YAN Zheng, WAN Xiaojiao, LING Chaodong. An Estimation Method for Multi-channel EEG Data Based on Canonical Correlation Analysis[J]. Chinese Journal of Electronics, 2015, 24(3): 569-572. doi: 10.1049/cje.2015.07.022
Citation: YAN Zheng, WAN Xiaojiao, LING Chaodong. An Estimation Method for Multi-channel EEG Data Based on Canonical Correlation Analysis[J]. Chinese Journal of Electronics, 2015, 24(3): 569-572. doi: 10.1049/cje.2015.07.022

An Estimation Method for Multi-channel EEG Data Based on Canonical Correlation Analysis

doi: 10.1049/cje.2015.07.022
Funds:  This work is supported by the National Natural Science Foundation of China (No. 61203369).
  • Received Date: 2013-12-23
  • Rev Recd Date: 2014-05-14
  • Publish Date: 2015-07-10
  • Electroencephalogram (EEG) signal is often contaminated by electronic noise as well as movement artifacts. This paper presented an algorithm based on Canonical correlation analysis (CCA) to estimate multichannel EEG data. Different from previous studies, in which CCA was mainly used to detect the invariant features specific to each brain state, in this paper, the canonical variates computed by CCA were used to reconstruct the multi-channel EEG data. Firstly, two data sets, EEG signals and the reference signals based on prior knowledge were constructed. Next, canonical variates were computed by projecting the two data sets onto basis vectors. Finally, a least squares solution was used to estimate the multichannel EEG data. The experiment results suggested that the algorithm is capable of reconstructing the actual specific components with high quality. We also hint future possible application of the algorithm in the estimation of functional connectivity patterns at the end of the paper.
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  • H. Hotelling, "Relations between two sets of variants", Biometrika, Vol.35, No.3, pp.321-377, 1936.
    T.W. Anderson, An introduction to multivariate statistical analysis, John Wiley & Sons, New York, USA, pp.18-19, 1984.
    F. Joseph, Jr. Hair, E. Rolph, et al., Multivariate data analysis, Prentice Hall, Inc., New York, USA, pp.41-43, 1998.
    E.J. Hannan, "Canonical correlation and multiple equation system in economics", Econometrica, Vol.25, No.1, pp.123-128, 1967.
    M.L.L. Prevoo, M.A. Van, H.H. Kuper, et al., "Modified disease activity scores that include twenty-eight-joint counts development and validation in a prospective longitudinal study of patients with rheumatoid arthritis", Arthritis & Rheumatism, Vol.38, No.1, pp.44-48, 1995.
    Jacob S. Vestergaard and Allan A. Nielsen, "Automated invariant alignment to improve canonical variates in image fusion of satellite and weather radar data", J. Appl. Meteor. Climatol, Vol.52, No.3, pp.701-709, 2013.
    A.Singh, M.A. Kulkarni, U.C. Mohanty, et al., "Prediction of Indian summer monsoon rainfall (ISMR) using canonical correlation analysis of global circulation model products", Meteorological Applications, Vol.19, No.2, pp.179-188, 2012.
    Lin Zhonglin, Zhang Changshui, Wu Wei, Gao Xiaorong, "Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs", IEEE Transactions on Biomedical Engineering, Vol.54, No.12, pp.1172-1176, 2007.
    Bin Guangyu, Yan Zheng, Gao Xiaorong, et al., "An online multi-channel SSVEP-based BCI using CCA method", Journal of Neural Engieering, Vol.6, No.4, pp.429-433, 2009.
    Yan Zheng, Gao Xiaorong, Gao Shangkai, "Right-and-left visual field stimulation: A frequency and space mixed coding method for SSVEP based brain-computer interface", Science China Information Sciences, Vol.54, No.12, pp.2492-2498, 2011.
    D.H.Brainard, "The psychophysics toolbox", Spatial Vision, Vol.10, No.4, pp.443-446, 1997.
    Cheng Ming, Gao Xiaorong, Gao Shangkai, et al., "Design and implementation of a brain-computer interface with high transfer rates", IEEE Transactions on Biomedical Engineering, Vol.49, No.10, pp.1181-1186, 2002.
    M. Kaminski M.K. Blinowska, "A new method of the description of the information flow in the brain structures", Biology Cybernetics, Vol.65, No.3, pp.203-210, 1991.
    M. Kaminski, M. Ding, W. Truccolo, et al., "Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance",Biology Cybernetics, Vol.85, No.2, pp.145-157, 2001.
    L. Astolfi, F. Cincotti, D. Mattia, et al., "Assessing cortical functional connectivity by linear inverse estimation and directed transfer function: Simulations and application to real data", Clinical Neurophsiology, Vol.116, No.4, pp.920-932, 2005.
    F. Babiloni, "From the analysis of the brain images to the study of brain networks using functional connectivity and multimodal brain signals", Brain Topography, Vol.23, No.2, pp.115-118, 2010.
    A. Delorme, S. Makeig, "EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis", Journal of Neuroscience Methods, Vol.134, No.1, pp.9-21, 2004.
    Yan Zheng and Gao Xiaorong, "Functional connectivity analysis of steady-state visual evoked potentials", Neuroscience Letters, Vol.499, No.3, pp.199-203, 2011.
    J.J. Todd, R. Marois, "Capacity limit of visual short-term memory in human posterior parietal cortex", Nature, Vol.428, No.15, pp.751-754, 2004.
    Bin Guangyu, Lin Zhonglin, Gao Xiaorong, Hong Bo, Gao Shangkai, "The SSVEP topographic scalp maps by canonical correlation analysis", Proc. of IEEE Symposium on EMBS, Vancouver, British Columbia, Canada, pp.3759-3762, 2008.
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