GREEN architecture (Gabor Riemann EEGNet)

About the architecture

GREEN is a deep learning architecture designed for EEG data that combines wavelet transforms and Riemannian geometry.

The model (see our paper for details) is composed of the following layers:

• 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 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.

To dive into the background, check out our paper published in Patterns (Cell Press).

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
lightning
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 routines.

import torch
from braindecode import EEGRegressor
from green.wavelet_layers import RealCovariance
from green.research_code.pl_utils import get_green

green_model = get_green(
	n_freqs=5,	# Learning 5 wavelets
	n_ch=22,	# EEG data with 22 channels
	sfreq=100,	# Sampling frequency of 100 Hz
	dropout=0.5,	# Dropout rate of 0.5 in FC layers
	hidden_dim=[100],	# Use 100 units in the hidden layer
	pool_layer=RealCovariance(),	# Compute covariance after wavelet transform
	bi_out=[20],	# Use a BiMap layer outputing a 20x20 matrix
	out_dim=1,	# Output dimension of 1, for regression
)

device = "cuda" if torch.cuda.is_available() else "cpu"
EarlyStopping(monitor="valid_loss", patience=10, load_best=True)
clf = EEGRegressor(
	module=green_model,
	criterion=torch.nn.CrossEntropyLoss,
	optimizer=torch.optim.AdamW,
	device=device,
	callbacks=[],	# Callbacks can be added here, e.g. EarlyStopping
)

Citation

When using our code, please cite the reference article:

@article{paillard2025,
    author = {Paillard, Joseph and Hipp, J{\"o}rg F. and Engemann, Denis A.},
    title = {GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals},
    doi = {10.1016/j.patter.2025.101182},
    url = {https://doi.org/10.1016/j.patter.2025.101182},
    journal = {Patterns},
    publisher = {Elsevier},
    isbn = {2666-3899}
}

Contributing

We currently do not accept contributions.