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README.md

@@ -14,13 +14,12 @@ tags:
 
 # LUNA
 
-LUNA from Döner et al .
+LUNA from Döner et al [LUNA].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.LUNA` 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
 
@@ -39,179 +38,43 @@ model = LUNA(
 )
 ```
 
-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.LUNA.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.LUNA.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/luna.py#L30>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>LUNA from Döner et al [LUNA]_.</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:#d9534f;color:white;font-size:11px;font-weight:600;margin-right:4px;">Foundation Model</span>
-
-:bdg-dark-line:`Channel`
-
-   .. figure:: https://arxiv.org/html/2510.22257v1/x1.png
-       :align: center
-       :alt: LUNA Architecture.
-
-   LUNA is a topology-invariant EEG model that processes signals from varying
-   numbers of channels using a channel-unification mechanism with learned queries.
-
-   The architecture consists of:
-   1. Patch Feature Extraction (temporal CNN + FFT-based features)
-   2. Channel-Unification Module (cross-attention with learned queries)
-   3. Patch-wise Temporal Encoder (RoPE-based transformer)
-   4. Decoder Heads (classification or reconstruction)
-
-   .. important::
-      **Pre-trained Weights Available**
-
-      This model has pre-trained weights available on the Hugging Face Hub
-      at `PulpBio/LUNA <https://huggingface.co/PulpBio/LUNA>`_.
-
-      Available model variants:
-
-      - **LUNA_base.safetensors** - Base model (embed_dim=64, num_queries=4, depth=8)
-      - **LUNA_large.safetensors** - Large model (embed_dim=96, num_queries=6, depth=10)
-      - **LUNA_huge.safetensors** - Huge model (embed_dim=128, num_queries=8, depth=24)
-
-      Example loading for fine-tuning:
-
-      .. code:: python
-          from braindecode.models import LUNA
-
-          # Load pre-trained base model from Hugging Face Hub
-          model = LUNA.from_pretrained(
-              "PulpBio/LUNA",
-              filename="LUNA_base.safetensors",
-              n_outputs=2,
-              n_chans=22,
-              n_times=1000,
-              embed_dim=64,
-              num_queries=4,
-              depth=8,
-          )
-
-      To push your own trained model to the Hub:
-
-      .. code:: python
-          # After training your model
-          model.push_to_hub(
-              repo_id="username/my-luna-model", commit_message="Upload trained LUNA model"
-          )
-
-      Requires installing ``braindecode[hug]`` for Hub integration.
-
-   Parameters
-   ----------
-   patch_size : int
-       Number of time samples per patch. Default: 40.
-   num_queries : int
-       Number of learned queries for channel unification.
-       Paper uses: 4 (Base), 6 (Large), 8 (Huge). Default: 4.
-   embed_dim : int
-       Embedding dimension for patch features.
-       Paper uses: 64 (Base), 96 (Large), 128 (Huge). Default: 64.
-   depth : int
-       Number of transformer encoder blocks.
-       Paper uses: 8 (Base), 10 (Large), 24 (Huge). Default: 8.
-   num_heads : int
-       Number of attention heads in channel unification.
-       Default: 2.
-   mlp_ratio : float
-       Ratio of MLP hidden dimension to embedding dimension. Default: 4.0.
-   norm_layer : nn.Module
-       Normalization layer class. Default: nn.LayerNorm.
-   drop_path : float
-       Stochastic depth rate. Default: 0.0.
-
-   References
-   ----------
-   .. [LUNA] Döner, B., Ingolfsson, T. M., Benini, L., & Li, Y. (2025).
-       LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis.
-       The Thirty-Ninth Annual Conference on Neural Information Processing Systems - NeurIPS.
-       Retrieved from https://openreview.net/forum?id=uazfjnFL0G
-
-   .. 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 LUNA
-
-       # Train your model
-       model = LUNA(n_chans=22, n_outputs=4, n_times=1000)
-       # ... training code ...
-
-       # Push to the Hub
-       model.push_to_hub(
-           repo_id="username/my-luna-model",
-           commit_message="Initial model upload",
-       )
-
-   **Loading a model from the Hub:**
 
-   .. code::
-       from braindecode.models import LUNA
+## Architecture
 
-       # Load pretrained model
-       model = LUNA.from_pretrained("username/my-luna-model")
+![LUNA architecture](https://arxiv.org/html/2510.22257v1/x1.png)
 
-       # Load with a different number of outputs (head is rebuilt automatically)
-       model = LUNA.from_pretrained("username/my-luna-model", n_outputs=4)
 
-   **Extracting features and replacing the head:**
+## Parameters
 
-   .. code::
-       import torch
+| Parameter | Type | Description |
+|---|---|---|
+| `patch_size` | int | Number of time samples per patch. Default: 40. |
+| `num_queries` | int | Number of learned queries for channel unification. Paper uses: 4 (Base), 6 (Large), 8 (Huge). Default: 4. |
+| `embed_dim` | int | Embedding dimension for patch features. Paper uses: 64 (Base), 96 (Large), 128 (Huge). Default: 64. |
+| `depth` | int | Number of transformer encoder blocks. Paper uses: 8 (Base), 10 (Large), 24 (Huge). Default: 8. |
+| `num_heads` | int | Number of attention heads in channel unification. Default: 2. |
+| `mlp_ratio` | float | Ratio of MLP hidden dimension to embedding dimension. Default: 4.0. |
+| `norm_layer` | nn.Module | Normalization layer class. Default: nn.LayerNorm. |
+| `drop_path` | float | Stochastic depth rate. Default: 0.0. |
 
-       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 = LUNA.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. Döner, B., Ingolfsson, T. M., Benini, L., & Li, Y. (2025). LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis. The Thirty-Ninth Annual Conference on Neural Information Processing Systems - NeurIPS. Retrieved from https://openreview.net/forum?id=uazfjnFL0G
 
-   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,