Replace with clean markdown card

README.md

@@ -9,18 +9,16 @@ tags:
   - neuroscience
   - braindecode
   - convolutional
-  - transformer
 ---
 
 # SCCNet
 
-SCCNet from Wei, C S (2019) .
+SCCNet from Wei, C S (2019) [sccnet].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.SCCNet` 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
 
@@ -39,218 +37,40 @@ model = SCCNet(
 )
 ```
 
-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.SCCNet.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.SCCNet.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/sccnet.py#L17>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>SCCNet from Wei, C S (2019) [sccnet]_.</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>
-
-
-
- Spatial component-wise convolutional network (SCCNet) for motor-imagery EEG
- classification.
-
- .. figure:: https://dt5vp8kor0orz.cloudfront.net/6e3ec5d729cd51fe8acc5a978db27d02a5df9e05/2-Figure1-1.png
-    :align: center
-    :alt:  Spatial component-wise convolutional network
-    :width: 680px
-
- .. rubric:: Architectural Overview
-
- SCCNet is a spatial-first convolutional layer that fixes temporal kernels in seconds
- to make its filters correspond to neurophysiologically aligned windows. The model
- comprises four stages:
-
- 1. **Spatial Component Analysis**: Performs convolution spatial filtering
-     across all EEG channels to extract spatial components, effectively
-     reducing the channel dimension.
- 2. **Spatio-Temporal Filtering**: Applies convolution across the spatial
-     components and temporal domain to capture spatio-temporal patterns.
- 3. **Temporal Smoothing (Pooling)**: Uses average pooling over time to smooth the
-    features and reduce the temporal dimension, focusing on longer-term patterns.
- 4. **Classification**: Flattens the features and applies a fully connected
-    layer.
-
- .. rubric:: Macro Components
-
- - `SCCNet.spatial_conv` **(spatial component analysis)**
-
-     - *Operations.*
-     - :class:`~torch.nn.Conv2d` with kernel `(n_chans, N_t)` and stride `(1, 1)` on an input reshaped to `(B, 1, n_chans, T)`; typical choice `N_t=1` yields a pure across-channel projection (montage-wide linear spatial filter).
-     - Zero padding to preserve time, :class:`~torch.nn.BatchNorm2d`; output has `N_u` component signals shaped `(B, 1, N_u, T)` after a permute step.
-
- *Interpretability/robustness.* Mimics CSP-like spatial filtering: each learned filter is a channel-weighted component, easing inspection and reducing channel noise.
-
- - `SCCNet.spatial_filt_conv` **(spatio-temporal filtering)**
-
-     - *Operations.*
-     - :class:`~torch.nn.Conv2d` with kernel `(N_u, 12)` over components and time (12 samples ~ 0.1 s at 125 Hz),
-     - :class:`~torch.nn.BatchNorm2d`;
-     - Nonlinearity is **power-like**: the original paper uses **square** like :class:`~braindecode.models.ShallowFBCSPNet` with the class :class:`~braindecode.modules.LogActivation` as default.
-     - :class:`~torch.nn.Dropout` with rate `p=0.5`.
-
- - *Role.* Learns frequency-selective energy features and inter-component interactions within a 0.1 s context (beta/alpha cycle scale).
-
- - `SCCNet.temporal_smoothing` **(aggregation + readout)**
-
-     - *Operations.*
-     - :class:`~torch.nn.AvgPool2d` with size `(1, 62)` (~ 0.5 s) for temporal smoothing and downsampling
-     - :class:`~torch.nn.Flatten`
-     - :class:`~torch.nn.Linear` to `n_outputs`.
-
-
- .. rubric:: Convolutional Details
-
- * **Temporal (where time-domain patterns are learned).**
-     The second block's kernel length is fixed to 12 samples (≈ 100 ms) and slides with
-     stride 1; average pooling `(1, 62)` (≈ 500 ms) integrates power over longer spans.
-     These choices bake in short-cycle detection followed by half-second trend smoothing.
-
- * **Spatial (how electrodes are processed).**
-     The first block's kernel spans **all electrodes** `(n_chans, N_t)`. With `N_t=1`,
-     it reduces to a montage-wide linear projection, mapping channels → `N_u` components.
-     The second block mixes **across components** via kernel height `N_u`.
-
- * **Spectral (how frequency information is captured).**
-     No explicit transform is used; learned **temporal kernels** serve as bandpass-like
-     filters, and the **square/log power** nonlinearity plus 0.5 s averaging approximate
-     band-power estimation (ERD/ERS-style features).
-
- .. rubric:: Attention / Sequential Modules
-
- This model contains **no attention** and **no recurrent units**.
-
- .. rubric:: Additional Mechanisms
-
- - :class:`~torch.nn.BatchNorm2d` and zero-padding are applied to both convolutions;
-   L2 weight decay was used in the original paper; dropout `p=0.5` combats overfitting.
- - Contrasting with other compact neural network, in EEGNet performs a temporal depthwise conv
-   followed by a **depthwise spatial** conv (separable), learning temporal filters first.
-   SCCNet inverts this order: it performs a **full spatial projection first** (CSP-like),
-   then a short **spatio-temporal** conv with an explicit 0.1 s kernel, followed by
-   **power-like** nonlinearity and longer temporal averaging. EEGNet's ELU and
-   separable design favor parameter efficiency; SCCNet's second-scale kernels and
-   square/log emphasize interpretable **band-power** features.
-
- - Reference implementation: see [sccnetcode]_.
-
- .. rubric:: Usage and Configuration
-
- * **Training from the original authors.**
-
- * Match window length so that `T` is comfortably larger than pooling length
-     (e.g., > 1.5-2 s for MI).
- * Start with standard MI augmentations (channel dropout/shuffle, time reverse)
-     and tune `n_spatial_filters` before deeper changes.
-
- Parameters
- ----------
- n_spatial_filters : int, optional
-     Number of spatial filters in the first convolutional layer, variable `N_u` from the
-     original paper. Default is 22.
- n_spatial_filters_smooth : int, optional
-     Number of spatial filters used as filter in the second convolutional
-     layer. Default is 20.
- drop_prob : float, optional
-     Dropout probability. Default is 0.5.
- activation : nn.Module, optional
-     Activation function after the second convolutional layer. Default is
-     logarithm activation.
-
- References
- ----------
- .. [sccnet] Wei, C. S., Koike-Akino, T., & Wang, Y. (2019, March). Spatial
-     component-wise convolutional network (SCCNet) for motor-imagery EEG
-     classification. In 2019 9th International IEEE/EMBS Conference on
-     Neural Engineering (NER) (pp. 328-331). IEEE.
- .. [sccnetcode] Hsieh, C. Y., Chou, J. L., Chang, Y. H., & Wei, C. S.
-     XBrainLab: An Open-Source Software for Explainable Artificial
-     Intelligence-Based EEG Analysis. In NeurIPS 2023 AI for
-     Science Workshop.
-
- .. 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 SCCNet
-
-     # Train your model
-     model = SCCNet(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-sccnet-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import SCCNet
-
-     # Load pretrained model
-     model = SCCNet.from_pretrained("username/my-sccnet-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = SCCNet.from_pretrained("username/my-sccnet-model", n_outputs=4)
-
- **Extracting features and replacing the head:**
 
- .. code::
-     import torch
+## Architecture
 
-     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"]
+![SCCNet architecture](https://dt5vp8kor0orz.cloudfront.net/6e3ec5d729cd51fe8acc5a978db27d02a5df9e05/2-Figure1-1.png)
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## Parameters
 
- .. code::
-     import json
+| Parameter | Type | Description |
+|---|---|---|
+| `n_spatial_filters` | int, optional | Number of spatial filters in the first convolutional layer, variable `N_u` from the original paper. Default is 22. |
+| `n_spatial_filters_smooth` | int, optional | Number of spatial filters used as filter in the second convolutional layer. Default is 20. |
+| `drop_prob` | float, optional | Dropout probability. Default is 0.5. |
+| `activation` | nn.Module, optional | Activation function after the second convolutional layer. Default is logarithm activation. |
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
 
-     model2 = SCCNet.from_config(config)    # reconstruct (no weights)
+## References
 
- 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.
+1. Wei, C. S., Koike-Akino, T., & Wang, Y. (2019, March). Spatial component-wise convolutional network (SCCNet) for motor-imagery EEG classification. In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 328-331). IEEE.
+2. Hsieh, C. Y., Chou, J. L., Chang, Y. H., & Wei, C. S. XBrainLab: An Open-Source Software for Explainable Artificial Intelligence-Based EEG Analysis. In NeurIPS 2023 AI for Science Workshop.
 
- 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,