Brain Source Localization by Alternating Projection

TL;DR

This paper introduces an iterative alternating projection method for localizing brain signals in MEG and EEG data, demonstrating superior accuracy and robustness over existing scanning methods, especially with correlated sources and model errors.

Contribution

The paper proposes a novel sequential and iterative localization approach based on alternating projection, improving robustness and accuracy over traditional beamformer and MUSIC methods.

Findings

Robust localization accuracy in simulated and experimental MEG data.

Superior performance compared to beamformer and MUSIC methods.

Enhanced robustness to head movement errors and correlated sources.

Abstract

We present a novel solution to the problem of localizing magnetoencephalography (MEG) and electroencephalography (EEG) brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares criterion by the Alternating Projection algorithm. Results from simulated and experimental MEG data from a human subject demonstrated robust performance, with consistently superior localization accuracy than scanning methods belonging to the beamformer and multiple-signal classification (MUSIC) families. Importantly, the proposed solution is more robust to forward model errors resulting from head rotations and translations, with a significant advantage in highly correlated sources.