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
+  - transformer
+---
+
+# FBLightConvNet
+
+LightConvNet from Ma, X et al (2023) .
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.FBLightConvNet` 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 FBLightConvNet
+
+model = FBLightConvNet(
+    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.FBLightConvNet.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/fblightconvnet.py#L18>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>LightConvNet from Ma, X et al (2023) [lightconvnet]_.</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/Ma-Xinzhi/LightConvNet/refs/heads/main/network_architecture.png
+     :align: center
+     :alt: LightConvNet Neural Network
+
+ A lightweight convolutional neural network incorporating temporal
+ dependency learning and attention mechanisms. The architecture is
+ designed to efficiently capture spatial and temporal features through
+ specialized convolutional layers and **multi-head attention**.
+
+ The network architecture consists of four main modules:
+
+ 1. **Spatial and Spectral Information Learning**:
+     Applies filterbank and spatial convolutions.
+     This module is followed by batch normalization and
+     an activation function to enhance feature representation.
+
+ 2. **Temporal Segmentation and Feature Extraction**:
+     Divides the processed data into non-overlapping temporal windows.
+     Within each window, a variance-based layer extracts discriminative features,
+     which are then log-transformed to stabilize variance before being
+     passed to the attention module.
+
+ 3. **Temporal Attention Module**: Utilizes a multi-head attention
+     mechanism with depthwise separable convolutions to capture dependencies
+     across different temporal segments. The attention weights are normalized
+     using softmax and aggregated to form a comprehensive temporal
+     representation.
+
+ 4. **Final Layer**: Flattens the aggregated features and passes them
+     through a linear layer to with kernel sizes matching the input
+     dimensions to integrate features across different channels generate the
+     final output predictions.
+
+ Notes
+ -----
+ This implementation is not guaranteed to be correct and has not been checked
+ by the original authors; it is a braindecode adaptation from the Pytorch
+ source-code [lightconvnetcode]_.
+
+ Parameters
+ ----------
+ n_bands : int or None or list of tuple of int, default=8
+     Number of frequency bands or a list of frequency band tuples. If a list of tuples is provided,
+     each tuple defines the lower and upper bounds of a frequency band.
+ n_filters_spat : int, default=32
+     Number of spatial filters in the depthwise convolutional layer.
+ n_dim : int, default=3
+     Number of dimensions for the temporal reduction layer.
+ stride_factor : int, default=4
+     Stride factor used for reshaping the temporal dimension.
+ activation : nn.Module, default=nn.ELU
+     Activation function class to apply after convolutional layers.
+ verbose : bool, default=False
+     If True, enables verbose output during filter creation using mne.
+ filter_parameters : dict, default={}
+     Additional parameters for the FilterBankLayer.
+ heads : int, default=8
+     Number of attention heads in the multi-head attention mechanism.
+ weight_softmax : bool, default=True
+     If True, applies softmax to the attention weights.
+ bias : bool, default=False
+     If True, includes a bias term in the convolutional layers.
+
+ References
+ ----------
+ .. [lightconvnet] Ma, X., Chen, W., Pei, Z., Liu, J., Huang, B., & Chen, J.
+     (2023). A temporal dependency learning CNN with attention mechanism
+     for MI-EEG decoding. IEEE Transactions on Neural Systems and
+     Rehabilitation Engineering.
+ .. [lightconvnetcode] Link to source-code:
+     https://github.com/Ma-Xinzhi/LightConvNet
+
+ .. 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 FBLightConvNet
+
+     # Train your model
+     model = FBLightConvNet(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-fblightconvnet-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import FBLightConvNet
+
+     # Load pretrained model
+     model = FBLightConvNet.from_pretrained("username/my-fblightconvnet-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = FBLightConvNet.from_pretrained("username/my-fblightconvnet-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 = FBLightConvNet.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.