Adaptive CSP with subspace alignment for subject-to-subject transfer in motor imagery brain-computer interfaces

Abstract

In brain-computer interfaces, adapting a classifier from one user to another is challenging but essential to reduce training time for new users. Common Spatial Patterns (CSP) is a widely used method for learning spatial filters for user specific feature extraction but the performance is degraded when applied to a different user. This paper proposes a novel Adaptive Selective Common Spatial Pattern (ASCSP) method to update the covariance matrix using selected candidates. Subspace alignment is then applied to the extracted features before classification. The proposed method outperforms the standard CSP and adaptive CSP algorithms previously proposed. Visualization of extracted features is provided to demonstrate how subspace alignment contributes to reduce the domain variance between source and target domains.