Brain-computer interface performance is estimated using a model based on the detection of steady-state visual evoked potentials (SSVEPs). It is established that the most significant parameters determining if the SSVEP-based brain-computer interfaces can be used in principle are the ratio of the number of samples in the analyzed signal to the sampling rate and the frequency range in which SSVEPs are detected. If it is necessary to identify the factors that limit the performance of the interface, then the ratio of the frequency range to the number of possible frequencies of the SSVEPs and the ratio of the number of samples in the analyzed signal to the sampling rate are significant predictors. The results presented in this paper make it possible to simulate parameters of brain-computer interfaces on the basis of requirements for a particular device and its capabilities. This makes it possible to design simpler hardware and software for specific tasks and reduce debugging time. © 2014 Pleiades Publishing, Ltd.