README.md

---
license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
  - eeg
  - biosignal
  - pytorch
  - neuroscience
  - braindecode
  - convolutional
  - sleep-staging
---

# ContraWR

Contrast with the World Representation ContraWR from Yang et al (2021) [Yang2021].

> **Architecture-only repository.** Documents the
> `braindecode.models.ContraWR` 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 ContraWR

model = ContraWR(
    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.ContraWR.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/contrawr.py#L10>



## Parameters

| Parameter | Type | Description |
|---|---|---|
| `steps` | int, optional | Number of steps to take the frequency decomposition `hop_length` parameters by default 20. |
| `emb_size` | int, optional | Embedding size for the final layer, by default 256. |
| `res_channels` | list[int], optional | Number of channels for each residual block, by default [32, 64, 128]. |
| `activation: nn.Module, default=nn.ELU` | — | Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.ELU`. |
| `drop_prob` | float, default=0.5 | The dropout rate for regularization. Values should be between 0 and 1. |
| `.. versionadded:: 0.9` | — | — |


## References

1. Yang, C., Xiao, C., Westover, M. B., & Sun, J. (2023). Self-supervised electroencephalogram representation learning for automatic sleep staging: model development and evaluation study. JMIR AI, 2(1), e46769.
2. Yang, C., Westover, M.B. and Sun, J., 2023. BIOT Biosignal Transformer for Cross-data Learning in the Wild. GitHub https://github.com/ycq091044/BIOT (accessed 2024-02-13)


## 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.