Machine Learning

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.

Robust learning from corrupted EEG with dynamic spatial filtering

Population modeling with machine learning can enhance measures of mental health

Would you like to know more about the background and context of this work? Please consider the Q/A with me published in this blog by Hans Zauner for GigaScience. You may also be interested in this press-release.

Uncovering the structure of clinical EEG signals with self-supervised learning

Inference and Prediction Diverge in Biomedicine

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.

Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness

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

Semantic coding in the occipital cortex of early blind individuals