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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) associated to the speed of a task. First, we theoretically quantified the improvement in accuracy of a BCI system when using error potentials for correcting the output decision, in the general case of multiclass BCI. 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. Second we studied in simulation the performance of the closed-loop system in order to evaluate its ability to adapt to the changes in the mental states of the user. By setting the parameters of the simulator to experimentally determined values, we showed that updating the learning set with the examples estimated as correct based on the decoding of error potentials leads to convergence to the optimal solution.