Generic placeholder image

Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Comparison of Machine Learning Techniques Based Brain Source Localization Using EEG Signals

Author(s): Munsif Ali Jatoi*, Fayaz Ali Dharejo and Sadam Hussain Teevino

Volume 17, Issue 1, 2021

Published on: 26 February, 2020

Page: [64 - 72] Pages: 9

DOI: 10.2174/1573405616666200226122636

Price: $65


Background: The brain is the most complex organ of the human body with millions of connections and activations. The electromagnetic signals are generated inside the brain due to a mental or physical task performed. These signals excite a bunch of neurons within a particular lobe depending upon the nature of the task performed. To localize this activity, certain machine learning (ML) techniques in conjunction with a neuroimaging technique (M/EEG, fMRI, PET) are developed. Different ML techniques are provided in the literature for brain source localization. Among them, the most common are: minimum norm estimation (MNE), low resolution brain electromagnetic tomography (LORETA) and Bayesian framework based multiple sparse priors (MSP).

Aims: In this research work, EEG is used as a neuroimaging technique.

Methods: EEG data is synthetically generated at SNR=5dB. Afterwards, ML techniques are applied to estimate the active sources. Each dataset is run for multiple trials (>40). The performance is analyzed using free energy and localization error as performance indicators. Furthermore, MSP is applied with a variant number of patches to observe the impact of patches on source localization.

Results: It is observed that with an increased number of patches, the sources are localized with more precision and accuracy as expressed in terms of free energy and localization error, respectively.

Conclusion: The patches optimization within the Bayesian Framework produces improved results in terms of free energy and localization error.

Keywords: Electroencephalography, machine learning, source localization, multiple sparse priors, free energy, localization error.

Graphical Abstract
Baillet S, Mosher JC, Leahy RM. Electromagnetic brain mapping. IEEE Signal Process Mag 2001; 18(6): 14-30.
Jatoi MA, Kamel N, Malik AS, Faye I. EEG based brain source localization comparison of sLORETA and eLORETA. Australas Phys Eng Sci Med 2014; 37(4): 713-21.
[] [PMID: 25359588]
Jatoi MA, Kamel N, Malik AS, Faye I, Begum T. Representing EEG source localization using finite element method. In: 2013 IEEE International Conference on Control System, Computing and Engineering. 168-72.
Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, Grave de Peralta R. EEG source imaging. Clin Neurophysiol 2004; 115(10): 2195-222.
[] [PMID: 15351361]
Jatoi MA, Kamel N, Malik AS, Faye I, Begum T. A survey of methods used for source localization using EEG signals. Biomed Signal Process Control 2014; 11: 42-52.
Adde G, Clerc M, Faugeras O, Keriven R, Kybic J, Papadopoulo T. Symmetric BEM formulation for the M/EEG forward problem. In: Bie Int Conf on Inf Proc in Med Imag. 524-35.
Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV. Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 1993; 65(2): 413.
Pascual-Marqui RD. Review of methods for solving the EEG inverse problem. Int J Bioelectromagn 1999; 1(1): 75-86.
Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol 2002; 24(Suppl. D): 5-12.
[PMID: 12575463]
Pascual-Marqui RD. Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization arXiv preprint arXiv: 07103341 2007.
Mosher JC, Leahy RM. Source localization using recursively applied and projected (RAP) MUSIC. IEEE Trans Signal Process 1999; 47(2): 332-40.
Mosher JC, Leahy RM. Recursive MUSIC: a framework for EEG and MEG source localization. IEEE Trans Biomed Eng 1998; 45(11): 1342-54.
[] [PMID: 9805833]
López JD, Litvak V, Espinosa JJ, Friston K, Barnes GR. Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM. Neuroimage 2014; 84: 476-87.
[] [PMID: 24041874]
Friston K, Harrison L, Daunizeau J, et al. Multiple sparse priors for the M/EEG inverse problem. Neuroimage 2008; 39(3): 1104-20.
[] [PMID: 17997111]
Golub G. Numerical methods for solving linear least squares problems. Numer Math 1965; 7(3): 206-16.
Pascual-Marqui RD, Lehmann D, Koenig T, et al. Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Res 1999; 90(3): 169-79.
[] [PMID: 10466736]
Engl HW, Hanke M, Neubauer A. Regularization of inverse problems. Springer Science & Business Media 1996.
López Hincapié JD, Barnes GR, Espinosa JJ. Single meg/eeg source reconstruction with multiple sparse priors and variable patches. Dyna 2012; 79: 136-44.
Harrison LM, Penny W, Ashburner J, Trujillo-Barreto N, Friston KJ. Diffusion-based spatial priors for imaging. Neuroimage 2007; 38(4): 677-95.
[] [PMID: 17869542]
Friston K, Mattout J, Trujillo-Barreto N, Ashburner J, Penny W. Variational free energy and the Laplace approximation. Neuroimage 2007; 34(1): 220-34.
[] [PMID: 17055746]
Yitembe BR, Crevecoeur G, Van Keer R, Dupré L. Reduced conductivity dependence method for increase of dipole localization accuracy in the EEG inverse problem. IEEE Trans Biomed Eng 2011; 58(5): 1430-40.
[] [PMID: 21257364]
Yitembe BR, Crevecoeur G, Van Keer R, Dupré L. EEG inverse problem solution using a selection procedure on a high number of electrodes with minimal influence of conductivity. IEEE Trans Magn 2010; 47(5): 874-7.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy