Real-time Detection of Driver\'s Movement Intention in Response to Traffic Lights

Movements are preceded by certain brain states that can be captured through various neuroimaging techniques. Brain-Computer Interfaces can be designed to detect the movement intention brain state during driving, which could be beneficial in improving the interaction between a smart car and its driver, by providing assistance in-line with the driver's intention. In this paper, we present an Electroencephalogram based decoder of such brain states preceding movements performed in response to traffic lights in two experiments: in a car simulator and a real car. The results of both experiments (N=10: car simulator, N=8: real car) confirm the presence of anticipatory Slow Cortical Potentials in response to traffic lights for accelerating and braking actions. Single-trial classification performance exhibits an Area Under the Curve (AUC) of 0.71{+/-}0.14 for accelerating and 0.75{+/-}0.13 for braking. The AUC for the real car experiment is 0.63{+/-}0.07 and 0.64{+/-}0.13 for accelerating and braking respectively. Moreover, we evaluated the performance of real-time decoding of the intention to brake during online experiments only in the car simulator, yielding an average accuracy of 0.64{+/-}0.1. This paper confirms the existence of the anticipatory slow cortical potentials and the feasibility of single-trial detection these potentials in driving scenarios.