Voxel-ICA for reconstruction of source signal time-series and orientation in EEG and MEG

  • Yaqub Jonmohamadi
  • Govinda Poudel
  • Carrie Innes
  • Richard Jones
Technical Paper

Abstract

In electroencephalography (EEG) and magnetoencephalography signal processing, scalar beamformers are a popular technique for reconstruction of the time-course of a brain source in a single time-series. A prerequisite for scalar beamformers, however, is that the orientation of the source must be known or estimated, whereas in reality the orientation of a brain source is often not known in advance and current techniques for estimation of brain source orientation are effective only for high signal-to-noise ratio (SNR) brain sources. As a result, vector beamformers are applied which do not need the orientation of the source and reconstruct the source time-course in three orthogonal (x, y, and z) directions. To obtain a single time-course, the vector magnitude of the three orthogonal outputs of the beamformer can be calculated at each time point (often called neural activity index, NAI). The NAI, however, is different from the actual time-course of a source since it contains only positive values. Moreover, in estimating the magnitude of the desired source, the background activity (undesired signals) in the beamformer outputs also become all positive values, which, when added to each other, leads to a drop in the SNR. This becomes a serious problem when the desired source is weak. We propose applying independent component analysis (ICA) to the orthogonal time-courses of a brain voxel, as reconstructed by a vector beamformer, to reconstruct the time-course of a desired source in a single time-series. This approach also provides a good estimation of dipole orientation. Simulated and real EEG data were used to demonstrate the performance of voxel-ICA and were compared with a scalar beamformer and the magnitude time-series of a vector beamformer. This approach is especially helpful when the desired source is weak and the orientation of the source cannot be estimated by other means.

Keywords

Beamformer Electroencephalography Independent component analysis Magnetoencephalography Signal-to-noise ratio Time-course reconstruction 

References

  1. 1.
    Spencer M, Leahy RM, Mosher JC, Lewis PS (1992) Adaptive filters for monitoring localized brain activity from surface potential time series. Proc IEEE Asilomar Conf Signal Syst Comput 26:156–160Google Scholar
  2. 2.
    Van Veen BD, van Drongelen W, Yuchtman M, Suzuki A (1997) Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng 44:867–880PubMedCrossRefGoogle Scholar
  3. 3.
    Robinson SE, Vrba J (1998) Functional neuroimaging by synthetic aperture magnetometry (SAM), Proceedings of the 11th International Conference on Biomagnetism pp. 302–305Google Scholar
  4. 4.
    Sekihara K, Nagarajan SS, Poeppel D, Marantz A, Miyashita Y (2001) Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique. IEEE Trans Biomed Eng 48:760–771PubMedCrossRefGoogle Scholar
  5. 5.
    Ward DM, Jones RD, Bones PJ, Carroll GJ (1999) Enhancement of deep epileptiform activity in the EEG via 3-D adaptive spatial filtering. IEEE Trans Biomed Eng 46:707–716PubMedCrossRefGoogle Scholar
  6. 6.
    Congedo M (2006) Subspace projection filters for real-time brain electromagnetic imaging. IEEE Trans Biomed Eng 53:1624–1634PubMedCrossRefGoogle Scholar
  7. 7.
    Greenblatt RE, Ossadtchi A, Pflieger ME (2005) Local linear estimators for the linear bioelectromagnetic inverse problem. IEEE Trans Biomed Eng 53:3403–3412Google Scholar
  8. 8.
    Sekihara K, Sahani M, Nagarajan SS (2005) Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction. NeuroImage 25:1056–1067PubMedCrossRefPubMedCentralGoogle Scholar
  9. 9.
    Huang MX, Shih JJ, Lee RR, Harrington DL, Thoma RJ, Weisend MP, Hanlon F, Paulson KM, Li T, Martin K, Millers GA, Canive JM (2004) Commonalities and differences among vectorized beamformers in electromagnetic source imaging. Brain Topogr 16:139–158PubMedCrossRefGoogle Scholar
  10. 10.
    Mohamadi YJ, Poudel G, Innes C, Jones R (2012) Performance of beamformers on EEG source reconstruction. Proc Int Conf IEEE Eng Med Biol Soc 34:2517–2521Google Scholar
  11. 11.
    Van Veen BD, Buckley KM (1988) Beamforming: a versatile approach to spatial filtering. IEEE Mag Acoust Speech Signal Process 5:4–24Google Scholar
  12. 12.
    Li J (2005) Robust adaptive beamforming. Wiley, HobokenCrossRefGoogle Scholar
  13. 13.
    Sekihara K, Nagarajan SS, Poeppel D, Marantz A (2004) Asymptotic SNR of scalar and vector minimum-variance beamformers for neuromagnetic source reconstruction. IEEE Trans Biomed Eng 51:1726–1734PubMedCrossRefPubMedCentralGoogle Scholar
  14. 14.
    Vrba J, Robinson SE (2001) Signal processing in magnetoencephalography. Methods 25:249–271PubMedCrossRefGoogle Scholar
  15. 15.
    Quraan MA (2011) Characterization of brain dynamics using beamformer techniques: advantages and limitations. In: Pang EW (ed) Magnetoencephalography. InTech, Toronto, pp 67–92Google Scholar
  16. 16.
    Sanei S, Chambers JA (2007) EEG signal processing. Wiley, West SussexCrossRefGoogle Scholar
  17. 17.
    Onton J, Westerfield M, Townsend J, Makeig S (2006) Imaging human EEG dynamics using independent component analysis. Neurosci Biobehav Rev 30:808–822PubMedCrossRefGoogle Scholar
  18. 18.
    Makeig S, Debener S, Onton J, Delorme A (2004) Mining event-related brain dynamics. Trends Cogn Sci 8:204–210PubMedCrossRefGoogle Scholar
  19. 19.
    Jervis B, Belal S, Camilleri K, Cassar T, Bigan C, Linden D, Michalopoulos K, Zervakis M, Besleaga M, Fabri S, Muscat J (2007) The independent components of auditory P300 and CNV evoked potentials derived from single-trial recordings. Physiol Meas 28:745–771PubMedCrossRefGoogle Scholar
  20. 20.
    Ventouras EM, Ktonas PY, Tsekou H, Paparrigopoulos T, Kalatzis I, Soldatos CR (2010) Independent component analysis for source localization of EEG sleep spindle components. Comput Intell Neurosci 2010:1–12CrossRefGoogle Scholar
  21. 21.
    La Foresta F, Mammone N, Morabito FC (2009) PCAICA for automatic identification of critical events in continuous coma-EEG monitoring. Biomed Signal Process Control 4:229–235CrossRefGoogle Scholar
  22. 22.
    Jung TP, Humphries C, Lee TW, Makeig S, McKeown MJ, Iragui V, Sejnowski TJ (1998) Extended ICA remove artifacts from electroencephalographic recordings. Adv Neural Inf Process Syst 10:894–900Google Scholar
  23. 23.
    Jung TP, Makeig S, Humphries C, Lee TW, McKeown MJ, Iragui V, Sejnowski TJ (2000) Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37:163–178PubMedCrossRefGoogle Scholar
  24. 24.
    Oostendorp TF, van Oosterom A (1989) Source parameter estimation in inhomogeneous volume conductors of arbitrary shape. IEEE Trans Biomed Eng 36:382–391PubMedCrossRefGoogle Scholar
  25. 25.
    Oostenveld R, Fries P, Maris E, Schoffelen JM (2011) Fieldtrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011:1–9CrossRefGoogle Scholar
  26. 26.
    Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. J Neurosci Methods 134:9–21PubMedCrossRefGoogle Scholar
  27. 27.
    Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159PubMedCrossRefGoogle Scholar
  28. 28.
    Jonmohamadi Y, Poudel G, Innes C, Jones R (2013) Electromagnetic tomography via source-space ICA, Proc Int Conf IEEE Eng Med Biol Soc 35:37–40Google Scholar
  29. 29.
    Pezeshki A, Van Veen BD, Scharf LL, Cox H (2008) Eigenvalue beamforming using a multirank MVDR beamformer and subspace selection. IEEE Trans Signal Process 56:1954–1967CrossRefGoogle Scholar
  30. 30.
    Scharf LL, Pezeshki A, Van Veen BD, Cox H, Besson O (2006) Eigenvalue beamforming using a multirank MVDR beamformer and subspace selection. Proceedings of 5th Workshop on Defence Application of Signal Process, Queensland, Australia, 10–14 Dec 2006Google Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2014

Authors and Affiliations

  • Yaqub Jonmohamadi
    • 1
    • 2
  • Govinda Poudel
    • 2
    • 3
  • Carrie Innes
    • 2
    • 4
    • 5
  • Richard Jones
    • 1
    • 2
    • 4
    • 5
  1. 1.Department of MedicineUniversity of OtagoChristchurchNew Zealand
  2. 2.New Zealand Brain Research InstituteChristchurchNew Zealand
  3. 3.Monash Biomedical Imaging, Monash UniversityMelbourneAustralia
  4. 4.Department of Electrical and Computer EngineeringUniversity of CanterburyChristchurchNew Zealand
  5. 5.Department of Medical Physics and BioengineeringChristchurch HospitalChristchurchNew Zealand

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