Predictive-Spectral-Spatial Preprocessing for a Multiclass Brain-Computer Interface

Recent work has shown that combining prediction based preprocessing based on neural-time-series-prediction-preprocessing (NTSPP) along with spectral filtering (SF) and common-spatial patterns (CSP) can significantly improve the performance of a motor imagery based brain-computer interface (BCI) involving two classes. This paper illustrates how these performance improvements can be extended to a 4 class motor imagery BCI with between 2 and 22 channels. The results show that this combination of preprocessing techniques can significantly outperform any of methods operating independently and that NTSPP can reduce the number of electrodes required based on a comparison of results from 2, 3 and multichannel data.