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.
|Original language||English (US)|
|Number of pages||5|
|Journal||IEEE Transactions on Neural Systems and Rehabilitation Engineering|
|State||Published - Jun 2006|
Bibliographical noteFunding Information:
Manuscript received July 19, 2005; revised March 24, 2006; accepted March 25, 2006. This work was supported in part by the National Science Foundation under Grant NSF BES-0411898, in part by the National Institutes of Health under Grant NIH R01 EB00178, and in part by a grant from the Dean of Graduate School of the University of Minnesota.
- Brain-computer interface (BCI)
- Motor imagery
- Time-frequency analysis
- Time-frequency-spatial analysis