Single meg/eeg source reconstruction with multiple sparse priors and variable patches (2012)
MEG/EEG brain imaging has become an important tool in neuroimaging. The reconstruction of cortical current flow is an ill-posed problem, but its uncertainty can be reduced by including prior information within a Bayesian framework. Typically this involves using knowledge of the cortical manifold to construct a set of possible regions of neural source activity. In this work a second stage is proposed to reduce localisation error without severely increasing the computational load. This stage consists of iteratively updating the set of possible regions based on previous reconstructions, in order to focus on those brain regions with a higher probability of being active. The proposed methodology was tested with synthetic MEG datasets giving as result zero localisation error for single sources and different noise levels. Real data from a visual attention study was used for validation.