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Published on: June 30, 2018
Comparison of Machine Learning Techniques Based Brain Source Localization Using EEG Signals
Munsif Ali Jatoi1, Fayaz Ali Dharejo2, Sadam Hussain Teevino3
1Department of Electrical Engineering Technology, The Benazir Bhutto Shaheed University of Technology and Skill Development, Khairpur, Sindh, Pakistan.
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.
