---
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

![SleepStagerChambon2018 architecture](https://braindecode.org/dev/_static/model/SleepStagerChambon2018.jpg)


## 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.