Intralobular and Interlobular Parietal Functional Network Correlated to MI-BCI Performance

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Brain-computer interfaces (BCIs) offer solutions for neuromuscular diseases.
• Some individuals struggle with high BCI efficacy.
• Understanding brain connectivity is crucial for BCI improvement.

Purpose of the Study:

• Investigate brain-wide functional connectivity differences related to BCI performance.
• Determine the relationship between task-related connectivity strength and BCI performance.

Main Methods:

• Functional connectivity estimated using Pearson's correlation on EEG data.
• 48 subjects performed left/right hand motor imagery (MI) tasks.
• Linear Support Vector Machine (SVM) used for classification accuracy to represent BCI performance.

Main Results:

• Alpha wave connectivity (8-13 Hz) showed stronger correlation with classification performance than high-frequency waves.
• Stronger intralobular and interlobular parietal connections correlated with better MI-BCI performance.
• Results replicated in an independent MI dataset.

Conclusions:

• Parietal lobe functional connectivity can predict MI-BCI performance.
• Neurofeedback training based on connectivity could enhance MI-BCI usability.
• Identified specific parietal connections are key indicators for BCI success.