Effcient magnetometer sensor array selection for signal reconstruction and brain source localization

TL;DR

This paper introduces a computationally efficient greedy sensor selection algorithm for MEG that leverages low-dimensional brain activity patterns to optimize sensor placement, improving signal reconstruction and source localization.

Contribution

It proposes a novel QR decomposition-based greedy algorithm for sensor selection that outperforms uniform placement, especially in noisy conditions, for MEG applications.

Findings

Sensor selection improves reconstruction accuracy for shallow sources.

Systematic sensor placement can outperform full arrays in noisy environments.

Method enhances brain activity monitoring efficiency and cost-effectiveness.

Abstract

Magnetoencephalography (MEG) is a noninvasive method for measuring magnetic flux signals caused by brain activity using sensor arrays located on or above the scalp. A common strategy for monitoring brain activity is to place sensors on a nearly uniform grid, or sensor array, around the head. By increasing the total number of sensors, higher spatial-frequency components of brain activity can be resolved as dictated by Nyquist sampling theory. Currently, there are few principled mathematical architectures for sensor placement aside from Nyquist considerations. However, global brain activity often exhibits low-dimensional patterns of spatio-temporal dynamics. The low-dimensional global patterns can be computed from the singular value decomposition and can be leveraged to select a small number of sensors optimized for reconstructing brain signals and localizing brain sources. Moreover, a smaller number of sensors which are systematically chosen can outperform the entire sensor array when considering noisy measurements. We propose a greedy selection algorithm based upon the QR decomposition that is computationally efficient to implement for MEG. We demonstrate the performance of the sensor selection algorithm for the tasks of signal reconstruction and localization. The performance is dependent upon source localization, with shallow sources easily identified and reconstructed, and deep sources more difficult to locate. Our findings suggest that principled methods for sensor selection can improve MEG capabilities and potentially add cost savings for monitoring brain-wide activity.