mlsanityData to 14 Oct 2026

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

arXiv 2211.03878 ↗First posted 7 Nov 2022cs.LG eess.IV eess.SPcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 71 references that are in the map. First posted on arXiv 7 Nov 2022.

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19 of 71 references.

References · 19

  1. 2017Attention Is All You Needcited by 60,361
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  3. 2019A Closer Look at Few-shot Classificationcited by 684
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  6. 2018Meta-Transfer Learning for Few-Shot Learningcited by 306
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  11. 2014Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognitioncited by 111
  12. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
7 more
  1. 2016A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOGcited by 33
  2. 2018Multi-channel EEG recordings during a sustained-attention driving taskcited by 32
  3. 2021EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classificationcited by 16
  4. 2017Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)cited by 9
  5. 2021Attention-based Neural Network for Driving Environment Complexity Perceptioncited by 9
  6. 2020A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysiscited by 4
  7. 2020A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagerycited by 2

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