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.
![](https://cdn.statically.io/img/media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11571-016-9398-9/MediaObjects/11571_2016_9398_Fig1_HTML.gif)
![](https://cdn.statically.io/img/media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11571-016-9398-9/MediaObjects/11571_2016_9398_Fig2_HTML.gif)
![](https://cdn.statically.io/img/media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11571-016-9398-9/MediaObjects/11571_2016_9398_Fig3_HTML.gif)
![](https://cdn.statically.io/img/media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11571-016-9398-9/MediaObjects/11571_2016_9398_Fig4_HTML.gif)
![](https://cdn.statically.io/img/media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11571-016-9398-9/MediaObjects/11571_2016_9398_Fig5_HTML.gif)
Similar content being viewed by others
References
Chang MH, Lee JS, Heo J, Park KS (2016) Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI. J Neurosci Methods 258:104–113
Cheng M, Gao X, Gao S, Xu D (2002) Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng 49:1181–1186
Chen X, Chen Z, Gao S, Gao X (2014) A high-ITR SSVEP-based BCI speller. Brain Comput Interf 1:181–191
Chen X, Wang Y, Nakanishi M, Gao X, Jung TP, Gao S (2015) High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci USA 112(44):E6058–E6067
Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74:829–836
Carmeli C, Knyazeva MG, Innocenti GM, De Feo O (2005) Assessment of EEG synchronization based on state-space analysis. Neuroimage 25:339–354
Freeman WJ (2007) Definitions of state variables and state space for brain-computer interface. Part 2: extraction and classification of feature vectors. Cognit Neurodyn 1:85–96
Friman O, Volosyak I, Graser A (2007) Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Trans Biomed Eng 54:742–750
Gao S, Wang Y, Gao X, Hong B (2014) Visual and auditory brain-computer interfaces. IEEE Trans Biomed Eng 61:1436–1447
He B, Baxter B, Edelman BJ, Cline CC, Ye WW (2015) Noninvasive brain-computer interfaces based on sensorimotor rhythms. Proc IEEE 103:907–925
Huang G, Liu G, Meng J, Zhang D, Zhu X (2010) Model based generalization analysis of common spatial pattern in brain computer interfaces. Cognit Neurodyn 4:217–223
Jia C, Gao X, Hong B, Gao S (2011) Frequency and phase mixed coding in SSVEP-based brain-computer interface. IEEE Trans Biomed Eng 58:200–206
Joudaki A, Salehi N, Jalili M, Knyazeva MG (2012) EEG-based functional brain networks: does the network size matter? PLoS One 7:e35673
Lance BJ, Kerick SE, Ries AJ, Oie KS, McDowell K (2012) Brain-computer interface technologies in the coming decades. Proc IEEE 100:1585–1599
Lin Z, Zhang C, Wu W, Gao X (2006) Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng 53:2610–2614
Long J, Gu Z, Li Y, Yu T, Li F, Fu M (2011) Semi-supervised joint spatio-temporal feature selection for P300-based BCI speller. Cognit Neurodyn 5:387–398
Nan W, Wong CM, Wang B, Wan F, Mak PU, Mak PI, Vai MI (2011) A comparison of minimum energy combination and canonical correlation analysis for SSVEP detection. In: Proceedings of 5th international IEEE EMBS conference on neural engineering, pp 469–472
Nakanishi M, Wang Y, Wang Y, Jung TP (2015) A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. PLoS One 10:e0140703
Tello RM, Muller SM, Bastos-Filho T, Ferreira A (2014a) A comparison of techniques and technologies for SSVEP classification. In: Proceedings of 5th ISSNIP-IEEE biosignalling and biorobotics conference on biosignal and robot better safer living, pp 1–6
Tello RM, Torres Muller SM, Bastos-Filho T, Ferreira A (2014b) Comparison of new techniques based on EMD for control of a SSVEP-BCI. In: IEEE 23rd international symposium on industrial electronics (ISIE), pp 992–997
Wang H (2010) Temporally local maximum signal fraction analysis for artifact removal from biomedical signals. IEEE T Signal Proces 58:4919–4925
Wang H, Xu D (2012) Comprehensive common spatial patterns with temporal structure information of EEG data: minimizing nontask related EEG component. IEEE Trans Biomed Eng 59:2496–2505
Wang H, Tang Q, Zheng W (2012) L1-norm-based common spatial patterns. IEEE Trans Biomed Eng 59:653–662
Wang H, Zhang Y, Waytowich N, Krusienski D, Zhou G, Jin J, Wang X, Cichocki A (2016) Discriminative feature extraction via multivariate linear regression for SSVEP-based BCI. IEEE Trans Neural Syst Rehabil Eng 24(5):532–541
Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113:767–791
Wu Z, Yao D (2007) Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs. J Neural Eng 5:36
Xu P, Yang P, Lei X, Yao D (2011) An enhanced probabilistic LDA for multi-class brain computer interface. PLoS One 6:e14634
Xu M et al (2013) Channel selection based on phase measurement in P300-based brain-computer interface. PLoS One 8:e60608
Yuan H, He B (2014) Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans Biomed Eng 61:1425–1435
Zhang Y, Zhao Q, Jin J, Wang X, Cichocki A (2012a) A novel BCI based on ERP components sensitive to configural processing of human faces. J Neural Eng 9:026018
Zhang Y, Xu P, Liu T, Hu J, Zhang R, Yao D (2012b) Multiple frequencies sequential coding for SSVEP-based brain-computer interface. PLoS One 7:e29519
Zhang R, Xu P, Liu T, Zhang Y, Guo L, Li P, Yao D (2013a) Local temporal correlation common spatial patterns for single trial EEG classification during motor imagery. Comput Math Methods Med 2013:591216
Zhang Y, Zhou G, Jin J, Wang M, Wang X, Cichocki A (2013b) L1-regularized multiway canonical correlation analysis for SSVEP-based BCI. IEEE Trans Neural Syst Rehabil Eng 21:887–896
Zhang Y, Xu P, Cheng K, Yao D (2014a) Multivariate synchronization index for frequency recognition of SSVEP-based brainCcomputer interface. J Neurosci Methods 221:32–40
Zhang Y, Dong L, Zhang R, Yao D, Zhang Y, Xu P (2014b) An efficient frequency recognition method based on likelihood ratio test for SSVEP-based BCIs. Comput Math Methods Med, Article ID 908719
Zhang Y, Zhou G, Jin J, Wang X, Cichocki A (2014c) Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. Int J Neural Syst 24:1450013
Zhang Z, Jung T-P, Makeig S, Pi Z, Rao B (2014d) Spatiotemporal sparse Bayesian learning with applications to compressed sensing of multichannel physiological signals. IEEE Trans Neural Syst Rehabil Eng 22:1186–1197
Zhang Y, Zhou G, Jin J, Wang X, Cichocki A (2015) SSVEP recognition using common feature analysis in brain-computer interface. J Neurosci Methods 244:8–15
Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (Nos. 81401484, 61527815, 81571770, 61305028) the Doctoral Fund of Southwest University of Science and Technology (Grant No. 15zx7115),and Shanghai Chenguang Program under Grant 14CG31.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Zhang, Y., Guo, D., Xu, P. et al. Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index. Cogn Neurodyn 10, 505–511 (2016). https://doi.org/10.1007/s11571-016-9398-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11571-016-9398-9