Steady-state visual evoked potential (SSVEP)-based brain computer interfaces (BCIs) have gained considerable research interest because of their higher signal-to-noise ratio and greater information transfer rate than those of other BCI techniques. The signal processing algorithm is of key importance to the performance of BCI systems, and therefore plays a significant role in practical applications. However, there is no comprehensive review of the signal processing algorithms used for SSVEP-based BCIs. This paper reviews relevant papers and analyzes recent developments in use of these algorithms. The aim is to find their limitations to provide a guideline for researchers in this field of SSVEP-based BCIs. Techniques employed for signal preprocessing, feature extraction, and feature classification are discussed. Algorithms that can be applied to nonlinear and non-stationary signal processing are increasingly employed rather than traditional Fourier-based transforms because they are more suitable for the characteristics of SSVEPs. Spatial filtering techniques for channel selection are better at eliminating nuisance signals than those that use a single channel signal for processing. In addition, other factors that affect the performance of the system are discussed.