutional neural network model.