Data Processing Approach for Localizing Bio-magnetic Sources in the Brain

TL;DR

This paper introduces a novel data processing approach combining clustering, filtering, and maximum entropy to localize brain bio-magnetic sources from MEG data, effectively narrowing down active regions with high accuracy.

Contribution

It presents a new method that improves localization precision of neural sources in MEG data by integrating multiple data processing techniques.

Findings

Successfully localizes sources within a few millimeters.

Reduces false positives in source localization.

Effective in the auditory cortex region.

Abstract

Magnetoencephalography (MEG) provides dynamic spatial-temporal insight of neural activities in the cortex. Because the number of possible sources is far greater than the number of MEG detectors, the proposition to localize sources directly from MEG data is notoriously ill-posed. Here we develop an approach based on data processing procedures including clustering, forward and backward filtering, and the method of maximum entropy. We show that taking as a starting point the assumption that the sources lie in the general area of the auditory cortex (an area of about 40 mm by 15 mm), our approach is capable of achieving reasonable success in pinpointing active sources concentrated in an area of a few mm's across, while limiting the spatial distribution and number of false positives.