EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals. (arXiv:2211.03878v1 [cs.AI])

A lack of driver's vigilance is the main cause of most vehicle crashes.
Electroencephalography(EEG) has been reliable and efficient tool for drivers'
drowsiness estimation. Even though previous studies have developed accurate and
robust driver's vigilance detection algorithms, these methods are still facing
challenges on following areas: (a) small sample size training, (b) anomaly
signal detection, and (c) subject-independent classification. In this paper, we
propose a generalized few-shot model, namely EEG-Fest, to improve
aforementioned drawbacks. The EEG-Fest model can (a) classify …

arxiv attention driver eeg fest network