Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Nikki Leeuwis*, Sue Yoon, Maryam Alimardani

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the sensorimotor area in order to classify imagery, but this does not reflect the true dynamics that underlie motor imagery. Functional connectivity reflects the interaction between brain regions during the MI task and resting-state network and is a promising tool in improving MI-BCI classification. In this study, 54 novice MI-BCI users were split into two groups based on their accuracy and their functional connectivity was compared in three network scales (Global, Large and Local scale) during the resting-state, left vs. right-hand motor imagery task, and the transition between the two phases. Our comparison of High and Low BCI performers showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings contribute to the existing literature that indeed connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.

Original languageEnglish
Article number732946
Number of pages13
JournalFrontiers in Human Neuroscience
Publication statusPublished - 15 Oct 2021


  • motor imagery (MI)
  • brain computer interface (BCI)
  • BCI inefficiency
  • electroencephalography (EEG)
  • functional connectivity (FC)
  • phase synchronization
  • EEG
  • BCI
  • MEG
  • LAG


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