EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training

Cui, Yuqi; Xu, Yifan; Wu, Dongrui · 2019 · Crossref

DOI: 10.1109/tnsre.2019.2945794

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Summary

This paper addresses the challenge of estimating driver drowsiness using electroencephalogram (EEG) signals without requiring subject-specific calibration data. Drowsy driving is a major cause of traffic accidents, and while EEG provides direct measures of brain states, individual differences make it difficult to develop generic estimators. Traditional approaches often require a calibration session to tune model parameters for each new user, which is inconvenient and hinders the development of plug-and-play brain-computer interfaces. The authors propose Feature Weighted Episodic Training (FWET), a method designed to eliminate this calibration requirement by improving domain generalization. The study utilizes data from 15 healthy subjects participating in a simulated driving experiment. Participants drove on a virtual highway while EEG signals were recorded. Drowsiness was quantified using a Drowsiness Index (DI) derived from reaction times to random lane-departure events. The authors extracted power spectral density features in the theta and alpha bands from 30-second EEG windows. The proposed FWET framework integrates two techniques: Feature Weighting (FW), which assigns different importance weights to EEG channels based on their correlation with drowsiness, and Episodic Training (ET), a meta-learning approach that trains a model to generalize across different subjects by simulating domain shifts during training. The model consists of a feature transformation network and a regression network, trained simultaneously to minimize prediction error while adapting to subject-specific variations. Experimental results demonstrate that FWET significantly outperforms baseline methods, including k-nearest neighbors, ridge regression, and standard aggregation training. Using leave-one-subject-out cross-validation, FWET achieved the lowest average Root Mean Squared Error (RMSE) of 0.2332 and the highest Pearson Correlation Coefficient (CC) of 0.5989. This represents a 6.9% improvement in RMSE and a 5.7% improvement in CC over the next best method, FW-AGG. The study also found that while FWET performed well for most subjects, it was sensitive to outliers in feature distributions for specific individuals, though performance improved when such outliers were removed. Statistical tests confirmed the significance of these improvements. The significance of this work lies in its ability to enable accurate, calibration-free drowsiness estimation. By eliminating the need for labeled or unlabeled data from new subjects, FWET facilitates the deployment of real-time, plug-and-play monitoring systems. This approach advances the field of affective computing and driver safety by providing a robust solution to the problem of inter-subject variability in EEG-based classification and regression tasks.

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Topics

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• drowsiness detection algorithms
• drowsiness

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software
• Theoretical Contribution: computational model