Title:Comparison of Machine Learning Techniques Based Brain Source Localization Using EEG Signals
Volume: 17
Author(s): Munsif Ali Jatoi*, Fayaz Ali Dharejo and Sadam Hussain Teevino
Affiliation:
- Department of Electrical Engineering Technology, The Benazir Bhutto Shaheed University of Technology and Skill Development, Khairpur, Sindh,Pakistan
Keywords:
Electroencephalography, machine learning, source localization, multiple sparse priors, free energy, localization error.
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.
