Generalization of ErrP-Calibration for Different Error-Rates in P300-Based BCIs

Automatic recognition of error-related potentials (ErrPs) requires a long calibration time in order to have enough error-samples to train the classifier. In this paper we analyze whether it is possible to reduce the ErrP-calibration time in a P300-based brain-computer interface (BCI), by calibrating the BCI with a high rate of errors (wrong detections of user intent). We analyze if a high error-rate condition still produces a discriminable ErrP and if its classification model generalizes well in sessions of different error-rates. Results show that the classification model built from a high error-rate calibration can be used successfully on sessions with lower error-rates.