Applying Independent Vector Analysis on EEG-based motor imagery classification

Joint Blind Source Separation (JBSS) is an essential and versatile research topic that has attracted attention of researchers in the last decade. Independent Vector Analysis (IVA) is an exciting approach in the context of the JBSS method since it is an extension of Independent Component Analysis (ICA) towards the exploitation of the statistical dependency between different datasets through the use of Mutual Information. In this work, we propose an original approach of IVA as a feature extraction step for Brain-Computer Interfaces, focused on the Motor Imagery (MI) paradigm. For this, we use the BCI Competition IV - Dataset 1. Since the participants of the experiment are performing the same MI tasks, we assume that the channels related to MI present correlated signals across subjects that might be explored by IVA techniques. The results show that the algorithm could classify the MI movements using a consolidated and low-cost classifier, Support Vector Machine, achieving an accuracy of 85%.