基于时间局部多变量同步指数的基于稳态视觉诱发电位的脑机接口的稳健频率识别。
Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index.
作者信息
Zhang Yangsong, Guo Daqing, Xu Peng, Zhang Yu, Yao Dezhong
机构信息
School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010 China.
Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 610054 China.
出版信息
Cogn Neurodyn. 2016 Dec;10(6):505-511. doi: 10.1007/s11571-016-9398-9. Epub 2016 Jul 19.
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.
多变量同步指数（MSI）已被证明是稳态视觉诱发电位脑机接口（SSVEP-BCI）系统中一种有效的频率识别方法。它根据多通道信号协方差矩阵归一化特征值的熵来测量相关性。在MSI方法中，协方差矩阵的估计忽略了样本的时间局部结构。在本研究中，提出了一种新的时空方法，称为时间局部MSI（TMSI）。这种新方法在协方差矩阵建模中明确利用了时间局部信息。为了评估TMSI的性能，我们在来自11名受试者的真实SSVEP数据集上对这两种方法进行了比较。结果表明，TMSI优于标准MSI。TMSI受益于利用脑电信号的时间局部结构，可能是一种实现基于SSVEP的BCI稳健性能的潜在方法。
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