Accuracy of a BCI based on movement-related and error potentials

New paradigms for brain computer interfacing (BCI), such as based on imagination of task characteristics, require long training periods, have limited accuracy, and lack adaptation to the changes in the users' conditions. Error potentials generated in response to an error made by the translation algorithm can be used to improve the performance of a BCI, as a feedback extracted from the user and fed into the BCI system. The present study addresses the inclusion of error potentials in a BCI system based on the decoding of movement-related cortical potentials (MRCPs). We theoretically quantify the improvement in accuracy of a BCI system when using error potentials for correcting the output decision, in the general case of multiclass classification. The derived theoretical expressions can be used during the design phase of any BCI system. They were applied to experimentally estimated accuracies in decoding MRCPs and error potentials. The average misclassification rate (n = 6 subjects) of MRCPs associated to the imagination of elbow flexions at two speeds was 26%, with a bit transfer rate of 0.17. The inclusion of error potentials, experimentally recorded and classified with misclassification rate of 20%, led to a theoretical error rate of 14% with a bit transfer rate of 0.30.