Hybrid Brain Computer Interface via Bayesian Integration of EEG and Eye Gaze

We describe a hybrid brain computer interface that integrates information from a four-class motor imagery based EEG classifier with information about gaze trajectories from an eye tracker. The novel aspect of this system is that no explicit gaze behavior is required of the user. Rather, the natural gaze behavior of the user integrated probabilistically to smooth the noisy classification results from the motor imagery based EEG. The goal is to provide for a more natural interaction with the BCI system than if gaze were used as an explicit command signal, as is commonly done. Our results on a 2D cursor control task show that integration of gaze information significantly improves task completion accuracy and reduces task completion time. In particular, our system achieves over 80% target completion accuracy on a cursor control task requiring guidance to one of 12 targets.