Use of the evoked potential P3 component for control in a virtual apartment.

Virtual reality (VR) may prove useful for training individuals to use a brain-computer interface (BCI). It could provide complex and controllable experimental environments during BCI research and development as well as increase user motivation. In the study reported here, we examined the robustness of the evoked potential P3 component in virtual and nonvirtual environments. We asked subjects to control several objects or commands in a virtual apartment. Our results indicate that there are no significant differences in the P3 signal between subjects performing a task while immersed in VR versus subjects looking at a computer monitor. This indicates the robustness of the P3 signal over different environments. For an online control task, the performance in a VR environment was not significantly different from performance when looking at a computer monitor. There was, however, a more significant result when the subject's head view of the virtual world was fixed (p < 0.05) when compared with looking at a computer monitor. We also found that subjects' self-reported qualitative experiences did not necessarily match their objective performance. Six out of nine subjects liked the VR environment better, but only one of these subjects performed the best in this environment. The possible ramifications of this, as well as plans for future work, are discussed.

Changing the P300 brain computer interface.

Brain-computer interfaces (BCIs) are now feasible for use as an alternative control option for those with severe motor impairments. The P300 component of the evoked potential has proven useful as a control signal. Individuals do not need to be trained to produce the signal, and it is fairly stable and has a large evoked potential. Even with recent signal classification advances, on-line experiments with P300-based BCIs remain far from perfect. We present two potential methods for improving control accuracy. Experimental results in an evoked potential BCI, used to control items in a virtual apartment, show a reduced response exists when items are accidentally controlled. The presence of a P300-like signal in response to goal items means that it can be used for automatic error correction. Preliminary results from an interface experiment using three different button configurations for a yes/no BCI task show that the configuration of buttons may affect on-line signal classification. These results will be discussed in light of the special considerations needed when working with an amyotrophic lateral sclerosis (ALS) patient.