  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.
