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Current Medical Imaging

Editor-in-Chief

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

Abstract

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
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