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Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces.
Nikki Leeuwis1, Sue Yoon1, Maryam Alimardani1
1Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands.
Frontiers in Human Neuroscience
|November 1, 2021
Summary
High-aptitude users show increased right-hemisphere functional connectivity during motor imagery (MI) tasks. This finding may help improve brain-computer interface (BCI) systems by identifying key neural features for better MI-BCI classification.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Brain-Computer Interfaces
Background:
- Motor Imagery Brain-Computer Interface (MI-BCI) systems face challenges with user inefficiency due to difficulties in accurately modulating brain activity.
- Previous research focused on sensorimotor mu suppression, which may not fully capture the complex neural dynamics of motor imagery.
- Functional connectivity, representing brain region interactions, is a promising avenue for enhancing MI-BCI performance.
Purpose of the Study:
- To investigate the role of functional connectivity in differentiating between high and low aptitude MI-BCI users.
- To compare functional connectivity patterns at global, large, and local network scales during resting-state and motor imagery tasks.
- To identify neural markers that can explain and potentially overcome MI-BCI inefficiency.
Main Methods:
- Fifty-four novice MI-BCI users were divided into high and low performing groups based on task accuracy.
- Functional connectivity was analyzed across three network scales (Global, Large, Local) during resting-state and motor imagery.
- Comparisons were made during task execution and the transition between resting and imagery states, focusing on the alpha frequency band.
Main Results:
- High-aptitude MI-BCI users exhibited increased functional connectivity in the right hemisphere compared to low-aptitude users during motor imagery.
- Differences in functional connectivity were observed in the alpha frequency band, suggesting its importance in MI-BCI performance.
- These findings highlight specific neural connectivity patterns associated with successful motor imagery modulation.
Conclusions:
- Functional connectivity, particularly in the right hemisphere's alpha band, is a significant factor differentiating MI-BCI performance.
- Connectivity patterns offer a valuable feature for improving the classification accuracy of MI-BCI systems.
- This research provides insights into addressing the inefficiency problem in motor imagery-based brain-computer interfaces.
