  In this study we demonstrate a novel Brain Computer Interface (BCI) approach
to detect driver distraction events to improve road safety. We use a commercial
wireless headset that generates EEG signals from the brain. We collected real
EEG signals from participants who undertook a 40-minute driving simulation and
were required to perform different tasks while driving. These signals are
segmented into short windows and labelled using a time series classification
(TSC) model. We studied different TSC approaches and designed a Long-term
Recurrent Convolutional Network (LCRN) model for this task. Our results showed
that our LRCN model performs better than the state of the art TSC models at
detecting driver distraction events.

Signal Processing;Machine Learning;Machine Learning