A reusable benchmark of brain-age prediction from M/EEG resting-state signals

•We provide systematic reusable benchmarks for brain age from M/EEG signals•The benchmarks were carried out on M/EEG from four countries > 2500 recordings•We compared machine learning pipelines capable of handling the non-linear regression task of relating biomedical outcomes to M/EEG dynamics, based on classical machine learning and deep learning•Next to data-driven methods we benchmarked template-based source localization as a practical tool for generating features less affected by electromagnetic field spread•The benchmarks are built on top of the MNE ecosystem and the braindecode package and can be applied on any M/EEG dataset presented in the BIDS format