Time-series discrimination using feature relevance analysis in motor imagery classification

Abstract

The use of motor imagery discrimination using feature relevance analysis (MIDFR) is investigated for classification tasks based on electroencephalography (EEG) signals. The method addresses the problem of a direct and automatic finding of the time-varying features influencing the most on distinguishing motor imagery tasks. The method introduces a stochastic relevance stage that is primarily used for properly handling the set of short-time features, which are extracted as to make prominent the nonstationary behavior of the EEG data. Furthermore, since it is widely accepted that the motor imagery information is concentrated in the μ and β neural activity bands, we make use of the empirical mode decomposition together with the common-spatial-patterns mapping. We test two different motor imagery databases, using a soft-margin support vector machine classifier that is validated by a 10-fold cross-validation methodology. Since the proposed MIDFR algorithm better encodes neural activity dynamics, experimental results carried out, which are also contrasted with other state-of-the art approaches, show that the proposed approach allows improving detection of MI classification tasks. Besides, the computed relevance on the EEG channels is in accordance with other clinical findings reported in the literature.