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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. 

Abstract Objective . This study aims to establish a generalized transfer-learning framework for boosting the performance of steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) by leveraging cross-domain data transferring. Approach . We enhanced the state-of-the-art template-based SSVEP decoding through incorporating a least-squares transformation (LST)-based transfer learning to leverage calibration data across multiple domains (sessions, subjects, and electroencephalogram montages). Main results . Study results verified the efficacy of LST in obviating the variability of SSVEPs when transferring existing data across domains. Furthermore, the LST-based method achieved significantly higher SSVEP-decoding accuracy than the standard task-related component analysis (TRCA)-based method and the non-LST naive transfer-learning method. Significance . This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances. The proposed framework significantly improved the SSVEP decoding accuracy over the standard TRCA approach when calibration data are limited. Its performance in calibration reduction could facilitate plug-and-play SSVEP-based BCIs and further practical applications.