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braindecode/DGCNN

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DGCNN

DGCNN for EEG classification from Song et al. (2018) [dgcnn].

Architecture-only repository. Documents the braindecode.models.DGCNN class. No pretrained weights are distributed here. Instantiate the model and train it on your own data.

Quick start

bash

pip install braindecode

python

from braindecode.models import DGCNN

model = DGCNN(
    n_chans=22,
    sfreq=250,
    input_window_seconds=4.0,
    n_outputs=4,
)

The signal-shape arguments above are illustrative defaults — adjust to match your recording.

Documentation

—Full API reference: <https://braindecode.org/stable/generated/braindecode.models.DGCNN.html>
—Interactive browser (live instantiation, parameter counts): <https://huggingface.co/spaces/braindecode/model-explorer>
—Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/dgcnn.py#L253>

Architecture

[image]

Parameters

Parameter	Type	Description
`chs_info`	list of dict, optional	Information about each channel, typically obtained from `mne.Info['chs']`. Each entry must contain a `'loc'` key with 3-D electrode positions so the initial adjacency matrix can be built from spatial proximity (Eq. 1). A montage must be set on the `mne.Info` object (see :meth:`mne.Info.set_montage`). If `None` or positions cannot be extracted, raised ValueError (see Notes).
`n_filters`	int, default=64	Number of spectral graph-convolutional filters. This is the output feature dimension per node produced by the Chebyshev graph convolution followed by the :math:`1 \times 1` convolution (see Fig. 2 in the paper). The original code uses 64.
`cheb_order`	int, default=2	Order :math:`K` of the Chebyshev polynomial approximation (Eq. 11).
`n_neighbors`	int, default=5	Number of spatial nearest neighbors per node used to build the initial adjacency matrix (Eq. 1).
`mlp_dims`	tuple[int, ...], default=(256,)	Hidden-layer sizes of the fully connected classification head.
`activation`	type[nn.Module], default=nn.ReLU	Activation function class used after the graph convolution and in the classification head.
`drop_prob`	float, default=0.5	Dropout probability in the classification head.

References

1.Song, T., Zheng, W., Song, P., & Cui, Z. (2018). EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Transactions on Affective Computing, 11(3), 532-541. https://doi.org/10.1109/TAFFC.2018.2817622

Citation

Cite the original architecture paper (see References above) and braindecode:

bibtex

@article{aristimunha2025braindecode,
  title   = {Braindecode: a deep learning library for raw electrophysiological data},
  author  = {Aristimunha, Bruno and others},
  journal = {Zenodo},
  year    = {2025},
  doi     = {10.5281/zenodo.17699192},
}

License

BSD-3-Clause for the model code (matching braindecode). Pretraining-derived weights, if you fine-tune from a checkpoint, inherit the licence of that checkpoint and its training corpus.