A Canonical Correlation Analysis-Based Transfer Learning Framework for Enhancing the Performance of SSVEP-Based BCIs.

A steady-state visual evoked potential (SSVEP)- based brain-computer interface (BCI) can either achieve high classification accuracy in the case of sufficient training data or suppress the training stage at the cost of low accuracy. Although some researches attempted to conquer the dilemma between performance and practicality, a highly effective approach has not yet been established. In this paper, we propose a canonical correlation analysis (CCA)-based transfer learning framework for improving the performance of an SSVEP BCI and reducing its calibration effort. Three spatial filters are optimized by a CCA algorithm with intra- and inter-subject EEG data (IISCCA), two template signals are estimated separately with the EEG data from the target subject and a set of source subjects and six coefficients are yielded by correlation analysis between a testing signal and each of the two templates after they are filtered by each of the three spatial filters. The feature signal used for classification is extracted by the sum of squared coefficients multiplied by their signs and the frequency of the testing signal is recognized by template matching. To reduce the individual discrepancy between subjects, an accuracy-based subject selection (ASS) algorithm is developed for screening those source subjects whose EEG data are more similar to those of the target subject. The proposed ASS-IISCCA integrates both subject-specific models and subject-independent information for the frequency recognition of SSVEP signals. The performance of ASS-IISCCA was evaluated on a benchmark data set with 35 subjects and compared with the state-of-the-art algorithm task-related component analysis (TRCA). The results show that ASS-IISCCA can significantly improve the performance of SSVEP BCIs with a small number of training trials from a new user, thus helping to facilitate their applications in real world.