IMPROVED CLASSIFICATION IN TACTILE BCIS USING A NOISY LABEL MODEL

Tactile BCIs have gained recent popularity in the BCI community due to the advantages of using a stimulation medium which does not inhibit the users visual or auditory senses, is naturally inconspicuous, and can still be used by a person who may be visually or auditorily impaired. While many systems have been proposed which utilize the P300 response elicited through an oddball task, these systems struggle to classify user responses with accuracies comparable to many visual stimulus based systems. In this study, we model the tactile ERP generation as label noise and develop a novel BCI paradigm for binary communication designed to minimize label confusion. The classification model is based on a modified Gaussian mixture and trained using expectation maximization (EM). Finally, we show after testing on multiple subjects that this approach yields cross-validated accuracies for all users which are significantly above chance and suggests that such an approach is robust and reliable for a variety of binary communication-based applications.