| --- |
| license: bsd-3-clause |
| library_name: braindecode |
| pipeline_tag: feature-extraction |
| tags: |
| - eeg |
| - biosignal |
| - pytorch |
| - neuroscience |
| - braindecode |
| - convolutional |
| - sleep-staging |
| --- |
| |
| # SleepStagerChambon2018 |
|
|
| Sleep staging architecture from Chambon et al. (2018) [Chambon2018]. |
|
|
| > **Architecture-only repository.** Documents the |
| > `braindecode.models.SleepStagerChambon2018` 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 SleepStagerChambon2018 |
| |
| model = SleepStagerChambon2018( |
| n_chans=2, |
| sfreq=100, |
| input_window_seconds=30.0, |
| n_outputs=5, |
| ) |
| ``` |
|
|
| The signal-shape arguments above are illustrative defaults — adjust to |
| match your recording. |
|
|
| ## Documentation |
| - Full API reference: <https://braindecode.org/stable/generated/braindecode.models.SleepStagerChambon2018.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/sleep_stager_chambon_2018.py#L13> |
|
|
|
|
| ## Architecture |
|
|
|  |
|
|
|
|
| ## Parameters |
|
|
| | Parameter | Type | Description | |
| |---|---|---| |
| | `n_conv_chs` | int | Number of convolutional channels. Set to 8 in [Chambon2018]. | |
| | `time_conv_size_s` | float | Size of filters in temporal convolution layers, in seconds. Set to 0.5 in [Chambon2018] (64 samples at sfreq=128). | |
| | `max_pool_size_s` | float | Max pooling size, in seconds. Set to 0.125 in [Chambon2018] (16 samples at sfreq=128). | |
| | `pad_size_s` | float | Padding size, in seconds. Set to 0.25 in [Chambon2018] (half the temporal convolution kernel size). | |
| | `drop_prob` | float | Dropout rate before the output dense layer. | |
| | `apply_batch_norm` | bool | If True, apply batch normalization after both temporal convolutional layers. | |
| | `return_feats` | bool | If True, return the features, i.e. the output of the feature extractor (before the final linear layer). If False, pass the features through the final linear layer. | |
| | `n_channels` | int | Alias for `n_chans`. | |
| | `input_size_s:` | — | Alias for `input_window_seconds`. | |
| | `n_classes:` | — | Alias for `n_outputs`. | |
| | `activation: nn.Module, default=nn.ReLU` | — | Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.ReLU`. | |
|
|
|
|
| ## References |
|
|
| 1. Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., & Gramfort, A. (2018). A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(4), 758-769. |
|
|
|
|
| ## 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. |
|
|
