Incremental Common Spatial Pattern algorithm for BCI

A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the on-line non-stationarity of the data blocks. An effective BCI system should be adaptive to and robust against the dynamic variations in brain signals. One solution to it is to adapt the model parameters of BCI system online. However, CSP is poor at adaptability since it is a batch type algorithm. To overcome this, in this paper, we propose the Incremental Common Spatial Pattern (ICSP) algorithm which performs the adaptive feature extraction on-line. This method allows us to perform the online adjustment of spatial filter. This procedure helps the BCI system robust to possible non-stationarity of the EEG data. We test our method to data from BCI motor imagery experiments, and the results demonstrate the good performance of adaptation of the proposed algorithm.