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 a calibration process. However, because of large human variability both across individuals and within individuals over time, we need to collect calibration (training) data from each individual before each session, which could be laborious and time-consuming, deteriorating its 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 test 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 SSVEP BCI. The LST method may lead to numerous real-world applications of a plug-and-play high-speed SSVEP-based BCI .
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication . 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 a calibration process. However, due to notable human variability across individuals and within individuals over time, calibration (training) data collection is non-negligible and often laborious and time-consuming, deteriorating the practicality of SSVEP BCIs in a real-world context. This study aims to develop a cross-subject transferring approach to reduce the need for collecting training data from a test user with a newly proposed least-squares transformation (LST) method . Study results show the capability of the LST in reducing the number of training templates required for a 40-class SSVEP BCI. The LST method may lead to numerous real-world applications using near-zero-training/ plug-and-play high-speed SSVEP BCIs .
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brain computer-interfaces
brain-computer interfaces
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achieving high information transfer rate
facilitating high-efficiency communication
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because of large human variability both
due to notable human variability
D
we need to collect
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from each individual before each session, which could be
collection is non-negligible and often
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its practicality
the practicality of SSVEP BCIs
A
collecting
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. Study results showed that a new
with a newly proposed
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was able to significantly reduce the
. Study results show the capability of the LST in reducing the number of
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of a
using near-zero-training/
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SSVEP-based BCI
SSVEP BCIs