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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.
|