Motor imagery classification using feature relevance analysis: An Emotiv-based BCI system

Abstract

Brain Computer Interfaces (BCI) have been emerged as an alternative to support automatic systems able to interpret brain functions, commonly, by analyzing electroencephalography (EEG) recordings. In this work, a time-series discrimination methodology, called Motor Imagery Discrimination by Relevance Analysis (MIDRA), is presented to support the development of BCI from EEG data. Particularly, a Motor Imagery (MI) paradigm is studied, i.e., imagination of left-right hand movements. In this sense, a feature relevance analysis strategy is presented to select representing characteristics using a variability criterion. Besides, short-time parameters are estimated from EEG data by considering both time and time-frequency representations to deal with non-stationary dynamics. MIDRA is assessed on two different BCI databases, a well-known MI data and an Emotiv-based dataset. Attained results showed that MIDRA enhances the BCI system performance in comparison with benchmark methods by suitable ranking the input feature set. Moreover, applying MIDRA in a BCI based on the Emotiv device is a straightforward alternative for dealing with MI paradigms.