Preliminary Assessment of hands motor imagery in theta- and beta-bands for Brain-Machine-Interfaces using functional connectivity analysis

TL;DR

This study investigates functional connectivity differences in theta and beta bands during motor imagery tasks using PDC analysis, aiming to enhance BCI feature extraction beyond traditional spectral power methods.

Contribution

It introduces the use of PDC for analyzing brain connectivity in motor imagery tasks, providing new insights into brain dynamics for BCI applications.

Findings

PDC analysis offers additional information over spectral power.

Beta band connectivity improves task differentiation.

PDC can enhance feature selection for BCIs.

Abstract

The use of time- and frequency-based features has proven effective in the process of classifying mental tasks in Brain Computer Interfaces (BCIs). Still, most of those methods provide little insight about the underlying brain activity and functions. Thus, a better understanding of the mechanisms and dynamics of brain activity, is necessary in order to obtain useful and informative features for BCIs. In the present study, the objective is to investigate the differences in functional connectivity of two motor imagery tasks, through a partial directed coherence (PDC) analysis, which is a frequency-domain metric that provides information about directionality in the interaction between signals recorded at different channels. Four healthy subjects participated in this study, two mental tasks were evaluated: Imagination of the movement of the right hand or left hand. We carry out the differentiation of these tasks through two different approaches: on one hand, the traditional one based on spectral power; on the other hand, an approach based on PDC. The results showed that EEG-based PDC analysis provides additional information and it can potentially improve the feature selection mainly in the beta frequency band.