A simple generative model applied to motor-imagery brain-computer interfacing

In this study, a generative model is developed in order to translate neural activity into predictable device commands for brain-computer interface (BCI) applications. Generative approaches to BCI translation differ from widely-used discriminative approaches because they develop a model of brain activity dependent on the mental state of the user. Preliminary results indicate that two of three subjects were able to control the system at a level (>70% accurate) that makes it a viable option for practical use. The accuracy rate of the generative model is compared to the accuracy rate calculated offline using a linear discriminant approach. The advantages of such a system are discussed, and the ongoing opportunities for paradigm improvement are outlined.