Skip to main content
SpringerLink
Log in
Book cover

EEG Signal Processing and Feature Extraction pp 117–140Cite as

Blind Source Separation

  • Chapter
  • First Online:

Abstract

Blind source separation algorithms have been widely used in the EEG signal processing. This chapter introduces the EEG model basis of blind source separation and details of three mainstream algorithms, i.e., principal component analysis (PCA), independent component analysis (ICA), and tensor decomposition, to provide a comprehensive review on this growing topic. The main focus will be on basic principles of applying ICA on continuous EEG data to remove artifacts, PCA, and tensor decomposition on ERP data to conduct group analysis. The introduction of current softwares specialized in PCA and ICA on EEG signal processing will also be covered.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Cattell RB. The procrustes program: producing direct rotation to test a hypothesized factor structure. Comput Behav Sci. 1976;7:258–62.

    Google Scholar 

  • Chaumette E, Comon P, Muller D. ICA-based technique for radiating sources estimation: application to airport surveillance. IEEE Proc Radar Sonar Navig. 1994;142(4):211.. 92363

    Google Scholar 

  • Cichocki A. Tensor decompositions: a new concept in brain data analysis? arXiv Preprint arXiv: 1305.0395. 2013;50(2011):507–17.

    Google Scholar 

  • Cichocki A, Zdunek R, Amari S. Nonnegative matrix and tensor factorization. IEEE Signal Process Mag. 2009;25(1):142–5.

    Article  Google Scholar 

  • Cois J. Blind signal separation: statistical principles. Proc IEEE. 2009;86(10):2009–25.

    Google Scholar 

  • Cong F, Kalyakin I, Chang Z, Ristaniemi T. Analysis on subtracting projection of extracted independent components from EEG recordings. Biomed Tech (Berl). 2011a;56(4):223–34.

    Article  Google Scholar 

  • Cong F, Kalyakin I, Ristaniemi T. Can back-projection fully resolve polarity indeterminacy of independent component analysis in study of event-related potential? Biomed Signal Process Control. 2011b;6(4):422–6.

    Article  Google Scholar 

  • Cong F, Phan AH, Zhao Q, Huttunen-Scott T, Kaartinen J, Ristaniemi T, et al. Benefits of multi-domain feature of mismatch negativity extracted by non-negative tensor factorization from Eeg collected by low-density array. Int J Neural Syst. 2012;22(6):1250025.

    Article  Google Scholar 

  • Cong F, He Z, Hämäläinen J, Leppänen PHT, Lyytinen H, Cichocki A, Ristaniemi T. Validating rationale of group-level component analysis based on estimating number of sources in EEG through model order selection. J Neurosci Methods. 2013;212(1):165–72.

    Article  Google Scholar 

  • Cong F, Puoliväli T, Alluri V, Sipola T, Burunat I, Toiviainen P, et al. Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis. J Neurosci Methods. 2014;223:74–84.

    Article  Google Scholar 

  • Cong F, Lin Q-H, Kuang L-D, Gong X-F, Astikainen P, Ristaniemi T. Tensor decomposition of EEG signals: a brief review. J Neurosci Methods. 2015a;248:59–69.

    Article  Google Scholar 

  • Cong F, Ristaniemi T, Lyytinen H. Advanced signal processing on event related potentials (ERPs): World Scientific; 2015b. p. 131–87.

    Google Scholar 

  • De Lathauwer L, De Moor B, Vandewalle J. Fetal electrocardiogram extraction by blind source. IEEE Trans Biomed Eng. 2000;47(5):567–72.

    Article  Google Scholar 

  • Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9–21.

    Article  Google Scholar 

  • Dien J. Addressing misallocation of variance in principal components analysis of event-related. Potentials. 1998;11(1):43–55.

    CAS  Google Scholar 

  • Dien J. Differential lateralization of trait anxiety and trait fearfulness: evoked potential correlates. Personal Individ Differ. 1999;26:1998–2000.

    Google Scholar 

  • Dien J, Beal DJ, Berg P. Optimizing principal components analysis of event-related potentials: matrix type, factor loading weighting, extraction, and rotations. Clin Neurophysiol. 2005;116:1808–25.

    Article  Google Scholar 

  • Durso G, Prieur P. Blind identification methods applied to Electricity C France’s civil works and power plants monitoring. The 1997 IEEE Signal Processing Workshop on Higher-Order Statistics; 1997. p. 82–86.

    Google Scholar 

  • Eichele T, Rachakonda S, Brakedal B, Eikeland R, Calhoun VD. EEGIFT: group independent component analysis for event-related EEG data. Comput Intell Neurosci. 2011;2011:1–9.

    Article  Google Scholar 

  • Harman HH. Modern factor analysis. Chicago: University of Chicago Press; 1976.

    Google Scholar 

  • Himberg J, Hyvärinen A, Esposito F. Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage. 2004;22(3):1214–22.

    Article  Google Scholar 

  • Hyvarinen A. Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans Neural Netw Learn Syst. 1999;10:626–34.

    Article  CAS  Google Scholar 

  • Hyvärinen A, Karhunenen J, Oja E. Independent component analysis. Neural Comput. 2001;13(7):504.

    Article  Google Scholar 

  • Kaiser H. The Varimax criterion for analytic rotation in factor analysis. Psychometrika. 1958;23(3):187–200.

    Article  Google Scholar 

  • Kolda TG, Bader BW. Tensor decompositions and applications. SIAM Rev. 2009;51(3):455–500.

    Article  Google Scholar 

  • Luck SJ. An introduction to the event-related potential technique. Monogr Soc Res Child Dev. 2014;78(3):388.

    Google Scholar 

  • Makeig S, Bell AJ, Jung T-P, Sejnowski TJ. Independent component analysis of electroencephalographic data. In: Advances in neural information processing systems; 1996. p. 145–51.

    Google Scholar 

  • Mathew G, Reddy VU. Blind separation of multiple co-channel bpsk signals arriving at an antenna array. IEEE Signal Process Lett. 1995;2(9):176–8.

    Article  Google Scholar 

  • Mocks J. Topographic components model for event-related potentials and some biophysical considerations. IEEE Trans Biomed Eng. 1988;35:482.

    Article  CAS  Google Scholar 

  • Mode D, Discovery N. Multi-subject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Rev Biomed Eng. 2012;5:60–73.

    Article  Google Scholar 

  • Sejnowski TJ, Bell AJ. Information-maximization approach to blind separation and blind deconvolution. Technology. 1995;1159(1994):1129–59.

    Google Scholar 

  • Spencer KM, Dien J, Donchin E. Spatiotemporal analysis of the late ERP responses to deviant stimuli. Psychophysiology. 2001;38(2):343–58.

    Article  CAS  Google Scholar 

  • Squires NK, Squires KC, Hillyard SA. Two varieties of long – latency positive waves evoke d by unpredictable auditory stimuli in man. Electroencephalogr Clin Neurophysiol. 1975;38(4):387–401.

    Article  CAS  Google Scholar 

  • Swindlehurst AL, Goris MJ, Ottersten B. Some experiments with array data collected in actual urban and suburban environments. In: First IEEE signal processing workshop on signal processing advances in wireless communications: IEEE; 1997. p. 301–4.

    Google Scholar 

  • van der Veen AJ. Algebraic methods for deterministic blind beamforming. Proc IEEE. 1998;86(10):1987–2008.

    Article  Google Scholar 

  • Zhang Q, Hu G, Tian L, Ristaniemi T, Wang H, Chen H, Wu J, Cong F. Examining stability of independent component analysis based on coefficient and component matrices for voxel-based morphometry of structural magnetic resonance imaging. Cogn Neurodyn. 2018;12:461–70.

    Article  Google Scholar 

  • Zhao Q, Zhou G, Adali T, Zhang L, Cichocki A. Kernelization of tensor-based models for multiway data analysis: processing of multidimensional structured data. IEEE Signal Process Mag. 2013;30(4):137–48.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China

    Fengyu Cong

Authors
  1. Fengyu Cong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fengyu Cong .

Editor information

Editors and Affiliations

  1. CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

    Li Hu

  2. School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, China

    Zhiguo Zhang

7.1 Supplementary Electronic Material (S)

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cong, F. (2019). Blind Source Separation. In: Hu, L., Zhang, Z. (eds) EEG Signal Processing and Feature Extraction. Springer, Singapore. https://doi.org/10.1007/978-981-13-9113-2_7

Download citation

Publish with us

Policies and ethics

Search

Navigation