Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs.

ABSTRACT: Recently, brain-computer interface (BCI) systems developed based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high information transfer rate (ITR) and increasing number of targets. However, SSVEP-based methods can be improved in terms of their accuracy and target detection time. We propose a new method based on canonical correlation analysis (CCA) to integrate subject-specific models and subject-independent information and enhance BCI performance. We propose to use training data of other subjects to optimize hyperparameters for CCA-based model of a specific subject. An ensemble version of the proposed method is also developed for a fair comparison with ensemble task-related component analysis (TRCA). The proposed method is compared with TRCA an

SUBMITTER: Mehdizavareh MH 

PROVIDER: S-EPMC6959579 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature