Applying Independent Vector Analysis on EEG-Based Motor Imagery Classification
Caroline P. A. Moraes, Bruno Aristimunha, Lucas Heck Dos Santos, Walter Hugo Lopez Pinaya, Raphael Yokoingawa de Camargo, Denis G. Fantinato, Aline Neves
Abstract
Joint Blind Source Separation (JBSS) is an essential and versatile research topic that has attracted the 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%.
BibTeX
@inproceedings{icassp2023_applyingindepend,
title = {Applying Independent Vector Analysis on EEG-Based Motor Imagery Classification},
author = {Caroline P. A. Moraes and Bruno Aristimunha and Lucas Heck Dos Santos and Walter Hugo Lopez Pinaya and Raphael Yokoingawa de Camargo and Denis G. Fantinato and Aline Neves},
booktitle = {ICASSP 2023},
year = {2023}
}