Kernel-based relevant feature extraction to support Motor Imagery classification

Abstract

Brain Computer Interface (BCI) has become one of the most interesting alternatives to support automatic systems able to interpret brain functions. Recently, the Motor Imagery (MI) paradigm is a widely topic of interest as a tool to develop BCI-based systems. Here, we present a relevant feature extraction methodology, termed MI discrimination using kernel relevance analysis (MIDKRA), to support MI classification in BCI systems. For such a purpose, a similarity criterion to rank the contribution of EEG features for classifying an MI paradigm is employed. The introduced approach includes the supervised information regarding the MI paradigm to find out a relevant set of features encoding discriminative information. We model the EEG recordings by considering both time and time-frequency representations. Moreover, a k nearest-neighbor classifier is carried out to validate the proposed feature relevance approach. Experimental results carried out on two different BCI databases, a well-known public MI data and an Emotiv-based dataset built by us, demonstrate that MIDKRA outperforms state of the art methods and it is a suitable alternative to support straightforward BCI systems.