  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 EEG 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.
