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

# SleepStagerBlanco2020

Sleep staging architecture from Blanco et al (2020) from [Blanco2020]

> **Architecture-only repository.** Documents the
> `braindecode.models.SleepStagerBlanco2020` 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 SleepStagerBlanco2020

model = SleepStagerBlanco2020(
    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.SleepStagerBlanco2020.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_blanco_2020.py#L11>


## Architecture

![SleepStagerBlanco2020 architecture](https://media.springernature.com/full/springer-static/image/art%3A10.1007%2Fs00500-019-04174-1/MediaObjects/500_2019_4174_Fig2_HTML.png)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `n_conv_chans` | int | Number of convolutional channels. Set to 20 in [Blanco2020]. |
| `n_groups` | int | Number of groups for the convolution. Set to 2 in [Blanco2020] for 2 Channel EEG. controls the connections between inputs and outputs. n_channels and n_conv_chans must be divisible by n_groups. |
| `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`. |
| `n_classes` | int | Alias for `n_outputs`. |
| `input_size_s` | float | Alias for `input_window_seconds`. |
| `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. Fernandez-Blanco, E., Rivero, D. & Pazos, A. Convolutional neural networks for sleep stage scoring on a two-channel EEG signal. Soft Comput 24, 4067–4079 (2020). https://doi.org/10.1007/s00500-019-04174-1


## 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.