title: Prediction of SSVEP-based BCI performance by the resting-state EEG network.
abstract: OBJECTIVE
  The prediction of brain-computer interface (BCI) performance is a significant topic in the BCI field. Some researches have demonstrated that resting-state data are promising candidates to achieve the goal. However, so far the relationships between the resting-state networks and the steady-state visual evoked potential (SSVEP)-based BCI have not been investigated. In this paper, we investigate the possible relationships between the SSVEP responses, the classification accuracy of five stimulus frequencies and the closed-eye resting-state network topology.
  
  
  METHODS
  The resting-state functional connectivity networks of the corresponding five stimulus frequencies were created by coherence, and then three network topology measures--the mean functional connectivity, the clustering coefficient and the characteristic path length of each network--were calculated. In addition, canonical correlation analysis was used to perform frequency recognition with the SSVEP data.
  
  
  RESULTS
  Interestingly, we found that SSVEPs of each frequency were negatively correlated with the mean functional connectivity and clustering coefficient, but positively correlated with characteristic path length. Each of the averaged network topology measures across the frequencies showed the same relationship with the SSVEPs averaged across frequencies between the subjects. Furthermore, our results also demonstrated that the classification accuracy can be predicted by three averaged network measures and their combination can further improve the prediction performance.
  
  
  CONCLUSIONS
  These findings indicate that the SSVEP responses and performance are predictable using the information at the resting-state, which may be instructive in both SSVEP-aided cognition studies and SSVEP-based BCI applications.
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, 610054, People's Republic of China.
authors.forename: Yangsong
authors.initials: Y
authors.lastname: Zhang
authors.forename: Peng
authors.initials: P
authors.lastname: Xu
authors.forename: Daqing
authors.initials: D
authors.lastname: Guo
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
mesh_terms: D000328:Adult; D062207:Brain-Computer Interfaces / Q000639:trends*; D004569:Electroencephalography / Q000379:methods / Q000639:trends*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods*; D012146:Rest / Q000502:physiology*; D055815:Young Adult