Single Trial P300 Recognition in a Virtual Environment

Virtual reality (VR) provides immersive and controllable experimental environments. It expands the bounds of possible evoked potential (EP) experiments by providing complex, dynamic environments in order to study cognition without sacriicing environmental control. The addition of quick, on-line analysis enables feedback to subjects, making the system we present ideal for safe Brain Computer Interface (BCI) research. In this context, we describe an experiment to recognize the existence of P300 EP epochs at red stoplights and the absence of this signal at yellow stoplights in a virtual driving environment. In order to determine the plausibility of single trial on-line P300 epoch analysis in the artifact ridden driving environment, we have compared the use of Independent Component Analysis (ICA), a Kalman lter, a robust Kalman lter, and correlation with the stoplight averages for recognition ability oo-line. We report that while all methods perform better than correlation , the robust Kalman lter gives the highest recognition accuracy and discuss future work in this context.