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 and extended CCA methods. A publicly available, 35-subject SSVEP benchmark dataset is used for comparison studies and performance is quantified by classification accuracy and ITR. The ITR of the proposed method is higher than those of TRCA and extended CCA. The proposed method outperforms extended CCA in all conditions and TRCA for time windows greater than 0.3 s. The proposed method also outperforms TRCA when there are limited training blocks and electrodes. This study illustrates that adding subject-independent information to subject-specific models can improve performance of SSVEP-based BCIs.
Highlights
Data Availability Statement: The data that support the findings of this study are openly available at "ftp://sccn.ucsd.edu/pub/ssvep_benchmark_ dataset/
When filter bank was applied in the 0.3 s time window, analysis of variance (ANOVA) revealed significant difference in the accuracy (F(2,68) = 17.79, p
The post-hoc paired t-tests showed that there was no significant difference in accuracy (p = 0.67) and ITR (p = 0.62) between the task-related component analysis (TRCA) method and the proposed method while both methods outperformed the extended canonical correlation analysis (CCA) method (p
Summary
Data Availability Statement: The data that support the findings of this study are openly available at "ftp://sccn.ucsd.edu/pub/ssvep_benchmark_ dataset/. Brain-computer interface (BCI) systems provide novel communication channels for the humans, especially severely disabled individuals [1,2,3]. A character speller system is a highly important BCI system which allows disabled individuals to communicate with their surrounding environment [2]. Steady-state visual evoked potential (SSVEP)-based BCI spellers have attracted much more attention compared with other BCI systems including motor imagery and P300. This is because of their high information transfer rate (ITR), less user training, and ability to deal with problems with a large number of classes [4,5,6,7]
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