Extracting common spatial patterns based on wavelet lifting for brain computer interface design

Abstract

Brain computer interfacing (BCI) offers the possibility to interact with machines uniquely relying on the user's thoughts. Although wavelet analysis has been used in the BCI field there is evidence that standard wavelet families, such as Daubechies, may not be the optimal approach. In this study, we developed a novel wavelet lifting scheme, specifically for BCI design. The lifting transform in this new approach is based on common spatial patterns (CSP), which allows to exploit the signal characteristics in temporal, spectral and spatial domains simultaneously. Experimental results show that in BCI applications the new wavelet outperforms several first generation wavelet families in terms of classification accuracy and resource consumption.