GREEN: A lightweight architecture for biomarker exploration with EEG signals

Our deep learning for exploring EEG biomarkers is out now in Patterns!

Summary (in brief)

Our brains generate electrical signals whose unique patterns reveal information about aging, health, and even thoughts. This information is useful for studying brain disorders and developing novel therapies. However, traditional analysis methods can miss subtle but important brain-activity patterns. GREEN, introduced here, is an AI model that readily decodes brainwave patterns. By combining innovative mathematical tools with deep learning, GREEN detects subtle changes in brain activity with a low computational footprint while providing interpretable outputs that reveal insights into brain function.

Citation

@article{GREEN,
    author = {Paillard, Joseph and Hipp, J{\"o}rg F. and Engemann, Denis A.},
    doi = {10.1016/j.patter.2025.101182},
    journal = {Patterns},
    publisher = {Elsevier},
    title = {GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals},
    url = {https://doi.org/10.1016/j.patter.2025.101182}
}