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• W4310595516 abstract "Steady-state visual evoked potential (SSVEP) is one of the most popular brain-computer interface (BCI) paradigms, with high information transmission rate and signal-to-noise ratio. Many calibration-free and calibration-based approaches have been proposed to improve the performance of SSVEP-based BCIs. This paper considers a quick calibration scenario, where there are plenty of data from multiple source subjects, but only a small number of calibration trials from a subset of stimulus frequencies for the new subject. We propose small data least-squares transformation (sd-LST) to solve this problem. Experiments on three publicly available SSVEP datasets demonstrated that sd-LST outperformed several classical or state-of-the-art approaches, with only about 10 calibration trials for 40-target SSVEP-based BCI spellers." @default.
• W4310595516 created "2022-12-12" @default.
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• W4310595516 date "2023-01-01" @default.
• W4310595516 modified "2023-10-18" @default.
• W4310595516 title "Small Data Least-Squares Transformation (sd-LST) for Fast Calibration of SSVEP-Based BCIs" @default.
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• W4310595516 doi "https://doi.org/10.1109/tnsre.2022.3225878" @default.
• W4310595516 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36455079" @default.
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