Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks

Abstract In recent years, sleepiness during driving has become a main cause for traffic accidents. However, the fact is that we know very little yet about the electrophysiological marker for assessing diver sleepiness. Previous studies and our researches have shown that alpha blocking phenomenon and alpha wave attenuation-disappearance phenomenon represent two different sleepiness levels, the relaxed wakefulness and the sleep onset, respectively. This paper proposes a novel model for driver sleepiness detection based on electroencephalography (EEG) and electrooculography (EOG) signals. Our model aims to track the change in alpha waves and differentiate the two alpha-related phenomena. Continuous wavelet transform is adopted to extract features from physiological signals in both time and frequency domains. Meanwhile, Long-Short Term Memory (LSTM) network is introduced to deal with temporal information of EEG and EOG signals. To deal with insufficient physiological sample problem, generative adversarial network (GAN) is used to augment the training dataset. Experimental results indicate that the F1 score for detecting start and end points of alpha waves reaches to around 95%. And Conditional Wasserstein GAN (CWGAN) we adopted was effective in augmenting dataset and boost classifier performance. Meanwhile, our LSTM classifier achieved a mean accuracy of 98% for classifying end points of alpha waves under leave-one-subject-out cross validation.