EEG-based Classification of Drivers Attention using Convolutional Neural Network

TL;DR

This study demonstrates that CNN models trained on raw EEG data can accurately classify driver attention levels in real-time, with potential for calibration-free BCI applications in road safety.

Contribution

It introduces a CNN-based approach using raw EEG signals for driver attention classification, outperforming traditional spectral methods and showing promise for calibration-free BCI systems.

Findings

CNN on raw EEG achieved 89% accuracy with kinesthetic feedback

Inter-subject transfer learning reached 75% accuracy

Raw EEG signals are effective for real-time attention detection

Abstract

Accurate detection of a drivers attention state can help develop assistive technologies that respond to unexpected hazards in real time and therefore improve road safety. This study compares the performance of several attention classifiers trained on participants brain activity. Participants performed a driving task in an immersive simulator where the car randomly deviated from the cruising lane. They had to correct the deviation and their response time was considered as an indicator of attention level. Participants repeated the task in two sessions; in one session they received kinesthetic feedback and in another session no feedback. Using their EEG signals, we trained three attention classifiers; a support vector machine (SVM) using EEG spectral band powers, and a Convolutional Neural Network (CNN) using either spectral features or the raw EEG data. Our results indicated that the CNN model trained on raw EEG data obtained under kinesthetic feedback achieved the highest accuracy (89%). While using a participants own brain activity to train the model resulted in the best performances, inter-subject transfer learning still performed high (75%), showing promise for calibration-free Brain-Computer Interface (BCI) systems. Our findings show that CNN and raw EEG signals can be employed for effective training of a passive BCI for real-time attention classification.