A study on the impact of spectral variability in brain-computer interface

The performance of a Brain-Computer Interface (BCI) depends on reliable feature extraction and accurate classification. Motor imagery has been successfully used in BCI for communication and control. During motor imagery, for EEG based BCI, it was known that the discriminative frequency bands are subject-specific. Moreover, such discriminative frequency bands for each subject might vary from time to time. In this paper, we investigate the variability of discriminative spectral ranges and its impact on classification accuracy. It is found that for each subject, his discriminative frequency bands changes significantly from session to session, but keeps almost stable within a session. We then propose a method to adaptively update the discriminative frequency bands using Time-Frequency fisher ratio. From the experimental analysis, it is found that we can reduce the average error rate by 11.50% compared to the case where fixed discriminative frequency bands obtained from calibration session are used.