EvoBrain: Dynamic Multi-channel EEG Graph Modeling for Time-Evolving Brain Network (NeurIPS 2025, Spotlight)

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Data

Temple University Seizure Corpus (TUSZ) v1.5.2 dataset is publicly available here. Once your request form is accepted, you can access the dataset.

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Setup

You can install the required dependencies using pip.

pip install -r requirements.txt

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Preprocessing

The preprocessing step resamples all EEG signals to 200Hz, and saves the resampled signals in 19 EEG channels as h5 files.

On terminal, run the following:

python ./data/resample_signals.py --raw_edf_dir <tusz-data-dir> --save_dir <resampled-dir>

where <tusz-data-dir> is the directory where the downloaded TUSZ v1.5.2 data are located, and <resampled-dir> is the directory where the resampled signals will be saved.

Experiments

Configurations

You can modify settings and training parameters by editing the 'args.py' file. This includes adjusting the task, model, number of epochs, learning rate, batch size, and other model training parameters. Alternatively, you can specify them during execution using flags like '--num_epochs'.

RUN

To train and test, you can run:

python main.py --dataset TUSZ --input_dir <resampled-dir> --raw_data_dir <tusz-data-dir> --save_dir results --model_name evobrain --num_epochs 100 

where <save-dir> is the directory where the results are located.

Citation

If you find this work useful, please cite our paper:

@inproceedings{
kotoge2025evobrain,
title={EvoBrain: Dynamic Multi-Channel {EEG} Graph Modeling for Time-Evolving Brain Networks},
author={Rikuto Kotoge and Zheng Chen and Tasuku Kimura and Yasuko Matsubara and Takufumi Yanagisawa and Haruhiko Kishima and Yasushi Sakurai},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025}
}