Electroencephalogram-Based Motor Imagery Brain-Computer Interface Using Multivariate Iterative Filtering and Spatial Filtering

In motor imagery (MI)-based brain-computer interface (BCI), common spatial pattern (CSP) is most popularly used for discriminant feature extraction. However, the performance of CSP depends on the operational frequency bands, which are selected manually or set to a broad frequency range in most of the previously developed applications. Due to subject to subject or even trial to trial variability of frequency band affected by MI task, these methods suffer from the poor performance. We have proposed a novel approach, using combination of multivariate iterative filtering (MIF) and CSP (MIFCSP), to automatically select optimal frequency bands based on MIF which can be further used for discriminant feature extraction. MIF decomposes the signal into several multivariate intrinsic mode functions, from which features are extracted using CSP. We select the minimum number of most significant features for which highest classification accuracy is achieved. Subsequently, linear discriminant analysis (LDA) classifier is used to classify different MI tasks. Experimental results for BCI competition IV data set 2a and BCI competition III-IIIa are presented. For left-hand versus right-hand MI classification, proposed MIFCSP method provides 83.18% and 84.44% average accuracy, respectively. Superior classification performance confirms that MIFCSP is a promising candidate for MI BCI application.