Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs

Objective. Electroencephalography (EEG) is a non-linear and non-stationary process, as a result, its features are unstable and often vary in quality across trials, which poses significant challenges to brain-computer interfaces (BCIs). One remedy to this problem is to adaptively collect sufficient EEG evidence using dynamic stopping (DS) strategies. The high-speed steady-state visual evoked potential (SSVEP)-based BCI has experienced tremendous progress in recent years. This study aims to further improve the high-speed SSVEP-based BCI by incorporating the DS strategy. Approach. This study involves the development of two different DS strategies for a high-speed SSVEP-based BCI, which were based on the Bayes estimation and the discriminant analysis, respectively. To evaluate their performance, they were compared with the conventional fixed stopping (FS) strategy using simulated online tests on both our collected data and a public dataset. Two most effective SSVEP recognition methods were used for comparison, including the extended canonical correlation analysis (CCA) and the ensemble task-related component analysis (TRCA). Main results. The DS strategies achieved significantly higher information transfer rates (ITRs) than the FS strategy for both datasets, improving 9.78% for the Bayes-based DS and 6.7% for the discriminant-based DS. Specifically, the discriminant- based DS strategy using ensemble TRCA performed the best for our collected data, reaching an average ITR of 353.3 +/- 67.1 bits min(-1) with a peak of 460 bits min(-1). The Bayes-based DS strategy using ensemble TRCA had the highest ITR for the public dataset, reaching an average of 230.2 +/- 65.8 bits min(-1) with a peak of 304.1 bits min(-1). Significance. This study demonstrates that the proposed dynamic stopping strategies can further improve the performance of a SSVEP-based BCI, and hold promise for practical applications.