Comparison of machine learning techniques based brain source localization using eeg signals

Munsif Ali Jatoi, Fayaz Ali Dharejo, Sadam Hussain Teevino

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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 devel-oped. 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.

Original languageBritish English
Pages (from-to)64-72
Number of pages9
JournalCurrent Medical Imaging
Volume17
Issue number1
DOIs
StatePublished - 2021

Keywords

  • Electroencephalography
  • Free energy
  • Localization error
  • Machine learning
  • Multiple sparse priors
  • Source localization

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