title: Efficient resting-state EEG network facilitates motor imagery performance.
abstract: OBJECTIVE
  Motor imagery-based brain-computer interface (MI-BCI) systems hold promise in motor function rehabilitation and assistance for motor function impaired people. But the ability to operate an MI-BCI varies across subjects, which becomes a substantial problem for practical BCI applications beyond the laboratory.
  
  
  METHODS
  Several previous studies have demonstrated that individual MI-BCI performance is related to the resting state of brain. In this study, we further investigate offline MI-BCI performance variations through the perspective of resting-state electroencephalography (EEG) network.
  
  
  RESULTS
  Spatial topologies and statistical measures of the network have close relationships with MI classification accuracy. Specifically, mean functional connectivity, node degrees, edge strengths, clustering coefficient, local efficiency and global efficiency are positively correlated with MI classification accuracy, whereas the characteristic path length is negatively correlated with MI classification accuracy. The above results indicate that an efficient background EEG network may facilitate MI-BCI performance. Finally, a multiple linear regression model was adopted to predict subjects' MI classification accuracy based on the efficiency measures of the resting-state EEG network, resulting in a reliable prediction.
  
  
  CONCLUSIONS
  This study reveals the network mechanisms of the MI-BCI and may help to find new strategies for improving MI-BCI performance.
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
authors.forename: Rui
authors.initials: R
authors.lastname: Zhang
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
authors.forename: Pedro A
authors.initials: PA
authors.lastname: Valdés-Sosa
authors.forename: Fali
authors.initials: F
authors.lastname: Li
authors.forename: Peiyang
authors.initials: P
authors.lastname: Li
authors.forename: Tao
authors.initials: T
authors.lastname: Zhang
authors.forename: Teng
authors.initials: T
authors.lastname: Ma
authors.forename: Yongjie
authors.initials: Y
authors.lastname: Li
authors.forename: Peng
authors.initials: P
authors.lastname: Xu
mesh_terms: D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D012146:Rest / Q000502:physiology*; D055815:Young Adult