  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 the query
sample's drowsiness with a few samples, (b) identify whether a query sample is
anomaly signals or not, and (c) achieve subject independent classification. The
proposed algorithm achieves state-of-the-art results on the SEED-VIG dataset
and the SADT dataset. The accuracy of the drowsy class achieves 92% and 94% for
1-shot and 5-shot support samples in the SEED-VIG dataset, and 62% and 78% for
1-shot and 5-shot support samples in the SADT dataset.
