README.md

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

# FBMSNet

FBMSNet from Liu et al (2022) [fbmsnet].

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

model = FBMSNet(
    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.FBMSNet.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/fbmsnet.py#L19>


## Architecture

![FBMSNet architecture](https://raw.githubusercontent.com/Want2Vanish/FBMSNet/refs/heads/main/FBMSNet.png)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `n_bands` | int, default=9 | Number of input channels (e.g., number of frequency bands). |
| `n_filters_spat` | int, default=36 | Number of output channels from the MixedConv2d layer. |
| `temporal_layer` | str, default='LogVarLayer' | Temporal aggregation layer to use. |
| `n_dim: int, default=3` | — | Dimension of the temporal reduction layer. |
| `stride_factor` | int, default=4 | Stride factor for temporal segmentation. |
| `dilatability` | int, default=8 | Expansion factor for the spatial convolution block. |
| `activation` | nn.Module, default=nn.SiLU | Activation function class to apply. |
| `kernels_weights` | Sequence[int], default=(15, 31, 63, 125) | Kernel sizes for the MixedConv2d layer. |
| `cnn_max_norm` | float, default=2 | Maximum norm constraint for the convolutional layers. |
| `linear_max_norm` | float, default=0.5 | Maximum norm constraint for the linear layers. |
| `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. |
| `verbose: bool, default False` | — | Verbose parameter to create the filter using mne. |


## References

1. Liu, K., Yang, M., Yu, Z., Wang, G., & Wu, W. (2022). FBMSNet: A filter-bank multi-scale convolutional neural network for EEG-based motor imagery decoding. IEEE Transactions on Biomedical Engineering, 70(2), 436-445.
2. Liu, K., Yang, M., Yu, Z., Wang, G., & Wu, W. (2022). FBMSNet: A filter-bank multi-scale convolutional neural network for EEG-based motor imagery decoding. https://github.com/Want2Vanish/FBMSNet


## 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.