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

Abstract

Recently, steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has attracted much attention due to its high information transfer rate (ITR) and increasing number of targets. However, the performance of SSVEP-based methods in terms of accuracy and time length required for target detection can be improved. We propose a new canonical correlation analysis (CCA)-based method to integrate subject-specific models and subject-independent information and enhance BCI performance. To optimize hyperparameters for CCA-based model of a specific subject, we propose to use training data of other subjects. An ensemble version of the proposed method is also developed and used for a fair comparison with ensemble task-related component analysis (TRCA). A publicly available 35-subject SSVEP benchmark dataset is used to evaluate different methods. The proposed method is compared with TRCA and extended CCA methods as reference methods. The performance of the methods is evaluated using classification accuracy and ITR. Offline analysis results show that the proposed method reaches highest ITR compared with TRCA and extended CCA. Also, the proposed method significantly improves performance of extended CCA in all conditions and TRCA for time windows greater than 0.3 s. In addition, the proposed method outperforms TRCA for low number of training blocks and electrodes. This study illustrates that adding subject-independent information to subject-specific models can improve the performance of SSVEP-based BCIs.

Personalized Brain–Computer Interface and Its Applications

“…Meanwhile, Rejer et al used wavelet transforms (WT) to determine the optimal flicker frequency for a specific user to achieve customization of SSVEP-BCI [ 97 ]. In addition, Mehdizavareh et al used the training data of other subjects to optimize the super-parameters of the canonical correlation analysis (CCA) model for specific subjects [ 98 ]. Peters introduced a method to adaptively select the test length of a specific user, which can improve the information transmission rate and the accuracy of letter selection [ 99 ].…”

Classification of SSVEP-based BCIs using Genetic Algorithm

“…GAs will be able to optimize the problem of feature selection with appropriate accuracy within an acceptable time. Based on the output results, it can be concluded that the proposed model is proper for feature selection, and can be used to reduce the processing size, and also BCI costs [28,29]. Finally, a comparison of classification is based on the classification accuracy, the program execution time and the initial database size.…”

“…For each subject and the estimated characteristics from each signal, 640 components are investigated. A variety of approaches exist for extracting features from signals [28,29]. The band Power is measured using the following equations.…”