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license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
- eeg
- biosignal
- pytorch
- neuroscience
- braindecode
- convolutional
---
# ShallowFBCSPNet
Shallow ConvNet model from Schirrmeister et al (2017) [Schirrmeister2017].
> **Architecture-only repository.** Documents the
> `braindecode.models.ShallowFBCSPNet` 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 ShallowFBCSPNet
model = ShallowFBCSPNet(
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.ShallowFBCSPNet.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/shallow_fbcsp.py#L24>
## Architecture
## Parameters
| Parameter | Type | Description |
|---|---|---|
| `n_filters_time: int` | — | Number of temporal filters. |
| `filter_time_length: int` | — | Length of the temporal filter. |
| `n_filters_spat: int` | — | Number of spatial filters. |
| `pool_time_length: int` | — | Length of temporal pooling filter. |
| `pool_time_stride: int` | — | Length of stride between temporal pooling filters. |
| `final_conv_length: int | str` | — | Length of the final convolution layer. If set to "auto", length of the input signal must be specified. |
| `conv_nonlin: type[nn.Module] | Callable` | — | Non-linear module class to be used after convolution layers. For backward compatibility, callables are also accepted and wrapped with :class:`~braindecode.modules.Expression`. |
| `pool_mode: str` | — | Method to use on pooling layers. "max" or "mean". |
| `activation_pool_nonlin: type[nn.Module]` | — | Non-linear module class to be used after pooling layers. |
| `split_first_layer: bool` | — | Split first layer into temporal and spatial layers (True) or just use temporal (False). There would be no non-linearity between the split layers. |
| `batch_norm: bool` | — | Whether to use batch normalisation. |
| `batch_norm_alpha: float` | — | Momentum for BatchNorm2d. |
| `drop_prob: float` | — | Dropout probability. |
## References
1. Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730
## 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.
|