A method for predicting the success of a BCI training session based on the classification of the CSP filters

We present an offline analysis of a large set of BCI experiments, focusing on CSP filters and patterns. First, we show that it is possible to infer from the CSP filters whether the cross-validation error of LDA-classified EEG data preprocessed by this CSP will be high or low and predict thus the future performance of the feedback sessions following the calibration. Second, from the CSP patterns, we calculate the corresponding source localization of the activations on the cortex. We explore the possibility of finding a way to use our method in order to improve the probability of a successful calibration and reduce the phenomenon of BCI illiteracy.