| --- |
| license: bsd-3-clause |
| library_name: braindecode |
| pipeline_tag: feature-extraction |
| tags: |
| - eeg |
| - biosignal |
| - pytorch |
| - neuroscience |
| - braindecode |
|
|
| --- |
| |
| # 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 |
|
|
|  |
|
|
|
|
| ## 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. |
|
|
