Brain Source Localization with covariance fitting approaches

The techniques like fMRI, CT scans, etc are used to localize the activity in the brain. Though these techniques have a high spatial resolution they are very expensive and uncomfortable for the patients. On the other hand, EEG signals can be obtained quite comfortably but suffer from low spatial resolution. A lot of research is being done to effectively extract spatial information from EEG signals. Many inverse techniques like MNE, LORETA, sLORETA, etc are available. All these methods can detect only a few sources and their performance degrades at low SNR. In this paper, covariance-based methods are used to estimate the location of brain activity from EEG signals such as SPICE (sparse iterative covariance-based estimation), and LIKES (likelihood-based estimation of sparse parameters). Intense simulation work has been presented to show that the proposed methods outperform the state-of-the-art methods.