README

This repository is the official implementation of our work "A Grassmannian Manifold Self-Attention Network for Signal Classification", which has been accepted by IJCAI 2024.

The implementation of our GDLNet is based on the code of Matt [a]. We would like to express our sincere thanks to the authors.

Requirements

Step 1:

To install requirements:

conda env create -f /path/to/mAtt_env.yml
conda activate mAtt_env

Step 2:

Download datasets and unzip them to the folder 'data'.

Dataset

MAMEM-SSVEP-II: https://www.mamem.eu/results/datasets/
BCI-ERN: https://www.kaggle.com/competitions/inria-bci-challenge/data

Link to download data

Training and testing

To train and test the mAtt in the paper, run this command:

python mAtt_<which_dataset>.py

All default hyperparameters are already set in files. 'which_dataset' can be chosen as 'mamem' (MAMEM-SSVEP-II), or 'bcicha' (BCI-ERN).

Reference

[a] Pan, Y. T., Chou, J. L., & Wei, C. S. (2022). MAtt: A manifold attention network for EEG decoding. Advances in Neural Information Processing Systems, 35, 31116-31129.

GitWR / GDLNet