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README.md

@@ -13,13 +13,12 @@ tags:
 
 # FBCNet
 
-FBCNet from Mane, R et al (2021) .
+FBCNet from Mane, R et al (2021) [fbcnet2021].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** 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.
+> distributed here.** Instantiate the model and train it on your own
+> data.
 
 ## Quick start
 
@@ -38,145 +37,45 @@ model = FBCNet(
 )
 ```
 
-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.FBCNet.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.FBCNet.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/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
+## Architecture
 
-     # Load pretrained model
-     model = FBCNet.from_pretrained("username/my-fbcnet-model")
+![FBCNet architecture](https://raw.githubusercontent.com/ravikiran-mane/FBCNet/refs/heads/master/FBCNet-V2.png)
 
-     # 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:**
+## Parameters
 
- .. code::
-     import torch
+| Parameter | Type | Description |
+|---|---|---|
+| `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. |
 
-     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)
+## References
 
- **Saving and restoring full configuration:**
+1. 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.
+2. Link to source-code: https://github.com/ravikiran-mane/FBCNet
 
- .. 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:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,