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Adapted from the concept of channel capacity, the information transfer rate (ITR) has been widely used to evaluate the performance of a brain–computer interface (BCI). However, its traditional formula considers the model of a discrete memoryless channel in which the transition matrix presents very particular symmetries. As an alternative to compute the ITR, this work indicates a more general closed-form expression—also based on that channel model, but with less restrictive assumptions—and, with the aid of a selection heuristic based on a wrapper algorithm, extends such formula to detect classes that deteriorate the operation of a BCI system. The benchmark is a steady-state visually evoked potential (SSVEP)-based BCI dataset with 40 frequencies/classes, in which two scenarios are tested:(1) our proposed formula is used and the classes are gradually evaluated in the order of the class …