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A visual stimulator plays an important role in a steady-state visual evoked potential (SSVEP)-based braincomputer interface (BCI). In conventional BCI studies, SSVEPs have been elicited by either a single stimulus whose flickering frequency varies across trials or multiple stimuli flickering at different frequencies simultaneously. It has been implicitly assumed that the SSVEPs generated by the single- and multiple-target stimulation methods are comparable. However, no study has directly compared their efficacy in eliciting SSVEPs. This study, therefore, performed a quantitative comparison of signal-to-noise ratio (SNR) and classification accuracy using 4-class SSVEPs generated by these two methods. The classification accuracy was estimated by three commonly-used target identification algorithms including calibration-free canonical correlation analysis (CCA)-based method and template-based methods with CCA- and task-related component analysis (TRCA)-based spatial filters. The results showed that the single-target stimulation method led to significantly higher SNR and classification accuracy than its multi-target counterpart.