title: Enhancement of classification accuracy of a time-frequency approach for an EEG-based brain-computer interface.
abstract: OBJECTIVE
  The aim of this paper is to develop a new algorithm to enhance the performance of EEG-based brain-computer interface (BCI).
  
  
  METHODS
  We improved our time-frequency approach of classification of motor imagery (MI) tasks for BCI applications. The approach consists of Laplacian filtering, band-pass filtering and classification by correlation of time-frequency-spatial patterns.
  
  
  CONCLUSIONS
  Through off-line analysis of data collected during a "cursor control" experiment, we evaluated the capability of our new method to reveal major features of the EEG control for enhancement of MI classification accuracy. The pilot results in a human subject are promising, with an accuracy rate of 96.1%.
authors.affiliation: Department of Biomedical Engineering, University of Minnesota, 7-105 NHH, 312 Church Street S.E., Minneapolis, MN 55455, USA.
authors.forename: N
authors.initials: N
authors.lastname: Yamawaki
authors.forename: C
authors.initials: C
authors.lastname: Wilke
authors.forename: L
authors.initials: L
authors.lastname: Hue
authors.forename: Z
authors.initials: Z
authors.lastname: Liu
authors.forename: B
authors.initials: B
authors.lastname: He
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004567:Electrodes, Implanted; D004569:Electroencephalography / Q000295:instrumentation*; D006225:Hand / Q000502:physiology*; D006801:Humans; D009068:Movement / Q000502:physiology*; D010865:Pilot Projects; D012815:Signal Processing, Computer-Assisted; D013995:Time; D014584:User-Computer Interface