EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals
This work addresses driver drowsiness detection, a critical safety issue, but is incremental as it builds on existing few-shot and attention methods for EEG analysis.
The paper tackles driver vigilance estimation from EEG signals by proposing EEG-Fest, a few-shot attention network that addresses small sample size, anomaly detection, and subject-independent classification, achieving state-of-the-art accuracies up to 94% on benchmark datasets.
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.