SLEEP-SAFE: Self-Supervised Learning for Estimating Electroencephalogram Patterns With Structural Analysis of Fatigue Evidence

Recently, deep learning frameworks have gained increasing attentions from electroencephalogram (EEG)-based driver’s fatigue estimation, thanks to their unprecedented feature extraction calibre. However, it is still challenging to develop session- and/or subject-independent system, because of the complex structural characteristics of EEG signals. In this regard, this work proposes a novel deep convolutional neural network architecture that can learn spectro-spatio-temporal representation of the vigilance EEG signals, thereby achieving a powerful mental status recognition ability. Specifically, the proposed network pretrained via two novel self-supervision pretext tasks. Further, both differential entropy and EEG signal itself are exploited to acquire rich features. To demonstrate the validity of the proposed methods, this work conduct intra- and inter-subject classification experiments using a publicly available dataset. In the exhaustive experiments, we observed that our proposed framework has practical availability. Specifically, our proposed multiple path structure improved the model’s performance by $3\sim 7$ percentage points (%p) in the session-independent setting and by $6\sim 9$ %p in the subject-independent setting. Besides, the novel self-supervised learning strategy enhanced the performance by $10\sim 17$ and $12\sim 16$ %p in the session- and subject-independent case, respectively. Furthermore, this work also investigate strengths and society-friendliness of our proposed framework.