Single Trial P3 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 sacrificing environmental control. The addition of quick, on-line analysis enables feed-back 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 P3 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 P3 epoch analysis in the artifact ridden driving environment, we have compared the use of Independent Component Analysis (ICA), a Kalman filter, a robust Kalman filter, and correlation with the stoplight averages for recognition ability off-line. We report that while all methods perform better than correlation, the robust Kalman filter gives the highest recognition accuracy and discuss future work in this context. This research was supported by NIH/PHS grant1-P41-RR09283. It was also facilitated in part by a National Physical Science Consortium Fellowship and by stipend support from NASA Goddard Space Flight Center.