Localization of MEG and EEG Brain Signals by Alternating Projection

TL;DR

This paper introduces a new iterative localization method for MEG and EEG signals using Alternating Projection, which outperforms existing techniques especially in low SNR and with correlated sources.

Contribution

The paper presents a novel AP-based algorithm for brain signal localization that is robust to noise and source correlation, improving upon existing scanning methods.

Findings

Robust localization performance across low SNR conditions.

Effective in resolving closely spaced and correlated sources.

Superior accuracy compared to traditional scanning methods.

Abstract

We present a novel solution to the problem of localization of MEG and EEG brain signals. The solution is sequential and iterative, and is based on minimizing the least-squares (LS)criterion by the Alternating Projection (AP) algorithm, which is well known in the context of array signal processing. Unlike existing scanning solutions belonging to the beamformer and multiple-signal classification (MUSIC) families, the algorithm has good performance in low signal-to-noise ratio (SNR) and can cope with closely spaced sources and any mixture of correlated sources. Results from simulated and experimental MEG data from a real phantom demonstrated robust performance across an extended SNR range, the entire inter-source correlation range, and across multiple sources, with consistently superior localization accuracy than popular scanning methods.