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Andrei Ivanov 23c566a60a | 6 months ago | |
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.. | ||
EEGGraphDataset.py | 1 year ago | |
README.md | 2 years ago | |
deep_EEGGraphConvNet.py | 1 year ago | |
main.py | 6 months ago | |
shallow_EEGGraphConvNet.py | 1 year ago |
This example is a simplified version that presents how to utilize the original EEG-GCNN model proposed in the paper EEG-GCNN, implemented with DGL library. The example removes cross validation and optimal decision boundary that are used in the original code. The performance stats are slightly different from what is present in the paper. The original code is here.
EEGGraphDataset.py
, we specify the channels and electrodes and use precomputed spectral coherence values to compute the edge weights. To use this example in your own advantage, please specify your channels and electrodes in __init__
function of EEGGraphDataset.py
. # ....loop over all windows in dataset....
# window data is 10-second preprocessed multi-channel timeseries (shape: n_channels x n_timepoints) containing all channels in ch_names
window_data = np.expand_dims(window_data, axis=0)
# ch_names are listed in EEGGraphDataset.py
for ch_idx, ch in enumerate(ch_names):
# number of channels is is len(ch_names), which is 8 in our case.
spec_coh_values, _, _, _, _ = mne.connectivity.spectral_connectivity(data=window_data, method='coh', indices=([ch_idx]*8, range(8)), sfreq=SAMPLING_FREQ,
fmin=1.0, fmax=40.0, faverage=True, verbose=False)
figshare_upload/master_metadata_index.csv
, figshare_upload/psd_features_data_X
, figshare_upload/labels_y
, figshare_upload/psd_shallow_eeg-gcnn/spec_coh_values.npy
, and figshare_upload/psd_shallow_eeg-gcnn/standard_1010.tsv.txt
. Put them in the repo. wget https://ndownloader.figshare.com/files/25518170
python main.py
shallow_EEGGraphConvNet.py
. To use deep_EEGGraphConvNet.py
, run:python main.py --model deep
DGL | AUC | Bal. Accuracy |
---|---|---|
Shallow EEG-GCNN | 0.832 | 0.750 |
Deep EEG-GCNN | 0.830 | 0.736 |
Shallow_EEGGraphConvNet | AUC | Bal.Accuracy |
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Deep_EEGGraphConvNet | AUC | Bal.Accuracy |
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Wagh, N. & Varatharajah, Y.. (2020). EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network. Proceedings of the Machine Learning for Health NeurIPS Workshop, in PMLR 136:367-378 Available from http://proceedings.mlr.press/v136/wagh20a.html.
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Python C++ Jupyter Notebook Cuda Text other
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