Multivariate synchronization index for frequency recognition of SSVEP-based brain–computer interface

Zhang, Yangsong; Xu, Peng; Cheng, Kuo Hsing; Yao, Dezhong

doi:10.1016/j.jneumeth.2013.07.018

Cited by 196 publications

(129 citation statements)

References 27 publications

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“…These experimental results indicated that TMSI is a promising method for the frequency detection in SSVEP-based BCI. There are only four frequencies in this study, which could be used for controlling the limbs of the robot (Zhang et al 2014a). In our future study, we need to investigate the performance of the TMSI and compare it with other algorithms in the system with larger number of stimulus targets.…”

Section: Resultsmentioning

confidence: 99%

“…The TMSI was evaluated on the Eleven datasets used in our previous study (Zhang et al 2014a). Eleven healthy subjects (two female and nine male, aged from 21 to 25 years) were recruited to participate in this study, which was approved by the Human Research and Ethics Committee of the University of Electronic Science and Technology of China.…”

Section: Ssvep Datasetsmentioning

confidence: 99%

“…This kind of methods could efficiently exploit multichannel EEG signals to extract more robust features, then achieve higher detection accuracy. The minimum energy combination (MEC) (Friman et al 2007), canonical correlation analysis (CCA) (Lin et al 2006) and multivariate synchronization index (MSI) (Zhang et al 2014a) are the mainly methods adopted in recent SSVEP-based BCI systems. The aim of MEC is to find optimized spatial filters to minimize the nuisance signals and noise as much as possible (Friman et al 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several extended CCA method have been proposed, such as individual template based CCA (IT-CCA) , multiset canonical correlation analysis (MCCA) (Zhang et al 2014c), L1-regularized version of MCCA (Zhang et al 2013b). The MSI is a novel method to estimate the synchronization index between the multiple signals and the preconstructed reference signals as the classification feature (Zhang et al 2014a). The MSI showed the best performance among the three method (Zhang et al 2014a;Tello et al 2014a, b).…”

Section: Introductionmentioning

confidence: 99%

“…The MSI is a novel method to estimate the synchronization index between the multiple signals and the preconstructed reference signals as the classification feature (Zhang et al 2014a). The MSI showed the best performance among the three method (Zhang et al 2014a;Tello et al 2014a, b). Although the MSI method provides satisfactory performance, it completely ignores the temporal information of the EEG signal in the covariances modelling.…”

Section: Introductionmentioning

confidence: 99%

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Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index

Zhang

Guo

et al. 2016

Cogn Neurodyn

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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.Keywords Steady-state visual evoked potential (SSVEP) Á Brain-computer interface (BCI) Á Multivariate synchronization index (MSI) Á Temporally local information

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Section: Resultsmentioning

confidence: 99%

Section: Ssvep Datasetsmentioning

confidence: 99%