Bayesian Framework based Brain Source Localization Using High SNR EEG Data

2018 ◽  
Author(s):  
Munsif Ali Jatoi ◽  
Nidal Kamel ◽  
Anwar Ali Gaho ◽  
Fayaz Ali Dharejo
Keyword(s):  
Source Localization ◽  
Bayesian Framework ◽  
Eeg Data ◽  
Brain Source Localization

Comparison of Machine Learning Techniques Based Brain Source Localization Using EEG Signals

2020 ◽  
Vol 16 ◽  
Author(s):  
Munsif Ali Jatoi ◽  
Fayaz Ali Dharejo ◽  
Sadam Hussain Teevino
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Source Localization ◽  
Research Work ◽  
Bayesian Framework ◽  
Machine Learning Techniques ◽  
Localization Error ◽  
The Impact ◽  
The Brain ◽  
Brain Source Localization

Background: The Brain is the most complex organ of 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 nature of 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 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 variant number of patches to observe the impact of patches on source localization. Results: It is observed that with 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 Bayesian Framework produces improved results in terms of free energy and localization error.

Bayesian Algorithm Based Localization of EEG Recorded Electromagnetic Brain Activity

2019 ◽  
Vol 15 (2) ◽  
pp. 184-193
Author(s):  
Munsif Ali Jatoi ◽  
Nidal Kamel ◽  
Sayed Hyder Abbas Musavi ◽  
José David López
Keyword(s):  
Inverse Problem ◽  
Source Localization ◽  
Brain Activity ◽  
Research Work ◽  
Bayesian Framework ◽  
Forward Problem ◽  
Source Reconstruction ◽  
Eeg Data ◽  
Physical Task ◽  
Ill Posed

Background: Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms. Methods: Such activity is recorded through various neuroimaging techniques like fMRI, EEG, MEG etc. EEG signals based localization is termed as EEG source localization. The source localization problem is defined by two complementary problems; the forward problem and the inverse problem. The forward problem involves the modeling how the electromagnetic sources cause measurement in sensor space, while the inverse problem refers to the estimation of the sources (causes) from observed data (consequences). Usually, this inverse problem is ill-posed. In other words, there are many solutions to the inverse problem that explains the same data. This ill-posed problem can be finessed by using prior information within a Bayesian framework. This research work discusses source reconstruction for EEG data using a Bayesian framework. In particular, MSP, LORETA and MNE are compared. Results: The results are compared in terms of variational free energy approximation to model evidence and in terms of variance accounted for in the sensor space. The results are taken for real time EEG data and synthetically generated EEG data at an SNR level of 10dB. Conclusion: In brief, it was seen that MSP has the highest evidence and lowest localization error when compared to classical models. Furthermore, the plausibility and consistency of the source reconstruction speaks to the ability of MSP technique to localize active brain sources.

scholarly journals MEG Source Localization under Multiple Constraints: An Extended Bayesian Framework

2007 ◽  
pp. 383-395
Author(s):  
Jeremie Mattout ◽  
Christophe Phillips ◽  
Richard Henson ◽  
Karl Friston
Keyword(s):  
Source Localization ◽  
Bayesian Framework ◽  
Multiple Constraints

scholarly journals A Novel Bayesian Approach for EEG Source Localization

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Vangelis P. Oikonomou ◽  
Ioannis Kompatsiaris
Keyword(s):  
Source Localization ◽  
Prior Distribution ◽  
State Of The Art ◽  
Brain Regions ◽  
Regularization Term ◽  
Accurate Localization ◽  
Eeg Data ◽  
Spatially Extended ◽  
Eeg Source Localization ◽  
Sparse Prior

We propose a new method for EEG source localization. An efficient solution to this problem requires choosing an appropriate regularization term in order to constraint the original problem. In our work, we adopt the Bayesian framework to place constraints; hence, the regularization term is closely connected to the prior distribution. More specifically, we propose a new sparse prior for the localization of EEG sources. The proposed prior distribution has sparse properties favoring focal EEG sources. In order to obtain an efficient algorithm, we use the variational Bayesian (VB) framework which provides us with a tractable iterative algorithm of closed-form equations. Additionally, we provide extensions of our method in cases where we observe group structures and spatially extended EEG sources. We have performed experiments using synthetic EEG data and real EEG data from three publicly available datasets. The real EEG data are produced due to the presentation of auditory and visual stimulus. We compare the proposed method with well-known approaches of EEG source localization and the results have shown that our method presents state-of-the-art performance, especially in cases where we expect few activated brain regions. The proposed method can effectively detect EEG sources in various circumstances. Overall, the proposed sparse prior for EEG source localization results in more accurate localization of EEG sources than state-of-the-art approaches.

Source localization with EEG data for BP shows major activities in the frontal areas of the brain

2007 ◽  
Author(s):  
Rajkishore Prasad ◽  
Hovagim Bakardjian ◽  
A. Cichocki ◽  
F. Matsuno
Keyword(s):  
Source Localization ◽  
Eeg Data ◽  
The Brain
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