Hidden Markov Support Vector Machines for Self-Paced Brain Computer Interfaces

Abstract

Brain Computer Interfaces (BCI) aim at providing a means to control devices with brain signals. Self-paced BCIs, as opposed to synchronous ones, have the advantage of being operational at all times and not only at specific system-defined periods. Traditionally, in the BCI field, a sliding window over the brain signal is used to detect the intention of the user at a given time. This approach ignores the temporal correlations between the adjacent time windows. This paper proposes a novel approach to classify self-paced BCI data using structural support vector machines. Our proposed approach considers the history of the brain signals in the context of sequential supervised learning to better detect the intention of the user from his/her brain signals. We have compared our proposed model to the sliding window approach with Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) classifiers. Using data collected from 4 individuals form BCI competition IV, it is shown that the F1 score of our approach is significantly better than the sliding window approach. The average F1 score of our method across all subjects is 0.3 and 0.5 higher than the sliding window with SVM and LDA classifiers, respectively.