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license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
- eeg
- biosignal
- pytorch
- neuroscience
- braindecode
- convolutional
---
# FBCNet
FBCNet from Mane, R et al (2021) [fbcnet2021].
> **Architecture-only repository.** Documents the
> `braindecode.models.FBCNet` 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 FBCNet
model = FBCNet(
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.FBCNet.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/fbcnet.py#L31>
## Architecture
## Parameters
| Parameter | Type | Description |
|---|---|---|
| `n_bands` | int or None or list[tuple[int, int]]], default=9 | Number of frequency bands. Could |
| `n_filters_spat` | int, default=32 | Number of spatial filters for the first convolution. |
| `n_dim: int, default=3` | — | Number of dimensions for the temporal reductor |
| `temporal_layer` | str, default='LogVarLayer' | Type of temporal aggregator layer. Options: 'VarLayer', 'StdLayer', 'LogVarLayer', 'MeanLayer', 'MaxLayer'. |
| `stride_factor` | int, default=4 | Stride factor for reshaping. |
| `activation` | nn.Module, default=nn.SiLU | Activation function class to apply in Spatial Convolution Block. |
| `cnn_max_norm` | float, default=2.0 | Maximum norm for the spatial convolution layer. |
| `linear_max_norm` | float, default=0.5 | Maximum norm for the final linear layer. |
| `filter_parameters: dict, default None` | — | Dictionary of parameters to use for the FilterBankLayer. If None, a default Chebyshev Type II filter with transition bandwidth of 2 Hz and stop-band ripple of 30 dB will be used. |
## References
1. Mane, R., Chew, E., Chua, K., Ang, K. K., Robinson, N., Vinod, A. P., ... & Guan, C. (2021). FBCNet: A multi-view convolutional neural network for brain-computer interface. preprint arXiv:2104.01233.
2. Link to source-code: https://github.com/ravikiran-mane/FBCNet
## 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.
|