From 33050e625c905154d3ea9a947303ab7e891ba186 Mon Sep 17 00:00:00 2001 From: dengemann Date: Thu, 19 Dec 2024 14:09:35 +0100 Subject: [PATCH] fixes --- README.md | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 685b064..c1757ca 100644 --- a/README.md +++ b/README.md @@ -1,27 +1,36 @@ -# GREEN architecture +# GREEN architecture (Gabor Riemann EEGNet) ![CI](https://github.com/Roche/neuro-green/actions/workflows/lint_and_test.yaml/badge.svg) --- ## About the architecture The model is a deep learning architecture designed for EEG data that combines wavelet transforms and Riemannian geometry. The model is composed of the following layers: It is based on the following layers: - - Convolution: Uses complex-valued Gabor wavelets with parameters (frequency and standard deviation) that are learned during training. + + - Convolution: Uses complex-valued Gabor wavelets with parameters that are learned during training. + - Pooling: Derives features from the wavelet-transformed signal, such as covariance matrices. + - Shrinkage layer: applies [shrinkage](https://scikit-learn.org/1.5/modules/covariance.html#basic-shrinkage) to the covariance matrices. + - Riemannian Layers: Applies transformations to the matrices, leveraging the geometry of the Symmetric Positive Definite (SPD) manifold. + - Fully Connected Layers: Standard fully connected layers for final processing. ![alt text](assets/concept_figure.png) -## Getting started +## Getting started +Clone the repository and install locally. ``` pip install -e . ``` ## Dependencies -``` + +You will need the following dependencies to get most out of GREEN. + +``` scikit-learn torch geotorch @@ -30,6 +39,7 @@ mne ``` ## Examples + Examples illustrating how to train the presented model can be found in the `green/research_code` folder. The notebook `example.ipynb` shows how to train the model on raw EEG data. And the notebook `example_wo_wav.ipynb` shows how to train a submodel that uses covariance matrices as input. In addition, being pure PyTorch, the GREEN model can easily be integrated to [`braindecode`](https://braindecode.org/stable/index.html) routines.