Cross-Stimulus Transfer Learning Framework Using Common Period Repetition Components for Fast Calibration of SSVEP-Based BCIs

The decoding approach of steady-state visual evoked potentials (SSVEPs) based on supervised learning has achieved remarkable results. However, these approaches require extensive calibration efforts to train the mode parameters for each stimulus. To facilitate the calibration process, we proposed a cross-stimulus transfer learning framework using the common periodic repetition components (CSTLF-CPRC) in fast calibration scenario. First, a source stimulus mode was constructed, which can use periodic repetition components to obtain a source synthetic SSVEP template and source ensemble spatial filter. Second, leveraging the common information between period repeated component templates across multistimulus periods, the common source aliasing matrix was further estimated. Finally, leveraging the commonality between target and source stimuli, a cross-stimulus transfer learning mode was constructed for SSVEP cross-stimulus recognition. Offline tests on public datasets show that the CSTLF-CPRC outperforms the state-of-the-art (SOTA) methods, such as filter band CCA, transfer learning CCA, and common impulse response cross-stimulus transfer learning, in a fast calibration scenario. Our method only needs 16 s to calibrate 40 targets on two public datasets and achieves an average information transfer rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$227.86~\pm ~106.47$ </tex-math></inline-formula> bit/min and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$162.41~\pm ~124.29$ </tex-math></inline-formula> bit/min, respectively. The study has the requirement of a few calibration data to achieve high-performance recognition and to promote effective development of the practical system.