EEG-based Universal Prediction Model of Emergency Braking Intention for Brain-controlled Vehicles

Electroencephalogram (EEG)-based prediction of driver emergency braking intention can help develop an assistance system to improve driving safety for brain-controlled vehicles. However, existing studies are focused on how to build an individual detection model for each participant. In this paper, to build a universal model, a convolutional neural network (CNN) is used to extract the features of brain signals and build the universal model. Experimental results from 13 subjects show that the proposed CNN-based method outperforms the linear discriminant analysis (LDA)-based method and has a comparable performance with individual models. This work lays a foundation for future developments of an EEG-based universal model of driver emergency braking intention detection.