Document Type: Original Research


1 PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran

2 PhD, Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

3 PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran

4 PhD Candidate, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran

5 PhD Candidate, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran

6 PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

7 MD, Iranian Centre of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran


Background: Brain source imaging based on electroencephalogram (EEG) data aims to recover the neuron populations’ activity producing the scalp potentials. This procedure is known as the EEG inverse problem. Recently, beamformers have gained a lot of consideration in the EEG inverse problem.
Objective: Beamformers lack acceptable performance in the case of correlated brain sources. These sources happen when some regions of the brain have simultaneous or correlated activities such as auditory stimulation or moving left and right extremities of the body at the same time. In this paper, we have developed a multichannel beamformer robust to correlated sources.
Material and Methods: In this simulation study, we have looked at the problem of brain source imaging and beamforming from a blind source separation point of view. We focused on the spatially constraint independent component analysis (scICA) algorithm, which generally benefits from the pre-known partial information of mixing matrix, and modified the steps of the algorithm in a way that makes it more robust to correlated sources. We called the modified scICA algorithm Multichannel ICA based EEG Beamformer (MIEB).
Results: We evaluated the proposed algorithm on simulated EEG data and compared its performance quantitatively with three algorithms: scICA, linearly-constrained minimum-variance (LCMV) and Dual-Core beamformers; it is considered that the latter is specially designed to reconstruct correlated sources.
Conclusion: The MIEB algorithm has much better performance in terms of normalized mean squared error in recovering the correlated/uncorrelated sources both in noise free and noisy synthetic EEG signals. Therefore, it could be used as a robust beamformer in recovering correlated brain sources.


  1. Acar ZA, Makeig S. Neuroelectromagnetic forward head modeling toolbox. J Neurosci Methods. 2010;190:258-70. doi: 10.1016/j.jneumeth.2010.04.031. PubMed PMID: 20457183.PubMed PMCID: PMC4126205.
  2. Hallez H, Vanrumste B, Grech R, Muscat J, De Clercq W, Vergult A, et al. Review on solving the forward problem in EEG source analysis. J Neuroeng Rehabil. 2007;4:46. doi: 10.1186/1743-0003-4-46. PubMed PMID: 18053144. PubMed PMCID: PMC2234413.
  3. Grech R, Cassar T, Muscat J, Camilleri KP, Fabri SG, Zervakis M, et al. Review on solving the inverse problem in EEG source analysis. J Neuroeng Rehabil. 2008;5:25. doi: 10.1186/1743-0003-5-25. PubMed PMID: 18990257. PubMed PMCID: PMC2605581.
  4. Pascual-Marqui RD. Review of methods for solving the EEG inverse problem. Int J Bioelectromagn. 1999;1:75-86.
  5. Mosher JC, Lewis PS, Leahy RM. Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans Biomed Eng. 1992;39:541-57. doi: 10.1109/10.141192. PubMed PMID: 1601435.
  6. Sekihara K, Nagarajan SS. Adaptive spatial filters for electromagnetic brain imaging. Berlin: Springer Science & Business Media; 2008.
  7. Jonmohamadi Y, Poudel G, Innes C, Weiss D, Krueger R, Jones R. Comparison of beamformers for EEG source signal reconstruction. Biomed Signal Process Control. 2014;14:175-88.
  8. Murzin V, Fuchs A, Scott Kelso JA. Detection of correlated sources in EEG using combination of beamforming and surface Laplacian methods. J Neurosci Methods. 2013;218:96-102. doi: 10.1016/j.jneumeth.2013.05.001. PubMed PMID: 23769770. PubMed PMCID: PMC3742082.
  9. Diwakar M, Huang MX, Srinivasan R, Harrington DL, Robb A, Angeles A, et al. Dual-Core Beamformer for obtaining highly correlated neuronal networks in MEG. Neuroimage. 2011;54:253-63. doi: 10.1016/j.neuroimage.2010.07.023. PubMed PMID: 20643211.
  10. Georgieva P, Bouaynaya N, Silva F, Mihaylova L, Jain LC. A Beamformer-Particle Filter Framework for Localization of Correlated EEG Sources. IEEE J Biomed Health Inform. 2016;20:880-92. doi: 10.1109/JBHI.2015.2413752. PubMed PMID: 25794405.
  11. Brookes MJ, Stevenson CM, Barnes GR, Hillebrand A, Simpson MI, Francis ST, et al. Beamformer reconstruction of correlated sources using a modified source model. Neuroimage. 2007;34:1454-65. doi: 10.1016/j.neuroimage.2006.11.012. PubMed PMID: 17196835.
  12. Van Veen BD, Van Drongelen W, Yuchtman M, Suzuki A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng. 1997;44:867-80. doi: 10.1109/10.623056. PubMed PMID: 9282479.
  13. Sekihara K, Nagarajan SS. Electromagnetic brain imaging: a bayesian perspective. New York: Springer; 2015.
  14. Sekihara K, Nagarajan SS, Poeppel D, Marantz A. Performance of an MEG adaptive-beamformer technique in the presence of correlated neural activities: effects on signal intensity and time-course estimates. IEEE Trans Biomed Eng. 2002;49:1534-46. PubMed PMID: 12549735.
  15. James CJ, Gibson OJ. Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis. IEEE Trans Biomed Eng. 2003;50:1108-16. doi: 10.1109/TBME.2003.816076. PubMed PMID: 12943278.
  16. Hesse CW, James CJ. On semi-blind source separation using spatial constraints with applications in EEG analysis. IEEE Trans Biomed Eng. 2006;53:2525-34. doi: 10.1109/TBME.2006.883796. PubMed PMID: 17153210.
  17. De Vos M, De Lathauwer L, Van Huffel S. Spatially constrained ICA algorithm with an application in EEG processing. Signal Processing. 2011;91:1963-72.
  18. Cichocki A, Amari S-I. Adaptive blind signal and image processing: learning algorithms and applications. New Jersey: John Wiley & Sons; 2002.
  19. Oja H, Nordhausen K. Independent component analysis. Encyclopedia of Environmetrics. 2013. doi: 10.1002/9780470057339.vnn086.
  20. Cardoso J-F, Souloumiac A. Blind beamforming for non-Gaussian signals. IEE Proceedings F (Radar and Signal Processing). 1993;140:362-70.
  21. Belouchrani A, Abed-Meraim K, Cardoso J-F, Moulines E. A blind source separation technique using second-order statistics. IEEE Trans Signal Process. 1997;45:434-44.
  22. Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw. 1999;10:626-34. doi: 10.1109/72.761722. PubMed PMID: 18252563.
  23. Roberts SJ. Independent component analysis: source assessment and separation, a Bayesian approach. IEE Proceedings-Vision, Image and Signal Processing. 1998;145:149-54.
  24. Golub GH, Van Loan CF. Matrix computations. 4th ed. Baltimore: Johns Hopkins University Press; 2013.
  25. Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. 2011;2011:156869. doi: 10.1155/2011/156869. PubMed PMID: 21253357. PubMed PMCID: PMC3021840.
  26. Assecondi S, Ostwald D, Bagshaw AP. Reliability of information-based integration of EEG and fMRI data: a simulation study. Neural Comput. 2015;27:281-305. doi: 10.1162/NECO_a_00695. PubMed PMID: 25514112.