Phase/amplitude synchronization of brain signals during motor imagery BCI tasks

TL;DR

This study investigates how phase and amplitude synchronization of brain signals during motor imagery can enhance brain-computer interface performance by analyzing functional connectivity networks and their neurophysiological implications.

Contribution

It introduces a combined analysis of spectral- and imaginary-coherence to better understand brain network changes during motor imagery for BCI applications.

Findings

Spectral-coherence increases in contralateral motor areas.

Imaginary-coherence decreases in the same regions.

Including connectivity features improves classification accuracy.

Abstract

The extraction of brain functioning features is a crucial step in the definition of brain-computer interfaces (BCIs). In the last decade, functional connectivity (FC) estimators have been increasingly explored based on their ability to capture synchronization between multivariate brain signals. However, the underlying neurophysiological mechanisms and the extent to which they can improve performance in BCI-related tasks, is still poorly understood. To address this gap in knowledge, we considered a group of 20 healthy subjects during an EEG-based hand motor imagery (MI) task. We studied two well-established FC estimators, i.e. spectral- and imaginary-coherence, and investigated how they were modulated by the MI task. We characterized the resulting FC networks by extracting the strength of connectivity of each EEG sensor and compared the discriminant power with respect to standard power spectrum features. At the group level, results showed that while spectral-coherence based network features were increasing the controlateral motor area, those based on imaginary-coherence were decreasing. We demonstrated that this opposite, but complementary, behavior was respectively determined by the increase in amplitude and phase synchronization between the brain signals. At the individual level, we proved that including these network connectivity features in the classification of MI mental states led to an overall improvement in accuracy. Taken together, our results provide fresh insights into the oscillatory mechanisms subserving brain network changes during MI and offer new perspectives to improve BCI performance.