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README.md

@@ -14,13 +14,12 @@ tags:
 
 # SignalJEPA_Contextual
 
-Contextual downstream architecture introduced in signal-JEPA Guetschel, P et al (2024) .
+Contextual downstream architecture introduced in signal-JEPA Guetschel, P et al (2024) [1].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.SignalJEPA_Contextual` 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
 
@@ -39,178 +38,36 @@ model = SignalJEPA_Contextual(
 )
 ```
 
-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.SignalJEPA_Contextual.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.SignalJEPA_Contextual.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/signal_jepa.py#L489>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>Contextual downstream architecture introduced in signal-JEPA Guetschel, P et al (2024) [1]_.</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>
-
-:bdg-dark-line:`Channel`<span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#d9534f;color:white;font-size:11px;font-weight:600;margin-right:4px;">Foundation Model</span>
-
-
-
- This architecture is one of the variants of :class:`SignalJEPA`
- that can be used for classification purposes.
-
- .. figure:: https://braindecode.org/dev/_static/model/sjepa_contextual.jpg
-     :align: center
-     :alt: sJEPA Contextual.
-
- .. versionadded:: 0.9
-
- .. rubric:: Pretrained Weights
-
- Loads the pre-trained SSL encoder (feature encoder + transformer +
- channel embedding) from the shared checkpoints. Two hub IDs are
- available:
-
- - ``braindecode/signal-jepa``: with pre-trained channel embeddings
-   (62 channels, ``channel_embedding='pretrain_aligned'``).
- - ``braindecode/signal-jepa_without-chans``: without channel embeddings.
-   The embedding table is re-initialized from your channel locations
-   (``channel_embedding='scratch'``, the default).
-
- .. important::
-    **Pre-trained Weights Available**
-
-    .. code:: python
-        from braindecode.models import SignalJEPA_Contextual
-
-        # Use pre-trained channel embeddings (subset of the 62 channels):
-        model = SignalJEPA_Contextual.from_pretrained(
-            "braindecode/signal-jepa",
-            n_times=2048,  # required: the SSL checkpoint is n_times-agnostic
-            n_outputs=4,
-            strict=False,  # classifier head is not in the SSL checkpoint
-        )
-
-        # Arbitrary channel set:
-        model = SignalJEPA_Contextual.from_pretrained(
-            "braindecode/signal-jepa_without-chans",
-            chs_info=[{"ch_name": "A", "loc": [...]}, ...],
-            n_times=2048,
-            n_outputs=4,
-            strict=False,
-        )
-
-    Requires installing ``braindecode[hub]`` for Hub integration.
-
- .. rubric:: Usage
-
- .. code:: python
-     from braindecode.models import SignalJEPA_Contextual
-
-     model = SignalJEPA_Contextual(
-         chs_info=[{"ch_name": "Fp1", "loc": [...]}, ...],
-         input_window_seconds=16.0,
-         sfreq=128,
-         n_outputs=4,  # e.g., 4-class classification
-     )
-
-     # Forward: (batch, n_chans, n_times) -> (batch, n_outputs)
-     output = model(eeg_data)
-
- .. warning::
-
-     Pre-trained at **128 Hz** on EEG bandpass-filtered between
-     **0.5 and 40 Hz** and rescaled by a factor of :math:`10^{6}`
-     (volts to microvolts). Apply the same preprocessing to your
-     data to match the pre-training distribution.
-
- Parameters
- ----------
- n_spat_filters : int
-     Number of spatial filters.
-
- References
- ----------
- .. [1] Guetschel, P., Moreau, T., & Tangermann, M. (2024).
-     S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention.
-     In 9th Graz Brain-Computer Interface Conference, https://www.doi.org/10.3217/978-3-99161-014-4-003
-
- .. 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 SignalJEPA_Contextual
-
-     # Train your model
-     model = SignalJEPA_Contextual(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-signaljepa_contextual-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import SignalJEPA_Contextual
-
-     # Load pretrained model
-     model = SignalJEPA_Contextual.from_pretrained("username/my-signaljepa_contextual-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = SignalJEPA_Contextual.from_pretrained("username/my-signaljepa_contextual-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"]
+![SignalJEPA_Contextual architecture](https://braindecode.org/dev/_static/model/sjepa_contextual.jpg)
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## Parameters
 
- .. code::
-     import json
+| Parameter | Type | Description |
+|---|---|---|
+| `n_spat_filters` | int | Number of spatial filters. |
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
 
-     model2 = SignalJEPA_Contextual.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. Guetschel, P., Moreau, T., & Tangermann, M. (2024). S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention. In 9th Graz Brain-Computer Interface Conference, https://www.doi.org/10.3217/978-3-99161-014-4-003
 
- 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,