Predicting the EEG Level of a Driver Based on Driving Information

Driving is an essential activity in today's busy and complex society, and it demands physical and mental abilities, collectively known as a driving workload. For safe and comfortable driving, it would be useful to detect when drivers are being overloaded. Analyzing driver's workload using an electroencephalograph (EEG) is useful for this purpose. However, it is very inconvenient to obtain an EEG during actual driving, since the measuring device needs to be attached to the driver. In this paper, we develop a model to predict the driver's EEG level utilizing basic information obtained while the vehicle is being driven. We divided the EEG values into two classes, "normal" and "overload", and extracted useful features from the vehicle driving information, such as engine RPM, vehicle speed, lane changes, and turns. A classification model using a support vector machine was built to predict normal and overload states during actual driving. We evaluated the performance of the proposed method using field-of-test data collected when driving on actual roads, and suggest directions for future research based on an analysis of the experimental results.