Enhancement of classification accuracy of a time-frequency approach for an EEG-based brain-computer interface.
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%.
Yamawaki
N
N
Department of Biomedical Engineering, University of Minnesota, 7-105 NHH, 312 Church Street S.E., Minneapolis, MN 55455, USA.
Wilke
C
C
Hue
L
L
Liu
Z
Z
He
B
B
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