📣 ✨Excited about our latest work in Patterns (Cell Press): GREEN a light-weight #NeuralNetwork with Riemannian Geometry + learnable wavelets for #EEG #biomarkers: doi.org/10.1016/j.pa... Joint work with @jpaillard.bsky.social & Jörg Hipp! Thread 🧵👇🏻👇🏾👇 - ThreadSky

📣 ✨Excited about our latest work in Patterns (Cell Press): GREEN a light-weight #NeuralNetwork with Riemannian Geometry + learnable wavelets for #EEG #biomarkers: https://doi.org/10.1016/j.patter.2025.101182 Joint work with @jpaillard.bsky.social & Jörg Hipp! Thread 🧵👇🏻👇🏾👇

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dngman.bsky.social•18 days ago

/1 GREEN is made of a few building blocks. But those represent operations well tested in EEG research and neuroscience. This not only makes parameter sparse models but facilitates interpretability by comparing what operations improve prediction performance.

dngman.bsky.social•18 days ago

/2 Riemannian models (G1) are effective EEG decoders as the log map matches lognormal brain scaling (Buzsaki & Mizuseki)? If adding a neural network improves => more complex non-linearity (G2). Or is it about hitting the right frequencies (G3)? GREEN will help you figure it out!

dngman.bsky.social•18 days ago

/3 We threw GREEN G1-G3 at five EEG prediction problems (>5000 EEG recordings) and, well, it depends. The flat Riemannian model (G1) was a very competitive baseline. Adding the FC layer (G2) could lead to improvements, yet, most was gained by adding learnable wavelets (G3).

dngman.bsky.social•18 days ago

/4 Comparing VS complex models e.g. transformers on the CAU data or TUAB, GREEN brings you the same performance with several orders of magnitude fewer parameters and training time. If needed you can expand GREEN via classical phase synchrony measures e.g. PLV - thanks wavelets!

dngman.bsky.social•18 days ago

/5 Comparing learnable wavelets VS a fixed grid of Morlet wavelets allowed us to explore if spectral information was concentrated or diffusely spread. On e.g. sex classification, fixed wavelets were unbeatable (diffuse). Other tasks showed highly localized oscillatory signatures.

dngman.bsky.social•18 days ago

/6 If you want to get your hands dirty with interpretation work, GREEN has you covered: A partial forward pass to get power topographies from the pooling layer (diagonal of covariance). For the classical Berger effect, we nicely see the occipital eyes-closed alpha modulation.

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