In the past decade, the performance of brain-computer interfaces based on steady-state visual evoked potentials (SSVEPs) has been significantly improved due to advances in signal analysis algorithms. For example, efficient target-identification methods based on template matching, in which individual templates are obtained by averaging the training data across trials, have been proposed to improve the performance of SSVEP detection. In template-based methods, spatial filtering plays an important role in improving the performance by enhancing the signal-to-noise ratio of SSVEPs. In conventional studies, several spatial-filtering approaches have been introduced for electroencephalogram analysis. However, the optimal spatial-filtering approach for individual template-based SSVEP detection still remains unknown. This chapter reviews the spatial-filtering approaches for improving the template-based SSVEP detection and evaluates their performance through a direct comparison using a benchmark dataset of SSVEPs.