BAYES IAN BASED BRAIN SOURCE LOCALIZATION TECHNIQUE USING EEG SIGNALS
Brain Source localization from EEG/MEG is an ill-posed inverse problem with high uncertainty in the solution. This source localization information is used to diagnose various brain disorders such as epilepsy, schizophrenia, stress, depression and Alzheimer. Different algorithms are proposed for t...
| Main Author: | JATOI, MUNSIF ALI |
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| Format: | Thesis |
| Language: | English |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-utpedia.21912 / |
| Published: |
2016
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| Subjects: | |
| Online Access: |
http://utpedia.utp.edu.my/21912/1/2016%20-ELECTRICAL%20%26%20ELECTRONIC%20-%20BAYESIAN%20BASED%20BRAIN%20SOURCE%20LOCALIZATION%20TECHNIQUE%20USING%20EEG%20SIGNALS%20-%20MUNSIF%20ALI%20JATOI.pdf http://utpedia.utp.edu.my/21912/ |
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| Summary: |
Brain Source localization from EEG/MEG is an ill-posed inverse problem with high
uncertainty in the solution. This source localization information is used to diagnose
various brain disorders such as epilepsy, schizophrenia, stress, depression and
Alzheimer. Different algorithms are proposed for the solution of this ill-posed
problem which include minimum norm estimation (MNE), second order Laplacian
based low resolution brain electromagnetic tomography (LORET A), standardized
LORETA (sLORETA), exact LORETA, subspace based multiple signal classifier
(MUSIC), Beamformer and Bayesian framework based multiple source priors (MSP).
The solution provided by each of the algorithms mentioned above is characterized by
various parameters which include the accuracy, computational complexity and
localization error. The existing algorithms suffer from low resolution (LORETA
family), high computational time (subspace algorithms and FOCUSS, WMNLORETA)
and no validation. |
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