Pattern rejection strategies for the design of self-paced EEG-based Brain-Computer Interfaces

This paper deals with pattern rejection strategies for self-paced brain-computer interfaces (BCI). First, it introduces two pattern rejection strategies not used yet for self-paced BCI design: 1) the rejection class (RC) strategy and 2) thresholds on reliability functions (TRF) based on the automatic multiple-threshold learning algorithm. Second, it compares several rejection strategies using several classifiers, on motor imagery data, in order to identify their most desirable properties. Results showed that nonlinear classifiers led to the most efficient self-paced BCI. Concerning the reject option, RC outperformed a specialized reject classifier which outperformed TRF. Overall, the best results were obtained using the RC reject option and non-linear classifiers such as a Gaussian support vector machine, a fuzzy inference system or a radial basis function network.