EEG-Based Driver Drowsiness Estimation Using Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based method for estimating driver drowsiness from EEG signals, utilizing spectral features and ensemble learning to improve accuracy and computational efficiency.

Contribution

It extends EEGNet from classification to regression and employs a spectral meta-learner to enhance driver drowsiness estimation performance.

Findings

Spectral features outperform raw EEG in regression accuracy.

Ensemble of EEGNets improves drowsiness estimation.

Proposed method achieves high accuracy with reduced computation.

Abstract

Deep learning, including convolutional neural networks (CNNs), has started finding applications in brain-computer interfaces (BCIs). However, so far most such approaches focused on BCI classification problems. This paper extends EEGNet, a 3-layer CNN model for BCI classification, to BCI regression, and also utilizes a novel spectral meta-learner for regression (SMLR) approach to aggregate multiple EEGNets for improved performance. Our model uses the power spectral density (PSD) of EEG signals as the input. Compared with raw EEG inputs, the PSD inputs can reduce the computational cost significantly, yet achieve much better regression performance. Experiments on driver drowsiness estimation from EEG signals demonstrate the outstanding performance of our approach.