Artificial Neural Networks for Magnetoencephalography: A review of an emerging field

TL;DR

This review explores the emerging use of Artificial Neural Networks in MEG neuroimaging, highlighting applications in brain decoding, modeling brain processes, and methodological improvements, while discussing current challenges and future directions.

Contribution

It provides a comprehensive overview of how ANNs are applied in MEG data analysis, including classification, modeling, and methodological advancements, with practical recommendations.

Findings

ANNs improve MEG signal classification accuracy

ANNs serve as models for brain information processing

ANNs assist in artifact correction and source estimation

Abstract

Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.