Cognitive Workload Detection from Raw EEG-Signals of Vehicle Driver using Deep Learning

Electroencephalography (EEG) signals have been proven to be effective in evaluating human's cognitive state under specific tasks. Conventional classification models utilized for EEG classification heavily rely on signal pre-processing and hand-designed features. In this paper, we propose an end-to-end deep neural network which is capable of classifying multiple types of cognitive workload of a vehicle driver and the context of driving using only raw EEG signals as its input without any pre-processing nor the need for conventional hand-designed features. Data used in this study are collected throughout multiple driving sessions conducted on a high-fidelity driving simulator. Experimental results conducted on 4 channels of raw EEG data show that the proposed model is capable of accurately detecting the cognitive workload of a driver and the context of driving.