README.md

---
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) .

> **Architecture-only repository.** This repo documents the
> `braindecode.models.FBCNet` class. **No pretrained weights are
> distributed here** — instantiate the model and train it on your own
> data, or fine-tune from a published foundation-model checkpoint
> separately.

## 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 example defaults — adjust them
to match your recording.

## Documentation

- Full API reference (parameters, references, architecture figure):
  <https://braindecode.org/stable/generated/braindecode.models.FBCNet.html>
- Interactive browser with live instantiation:
  <https://huggingface.co/spaces/braindecode/model-explorer>
- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/fbcnet.py#L31>

## Architecture description

The block below is the rendered class docstring (parameters,
references, architecture figure where available).

<div class='bd-doc'><main>
<p>FBCNet from Mane, R et al (2021) [fbcnet2021]_.</p>
<span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#5cb85c;color:white;font-size:11px;font-weight:600;margin-right:4px;">Convolution</span><span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#0072B2;color:white;font-size:11px;font-weight:600;margin-right:4px;">Filterbank</span>



 .. figure:: https://raw.githubusercontent.com/ravikiran-mane/FBCNet/refs/heads/master/FBCNet-V2.png
     :align: center
     :alt: FBCNet Architecture

 The FBCNet model applies spatial convolution and variance calculation along
 the time axis, inspired by the Filter Bank Common Spatial Pattern (FBCSP)
 algorithm.

 Notes
 -----
 This implementation is not guaranteed to be correct and has not been checked
 by the original authors; it has only been reimplemented from the paper
 description and source code [fbcnetcode2021]_. There is a difference in the
 activation function; in the paper, the ELU is used as the activation function,
 but in the original code, SiLU is used. We followed the code.

 Parameters
 ----------
 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
 ----------
 .. [fbcnet2021] 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.
 .. [fbcnetcode2021] Link to source-code:
     https://github.com/ravikiran-mane/FBCNet

 .. rubric:: Hugging Face Hub integration

 When the optional ``huggingface_hub`` package is installed, all models
 automatically gain the ability to be pushed to and loaded from the
 Hugging Face Hub. Install with::

     pip install braindecode[hub]

 **Pushing a model to the Hub:**

 .. code::
     from braindecode.models import FBCNet

     # Train your model
     model = FBCNet(n_chans=22, n_outputs=4, n_times=1000)
     # ... training code ...

     # Push to the Hub
     model.push_to_hub(
         repo_id="username/my-fbcnet-model",
         commit_message="Initial model upload",
     )

 **Loading a model from the Hub:**

 .. code::
     from braindecode.models import FBCNet

     # Load pretrained model
     model = FBCNet.from_pretrained("username/my-fbcnet-model")

     # Load with a different number of outputs (head is rebuilt automatically)
     model = FBCNet.from_pretrained("username/my-fbcnet-model", n_outputs=4)

 **Extracting features and replacing the head:**

 .. code::
     import torch

     x = torch.randn(1, model.n_chans, model.n_times)
     # Extract encoder features (consistent dict across all models)
     out = model(x, return_features=True)
     features = out["features"]

     # Replace the classification head
     model.reset_head(n_outputs=10)

 **Saving and restoring full configuration:**

 .. code::
     import json

     config = model.get_config()            # all __init__ params
     with open("config.json", "w") as f:
         json.dump(config, f)

     model2 = FBCNet.from_config(config)    # reconstruct (no weights)

 All model parameters (both EEG-specific and model-specific such as
 dropout rates, activation functions, number of filters) are automatically
 saved to the Hub and restored when loading.

 See :ref:`load-pretrained-models` for a complete tutorial.</main>
</div>

## Citation

Please cite both the original paper for this architecture (see the
*References* section 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.