Leveraging Peripheral Visual Stimuli for Enhanced SSVEP-Based BCIs in Fast Calibration Scenario

The decoding method based on training sample is utilized to effectively improve the recognition ability for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, these methods are not adapted to fast calibration scenarios because their performance degrades as the number of calibration trials decreases. For fast calibration scenarios, we proposed a multivisual source aliasing matrix estimation task-related component analysis (MVSAME-TRCA) method, which can simultaneously exploit the common information of the source aliasing matrix and the spatial filter across a peripheral visual stimulus (PVS). Experiment results show that the MVSAME-TRCA method outperforms the state-of-the art (SOTA) method such as ensemble TRCA approach (eTRCA), ePRCA, and same-eTRCA in a fast calibration scenario in the two public datasets. Notably, our approach only needs 28 s to calibrate 40 targets in the two public datasets and achieves an averaged information transfer rates (ITRs) of $248.78~\pm ~86.47$ bits/min and $186.86~\pm ~100.32$ bits/min, respectively. This research greatly reduces the need for individualized calibration for SSVEP system and is conducive to the development of practical plug and play SSVEP-BCI.