Replace with clean markdown card

README.md

@@ -9,18 +9,16 @@ tags:
   - neuroscience
   - braindecode
   - convolutional
-  - transformer
 ---
 
 # FBLightConvNet
 
-LightConvNet from Ma, X et al (2023) .
+LightConvNet from Ma, X et al (2023) [lightconvnet].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** 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.
+> distributed here.** Instantiate the model and train it on your own
+> data.
 
 ## Quick start
 
@@ -39,169 +37,46 @@ model = FBLightConvNet(
 )
 ```
 
-The signal-shape arguments above are example defaults — adjust them
-to match your recording.
+The signal-shape arguments above are illustrative defaults — adjust 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:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.FBLightConvNet.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/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.
+## Architecture
 
- 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
+![FBLightConvNet architecture](https://raw.githubusercontent.com/Ma-Xinzhi/LightConvNet/refs/heads/main/network_architecture.png)
 
- .. 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::
+## Parameters
 
-     pip install braindecode[hub]
+| Parameter | Type | Description |
+|---|---|---|
+| `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. |
 
- **Pushing a model to the Hub:**
 
- .. code::
-     from braindecode.models import FBLightConvNet
+## References
 
-     # Train your model
-     model = FBLightConvNet(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
+1. 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.
+2. Link to source-code: https://github.com/Ma-Xinzhi/LightConvNet
 
-     # 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:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,