title: Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index.
abstract: Multivariate synchronization index (MSI) has been proved to be an efficient method for frequency recognition in SSVEP-BCI systems. It measures the correlation according to the entropy of the normalized eigenvalues of the covariance matrix of multichannel signals. In the MSI method, the estimation of covariance matrix omits the temporally local structure of samples. In this study, a new spatio-temporal method, termed temporally local MSI (TMSI), was presented. This new method explicitly exploits temporally local information in modelling the covariance matrix. In order to evaluate the performance of the TMSI, we performs a comparison between the two methods on the real SSVEP datasets from eleven subjects. The results show that the TMSI outperforms the standard MSI. TMSI benefits from exploiting the temporally local structure of EEG signals, and could be a potential method for robust performance of SSVEP-based BCI.
authors.affiliation: School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010 China.
authors.forename: Yangsong
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 610054 China.
authors.forename: Daqing
authors.identifier: 0000-0003-2138-2530
authors.initials: D
authors.lastname: Guo
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 610054 China.
authors.forename: Peng
authors.initials: P
authors.lastname: Xu
authors.affiliation: Key Laboratory for Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 610054 China.
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
keywords: Brain-computer interface (BCI); Multivariate synchronization index (MSI); Steady-state visual evoked potential (SSVEP); Temporally local information