Add architecture-only model card

README.md

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+---
+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) .
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.FBMSNet` 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 FBMSNet
+
+model = FBMSNet(
+    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.FBMSNet.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/fbmsnet.py#L19>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>FBMSNet from Liu et al (2022) [fbmsnet]_.</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/Want2Vanish/FBMSNet/refs/heads/main/FBMSNet.png
+     :align: center
+     :alt: FBMSNet Architecture
+
+ 0. **FilterBank Layer**: Applying filterbank to transform the input.
+
+ 1. **Temporal Convolution Block**: Utilizes mixed depthwise convolution
+    (MixConv) to extract multiscale temporal features from multiview EEG
+    representations. The input is split into groups corresponding to different
+    views each convolved with kernels of varying sizes.
+    Kernel sizes are set relative to the EEG
+    sampling rate, with ratio coefficients [0.5, 0.25, 0.125, 0.0625],
+    dividing the input into four groups.
+
+ 2. **Spatial Convolution Block**: Applies depthwise convolution with a kernel
+    size of (n_chans, 1) to span all EEG channels, effectively learning spatial
+    filters. This is followed by batch normalization and the Swish activation
+    function. A maximum norm constraint of 2 is imposed on the convolution
+    weights to regularize the model.
+
+ 3. **Temporal Log-Variance Block**: Computes the log-variance.
+
+ 4. **Classification Layer**: A fully connected with weight constraint.
+
+ 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 [fbmsnetcode]_. There is an extra layer here to
+ compute the filterbank during bash time and not on data time. This avoids
+ data-leak, and allows the model to follow the braindecode convention.
+
+ Parameters
+ ----------
+ 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
+ ----------
+ .. [fbmsnet] 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.
+ .. [fbmsnetcode] 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
+
+ .. 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 FBMSNet
+
+     # Train your model
+     model = FBMSNet(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-fbmsnet-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import FBMSNet
+
+     # Load pretrained model
+     model = FBMSNet.from_pretrained("username/my-fbmsnet-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = FBMSNet.from_pretrained("username/my-fbmsnet-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 = FBMSNet.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.