Impact of time-frequency representation to the generalization ability of synthesized time-frequency spatial patterns algorithm in brain computer interface

This paper focuses on the problem of how time-frequency representation influences the generalization ability of the `synthesized time-frequency spatial pattern (TFSP)´ algorithm in brain computer interface (BCI) for classification. TFSP methods use time-frequency analysis to extract features in both time and frequency domains. Different time-frequency analysis methods have been used before. However, it is still unknown how these different approaches influence the generalization ability. We compared the performance of three different TFSP methods in classifying 3 stroke survivors´ intention in hand opening and closing. Each of these TFSP methods uses different time-frequency analysis approaches with different time-frequency resolutions. Our results show that a high resolution in time-frequency resolution doesn´t guarantee better generalization ability. It seems that although large redundancy in feature reduces the generalization ability of TFSP method, certain redundancy is necessary for achieving high generalization ability.