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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 androbust driver's vigilance detection algorithms, these methods are still facingchallenges on following areas: (a) small sample size training, (b) anomalysignal detection, and (c) subject-independent classification. In this paper, wepropose a generalized few-shot model, namely EEG-Fest, to improveaforementioned drawbacks. The EEG-Fest model can (a) classify the querysample's drowsiness with a few samples, (b) identify whether a query sample isanomaly signals or not, and (c) achieve subject independent classification. Theproposed algorithm achieves state-of-the-art results on the SEED-VIG datasetand the SADT dataset. The accuracy of the drowsy class achieves 92% and 94% for1-shot and 5-shot support samples in the SEED-VIG dataset, and 62% and 78% for1-shot and 5-shot support samples in the SADT dataset.

EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals