EEG-signals based cognitive workload detection of vehicle driver using deep learning

Vehicle driver's ability to maintain optimal performance and attention is essential to ensure the safety of the traffic. Electroencephalography (EEG) signals have been proven to be effective in evaluating human's cognitive state under specific tasks. In this paper, we propose the use of deep learning on EEG signals to detect the driver's cognitive workload under high and low workload tasks. Data used in this research are collected throughout multiple driving sessions conducted on a high fidelity driving simulator. Preliminary experimental results conducted on only 4 channels of EEG show that the proposed system is capable of accurately detecting the cognitive workload of the driver with an enormous potential for improvement.