Grouped Automatic Relevance Determination and Its Application in Channel Selection for P300 BCIs

During the development of a brain-computer interface, it is beneficial to exploit information in multiple electrode signals. However, a small channel subset is favored for not only machine learning feasibility, but also practicality in commercial and clinical BCI applications. An embedded channel selection approach based on grouped automatic relevance determination is proposed. The proposed Gaussian conjugate group-sparse prior and the embedded nature of the concerned Bayesian linear model enable simultaneous channel selection and feature classification. Moreover, with the marginal likelihood (evidence) maximization technique, hyper-parameters that determine the sparsity of the model are directly estimated from the training set, avoiding time-consuming cross-validation. Experiments have been conducted on P300 speller BCIs. The results for both public and in-house datasets show that the channels selected by our techniques yield competitive classification performance with the state-of-the-art and are biologically relevant to P300.