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

To get an overview on the paper, consider this Twitter thread: 📢🥁💫Our latest benchmarks of #BrainAge prediction from #MEG #EEG is out in @NeuroImage_EiC #BIDS pipeline, 4 datasets, >2500 recordings, #DeepLearning & classical #MachineLearning; @mne_news & Braindecode.

Combining magnetoencephalography with MRI enhances learning of surrogate-biomarkers

For a plain English summary, consider the eLife digest by Helena Perez Valle. To get an overview on the paper, consider this Twitter thread: I am very excited to share our latest work published in @eLife!

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

For a high-level presentation of the main findings check-out our OHBM 2020 Poster. Or visit this Twitter thread: Excited to share our paper @NeuroImage_EiC by @DavSabbagh with @PierreAblin @GaelVaroquaux @agramfort https://t.

Manifold-regression to predict from MEG/EEG brain signals without source modeling

Emergence of β and γ networks following multisensory training

MNE: Software for Acquiring, Processing,and Visualizing MEG/EEG Data

Semantic coding in the occipital cortex of early blind individuals

Encoding and Decoding Neuronal Dynamics: Methodological Framework to Uncover the Algorithms of Cognition

A reproducible MEG/EEG group study with the MNE software: recommendations, quality assessments, and good practices

Autoreject: Automated artifact rejection for MEG and EEG data