  Due to their high signal-to-noise ratio (SNR) and robustness to artifacts,
steady state visual evoked potentials (SSVEPs) are a popular technique for
studying neural processing in the human visual system. SSVEPs are
conventionally analyzed at individual electrodes or linear combinations of
electrodes which maximize some variant of the SNR. Here we exploit the
fundamental assumption of evoked responses -- reproducibility across trials --
to develop a technique that extracts a small number of high SNR, maximally
reliable SSVEP components. This novel spatial filtering method operates on an
array of Fourier coefficients and projects the data into a low-dimensional
space in which the trial-to-trial spectral covariance is maximized. When
applied to two sample data sets, the resulting technique recovers
physiologically plausible components (i.e., the recovered topographies match
the lead fields of the underlying sources) while drastically reducing the
dimensionality of the data (i.e., more than 90% of the trial-to-trial
reliability is captured in the first four components). Moreover, the proposed
technique achieves a higher SNR than that of the single-best electrode or the
Principal Components. We provide a freely-available MATLAB implementation of
the proposed technique, herein termed "Reliable Components Analysis".
