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

Chun Ren Phang, Li Wei Ko

Research output: Contribution to journalArticlepeer-review

Abstract

Brain-computer interface (BCI) brings hope to patients suffering from neuromuscular diseases, by allowing the control of external devices using neural signals from the central nervous system. However, a portion of individuals was unable to operate BCI with high efficacy. This research aimed to study the brain-wide functional connectivity differences that contributed to BCI performance, and investigate the relationship between task-related connectivity strength and BCI performance. Functional connectivity was estimated using pairwise Pearson’s correlation from the EEG of 48 subjects performing left or right hand motor imagery (MI) tasks. The classification accuracy of linear support vector machine (SVM) to distinguish both tasks were used to represent MI-BCI performance. The significant differences in connectivity strengths were examined using Welch’s T-test. The association between accuracy and connection strength was studied using correlation model. Three intralobular and fourteen interlobular connections from the parietal lobe showed a correlation of 0.31 and -0.34 respectively. Results indicate that alpha wave connectivity from 8 Hz to 13 Hz was more related to classification performance compared to high-frequency waves. Subject-independent trial-based analysis shows that MI trials executed with stronger intralobular and interlobular parietal connections performed significantly better than trials with weaker connections. Further investigation from an independent MI dataset reveals several similar connections that were correlated with MI-BCI performance. The functional connectivity of the parietal lobe could potentially allow prediction of MI-BCI performance and enable implementation of neurofeedback training for users to improve the usability of MI-BCI.

Keywords

  • BCI performance
  • brain connectivity network
  • Brain modeling
  • brain-computer interface
  • Correlation
  • EEG
  • Electroencephalography
  • Feature extraction
  • motor imagery
  • Performance evaluation
  • Support vector machines
  • Task analysis

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