Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) require minimal user training and can offer higher information throughput compared to other BCI modalities. We focused on SSVEPs elicited by high-frequency stimuli (>30 Hz) because they cause minimal fatigue/annoyance and reduce the risk of inducing photoepileptic seizures. This paper presents an approach that analyzes electroencephalographic activity to automatically obtain the optimum spatial filter for detecting the SSVEP at a given stimulation frequency from a short signal where the stimulation is presented at intermittent periods interspersed with breaks. A vector space generated by sinusoidal signals at the stimulation frequency and harmonics is defined. The spatial filter coefficients result from maximizing the ratio between the energy of the spatially filtered signal and that of its orthogonal component with regard to the vector space. The spatial filters are customized for each BCI user through a short calibration procedure taking into account individual specificity. Our experiments on six subjects applying the spatial filters resulted in an average transfer rate ranging from 20.9 to 22.7 bits/min.