Small Data Least-Squares Transformation (sd-LST) for Fast Calibration of SSVEP-based BCIs

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; p. 1
• Main Authors: Bian, Rui, Wu, Huanyu, Liu, Bin, Wu, Dongrui
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain; Brain-computer interface; Brain-Computer Interfaces; Calibration; Computer applications; Electroencephalography; Evoked Potentials, Visual; Human-computer interface; Humans; Implants; Information processing; Least squares; least-squares transformation; Photic Stimulation; Signal to noise ratio; steady-state visual evoked potential; transfer learning; Visual evoked potentials
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2022.3225878

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.