# EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training

arXiv: https://arxiv.org/abs/1909.11456 · HTML: https://mlsanity.com/paper/1909.11456/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2502.09203/) (2025)
- [Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey](https://mlsanity.com/paper/2212.03176/) (2022)
- [Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2412.07231/) (2024)
- [Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation](https://mlsanity.com/paper/2212.13887/) (2022)
- [Barycentric-alignment and reconstruction loss minimization for domain generalization](https://mlsanity.com/paper/2109.01902/) (2021)
- [Studying Drowsiness Detection Performance while Driving through Scalable Machine Learning Models using Electroencephalography](https://mlsanity.com/paper/2209.04048/) (2022)
- [Improving Generalization of Drowsiness State Classification by Domain-Specific Normalization](https://mlsanity.com/paper/2312.09461/) (2023)
- [Reading ability detection using eye-tracking data with LSTM-based few-shot learning](https://mlsanity.com/paper/2409.08798/) (2024)
- [Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2601.07556/) (2026)

## Abstract (arXiv)

Drowsy driving is pervasive, and also a major cause of traffic accidents. Estimating a driver's drowsiness level by monitoring the electroencephalogram (EEG) signal and taking preventative actions accordingly may improve driving safety. However, individual differences among different drivers make this task very challenging. A calibration session is usually required to collect some subject-specific data and tune the model parameters before applying it to a new subject, which is very inconvenient and not user-friendly. Many approaches have been proposed to reduce the calibration effort, but few can completely eliminate it. This paper proposes a novel approach, feature weighted episodic training (FWET), to completely eliminate the calibration requirement. It integrates two techniques: feature weighting to learn the importance of different features, and episodic training for domain generalization. Experiments on EEG-based driver drowsiness estimation demonstrated that both feature weighting and episodic training are effective, and their integration can further improve the generalization performance. FWET does not need any labelled or unlabelled calibration data from the new subject, and hence could be very useful in plug-and-play brain-computer interfaces.