Abstract
A template-matching approach combined with multivariate synchronization index (MSI) and independent component analysis (ICA) based spatial filtering for steady-state visual evoked potentials (SSVEPs) frequency recognition is proposed in this paper to enhance the performance of SSVEP-based brain-computer interface (BCI). As a type of electroencephalogram (EEG) signals, SSVEPs generated from underlying brain sources is different from other activities and artifacts, this spatial filter has great potential to enhance the signal-to-noise ratio (SNR) of SSVEPs. This study adapted the MSI-ICA based spatial filters to process test data and the averaged training data, and then used the correlation coefficients between them as features for SSVEP classification. Some conventional methods such as canonical correlation analysis (CCA), filter bank-CCA (FBCCA), and ICA based frequency recognition were adapted to do the contrasting experiment, using a 40-class SSVEP benchmark datasets recorded from 35 subjects. The experimental results demonstrate that the MSI-ICA based method outperforms other methods in terms of the classification accuracy and information transfer rate (ITR).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, X., Wang, Y., Nakanishi, M., et al.: High-speed spelling with a noninvasive brain–computer interface. Proc. Natl. Acad. Sci. 112(44), E6058–E6067 (2015)
Nakanishi, M., Wang, Y., Wang, Y.T., et al.: A high-speed brain speller using steady-state visual evoked potentials. Int. J. Neural Syst. 24(06), 1450019 (2014)
Zhang, Y., Xu, P., Liu, T., et al.: Multiple frequencies sequential coding for SSVEP-based brain-computer interface. PLoS ONE 7(3), e29519 (2012)
Jia, C., Gao, X., Hong, B., et al.: Frequency and phase mixed coding in SSVEP-based brain–computer interface. IEEE Trans. Biomed. Eng. 58(1), 200–206 (2011)
Chen, X., Chen, Z., Gao, S., et al.: A high-ITR SSVEP-based BCI speller. Brain-Computer Interfaces 1(3–4), 181–191 (2014)
Lin, Z., Zhang, C., Wu, W., et al.: Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans. Biomed. Eng. 53(12), 2610–2614 (2006)
Chen, X., Wang, Y., Gao, S., et al.: Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface. J. Neural Eng. 12(4), 046008 (2015)
Wang, Y., Nakanishi, M., Wang, Y.T., et al.: Enhancing detection of steady-state visual evoked potentials using individual training data. In: Proceedings of 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3037–3040 (2014)
Nakanishi, M., Wang, Y., Hsu, S.H., et al.: Independent component analysis-based spatial filtering improves template-based SSVEP detection. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3620–3623. IEEE (2017)
Delorme, A., Sejnowski, T., Makeig, S.: Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage 34(4), 1443–1449 (2007)
Bin, G., Gao, X., Yan, Z., et al.: An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method. J. Neural Eng. 6(4), 046002 (2009)
Ang, K.K., Chin, Z.Y., Zhang, H., et al.: Filter bank common spatial pattern (FBCSP) in brain-computer interface. In: IEEE World Congress on Computational Intelligence Neural Networks, IEEE International Joint Conference on IJCNN 2008, pp. 2390–2397. IEEE (2008)
Wang, Y., Wang, R., Gao, X., et al.: A practical VEP-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 234–240 (2006)
Zhang, Y., Xu, P., Cheng, K., et al.: Multivariate synchronization index for frequency recognition of SSVEP-based brain–computer interface. J. Neurosci. Methods 221, 32–40 (2014)
Wang, Y., Chen, X., Gao, X., et al.: A benchmark dataset for SSVEP-based brain–computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1746–1752 (2017)
Chen, W., Wang, S., Zhang, X., et al.: EEG-based motion intention recognition via multi-task RNNs. In: Proceedings of the 2018 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp. 279–287 (2018)
Acknowledgments
The research work is supported by National Natural Science Foundation of China (U1433116) and the Fundamental Research Funds for the Central Universities (NP2017208).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, Y., Dai, C., Pi, D. (2018). Multivariate Synchronization Index Based on Independent Component Analysis for SSVEP-Based BCI. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-05090-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05089-4
Online ISBN: 978-3-030-05090-0
eBook Packages: Computer ScienceComputer Science (R0)