Decision of braking intensity during simulated driving based on analysis of neural correlates

Recently neurophysiological studies have been concerned with using brain signals for driving assistance technologies. These studies verified that neurophysiological characteristics could be used for detection of emergency situations during simulated driving. However, it is hard to develop the braking assistant system which could control the vehicle continuously using this approach. In this article, the method for decoding of driver´s braking intention based on analysis of neural correlates is proposed to control the braking of vehicle continuously. The participants´ braking intention is decoded by kernel ridge regression (KRR) model to overcome the limitation of classification approach. In addition, the combination of three different features is employed to enhance the decoding performance. The decoding performances are evaluated by the correlation coefficient (r-value) and the normalized root-mean square error (NRMSE).