Calibration-Free EEG-based Driver Drowsiness Detection with Online Test-Time Adaptation

TL;DR

This paper introduces a calibration-free EEG-based driver drowsiness detection system that uses online test-time adaptation to improve accuracy across different subjects without prior calibration.

Contribution

The authors propose a novel online test-time adaptation framework that updates batch normalization parameters and incorporates prototype learning for robust, subject-independent drowsiness detection.

Findings

Achieves an average F1-score of 81.73% on the dataset.

Outperforms all baseline methods by 11.73%.

Demonstrates effective adaptation to unseen subjects during test time.

Abstract

Drowsy driving is a growing cause of traffic accidents, prompting recent exploration of electroencephalography (EEG)-based drowsiness detection systems. However, the inherent variability of EEG signals due to psychological and physical factors necessitates a cumbersome calibration process. In particular, the inter-subject variability of EEG signals leads to a domain shift problem, which makes it challenging to generalize drowsiness detection models to unseen target subjects. To address these issues, we propose a novel driver drowsiness detection framework that leverages online test-time adaptation (TTA) methods to dynamically adjust to target subject distributions. Our proposed method updates the learnable parameters in batch normalization (BN) layers, while preserving pretrained normalization statistics, resulting in a modified configuration that ensures effective adaptation during test time. We incorporate a memory bank that dynamically manages streaming EEG segments, selecting samples based on their reliability determined by negative energy scores and persistence time. In addition, we introduce prototype learning to ensure robust predictions against distribution shifts over time. We validated our method on the sustained-attention driving dataset collected in a simulated environment, where drowsiness was estimated from delayed reaction times during monotonous lane-keeping tasks. Our experiments show that our method outperforms all baselines, achieving an average F1-score of 81.73\%, an improvement of 11.73\% over the best TTA baseline. This demonstrates that our proposed method significantly enhances the adaptability of EEG-based drowsiness detection systems in non-i.i.d. scenarios.