steady-state visual evoked potential

Inter-and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-based BCIs

Author(s): Chi Man Wong, Ze Wang, Boyu Wang, Ka Fai Lao, Agostinho Rosa, Peng Xu, Tzyy-Ping Jung, C. L. Philip Chen, Feng Wan
Inter-and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-based BCIs 1000 920 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that can deliver high information transfer rate (ITR) usually require subject’s calibration data to learn the class-and subject-specific model parameters (e.g. the spatial filters and SSVEP templates). Normally, the amount of the calibration data for learning is proportional to the number of classes (or visual stimuli), which could be huge and consequently lead to a time-consuming calibration. read more