An enhanced time-frequency-spatial approach for motor imagery classification.
Human motor imagery (MI) tasks evoke electroencephalogram (EEG) signal changes. The features of these changes appear as subject-specific temporal traces of EEG rhythmic components at specific channels located over the scalp. Accurate classification of MI tasks based upon EEG may lead to a noninvasive brain-computer interface (BCI) to decode and convey intention of human subjects. We have previously proposed two novel methods on time-frequency feature extraction, expression and classification for high-density EEG recordings (Wang and He 2004; Wang, Deng, and He, 2004). In the present study, we refined the above time-frequency-spatial approach and applied it to a one-dimensional "cursor control" BCI experiment with online feedback. Through offline analysis of the collected data, we evaluated the capability of the present refined method in comparison with the original time-frequency-spatial methods. The enhanced performance in terms of classification accuracy was found for the proposed approach, with a mean accuracy rate of 91.1% for two subjects studied.
Yamawaki
Nobuyuki
N
Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Wilke
Christopher
C
Liu
Zhongming
Z
He
Bin
B
D000328:Adult; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D003143:Communication Devices for People with Disabilities; D016287:Computer Peripherals; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D008328:Man-Machine Systems; D018511:Systems Integration; D014584:User-Computer Interface; D014836:Volition / Q000502:physiology