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
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.