Composite Filter Bank Common Spatial Pattern for motor imagery-based Brain-Computer Interface

Abstract

The Filter Bank Common Spatial Pattern (FBCSP) algorithm employs multiple spatial filters across a bank of band-pass filtered EEC using the CSP algorithm. This is as opposed to the commonly used single spatial filter from band-pass filtered EEC. Hence, the FBCSP yields improved performance in autonomous selection of key temporal-spatial discriminative EEC characteristics in motor imagery-based Brain-Computer Interfaces (MI-BCI). However, the multiple spatial filtering involves multiple estimations of covariance matrices across the different frequency bands. Thus, the use of multiple spatial filters increases the sensitivity of the FBCSP algorithm to noise, artifacts and outliers compared to the CSP algorithm. Furthermore, the multiple spatial patterns are also less interpretable than a single spatial pattern. Hence this paper proposes a Composite FBCSP algorithm that employs a single spatial filter instead of multiple spatial filters. The composite spatial filter is computed from a weighted sum of covariance matrices whereby the weights are determined from the mutual information across selected frequency band. The performance of the Composite FBCSP is compared to the FBCSP on a publicly available dataset and data collected from 5 healthy subjects using session-to-session transfer kappa values on the independent test data. The results revealed improvements in accuracy and interpretability in the spatial patterns.