Steady state visual evoked potential (SSVEP)-based brain computer interfaces have shown its robustness in achieving high information transfer rate. State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of  SSVEP-based BCI through individualized calibration process. However, the calibration may be time-consuming in order to collect sufficient training templates, deteriorating the practicality in  real-world context. This study aims to develop a cross-subject transferring approach to reduce the need of training data from a new user. The proposed least-squares transformation (LST) method was able to significantly reduce the training templates required for TRCA-based SSVEP detection on a 40-class SSVEP dataset from 8 subjects. This study shed light on plug-and-play high speed SSVEP-based BCIfor further practical applications .
Steady-state visual evoked potential (SSVEP)-based brain computer-interfaces (BCIs) have shown its robustness in achieving high information transfer rate. State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of the SSVEP-based BCIs through an individualized calibration process. However, collecting sufficient calibration (e.g. training templates) data could be laborious and time-consuming, hindering the practicality in a real-world context. This study aims to develop a cross-subject transferring approach to reduce the need for training data from a new user. Study results showed that a new least-squares transformation (LST) method was able to significantly reduce the training templates required for a 40-class TRCA-based SSVEP BCI. The LST method may lead to numerous practical applications of plug-and-play high-speed SSVEP-based BCIs .
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the calibration may be time-consuming in order to collect sufficient training templates, deteriorating
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Study results showed that a new
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TRCA-based SSVEP detection on
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high speed SSVEP-based BCIfor further practical applications
high-speed SSVEP-based BCIs