A Speedy Calibration Method Using Riemannian Geometry Measurement and Other-Subject Samples on A P300 Speller

P300 spellers are among the most popular brain–computer interface paradigms, and they are used for many clinical applications. However, building the classifier for identifying event-related potential (ERP) responses, i.e., calibrating the P300 speller, is still a time-consuming and user-dependent problem. This paper proposes a novel method to reduce calibration times significantly. In the proposed method, a small number of ERP epochs from the current user were used to build a reference epoch. Based on this reference, the Riemannian distance measurement was used to select similar ERP samples from an existing data pool, which contained other-subject ERP responses. Linear discriminant analysis (LDA), support vector machine, and stepwise LDA were trained as ERP classifiers on the selected database and then were used to identify the user-attended character. With only 12 s of EEG data to calibrate, an average character recognition accuracy for 55 subjects of up to 87.82% was obtained. The LDA that built on other-subject samples that were selected by Riemannian distance outperformed the other classifiers. Compared with other state-of-the-art studies, this method significantly reduces P300 speller calibration times, while maintaining the character recognition accuracy.