EEG Signal Based Human-Computer Interaction based Autonomous Driving Control System

EEG signal analysis and driving intention prediction are the main challenges of human-centric autonomous intelligent driving control systems. This paper uses spectral characteristics and EEG functional brain networks to recognise straight-line, left-turn, and right-turn driving. The method classifies and identifies driving processes using support vector machines and Gaussian mixture models. Simulated driving results show that the suggested technique accurately identifies many driving activities. Out of 16 individuals, straight-line and turning recognition accuracy is regularly above 82%, peaking at 86.66%. The recognition accuracy for left- and right- and turn motions peaks at 77.95%. Examining critical area dependency during turning processes shows lateralisation differences. Specifically, left turns require more inter-brain region activity than right turns. While travelling straight, the left hemisphere is slightly more active than the right. The research findings shed light on motorists' cognitive abilities during turns and can be used to study driver states and behaviour on different road surfaces.