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README.md

@@ -13,13 +13,12 @@ tags:
 
 # EEGNeX
 
-EEGNeX model from Chen et al (2024) .
+EEGNeX model from Chen et al (2024) [eegnex].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.EEGNeX` 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
 
@@ -38,246 +37,47 @@ model = EEGNeX(
 )
 ```
 
-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.EEGNeX.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.EEGNeX.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/eegnex.py#L16>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>EEGNeX model from Chen et al (2024) [eegnex]_.</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>
-
-
-
- .. figure:: https://braindecode.org/dev/_static/model/eegnex.jpg
-     :align: center
-     :alt: EEGNeX Architecture
-     :width: 620px
-
- .. rubric:: Architectural Overview
-
- EEGNeX is a **purely convolutional** architecture that refines the EEGNet-style stem
- and deepens the temporal stack with **dilated temporal convolutions**. The end-to-end
- flow is:
-
- - (i) **Block-1/2**: two temporal convolutions ``(1 x L)`` with BN refine a
-   learned FIR-like *temporal filter bank* (no pooling yet);
- - (ii) **Block-3**: depthwise **spatial** convolution across electrodes
-   ``(n_chans x 1)`` with max-norm constraint, followed by ELU → AvgPool (time) → Dropout;
- - (iii) **Block-4/5**: two additional **temporal** convolutions with increasing **dilation**
-   to expand the receptive field; the last block applies ELU → AvgPool → Dropout → Flatten;
- - (iv) **Classifier**: a max-norm–constrained linear layer.
-
- The published work positions EEGNeX as a compact, conv-only alternative that consistently
- outperforms prior baselines across MOABB-style benchmarks, with the popular
- “EEGNeX-8,32” shorthand denoting *8 temporal filters* and *kernel length 32*.
-
-
- .. rubric:: Macro Components
-
- - **Block-1 / Block-2 — Temporal filter (learned).**
-
-    - *Operations.*
-    - :class:`torch.nn.Conv2d` with kernels ``(1, L)``
-    - :class:`torch.nn.BatchNorm2d` (no nonlinearity until Block-3, mirroring a linear FIR analysis stage).
-      These layers set up frequency-selective detectors before spatial mixing.
-
-    - *Interpretability.* Kernels can be inspected as FIR filters; two stacked temporal
-      convs allow longer effective kernels without parameter blow-up.
-
- - **Block-3 — Spatial projection + condensation.**
-
-     - *Operations.*
-     - :class:`braindecode.modules.Conv2dWithConstraint` with kernel``(n_chans, 1)``
-       and ``groups = filter_2`` (depthwise across filters)
-     - :class:`torch.nn.BatchNorm2d`
-     - :class:`torch.nn.ELU`
-     - :class:`torch.nn.AvgPool2d` (time)
-     - :class:`torch.nn.Dropout`.
-
- **Role**: Learns per-filter spatial patterns over the **full montage** while temporal
-   pooling stabilizes and compresses features; max-norm encourages well-behaved spatial
-   weights similar to EEGNet practice.
-
- - **Block-4 / Block-5 — Dilated temporal integration.**
-
-     - *Operations.*
-     - :class:`torch.nn.Conv2d` with kernels ``(1, k)`` and **dilations**
-       (e.g., 2 then 4);
-     - :class:`torch.nn.BatchNorm2d`
-     - :class:`torch.nn.ELU`
-     - :class:`torch.nn.AvgPool2d` (time)
-     - :class:`torch.nn.Dropout`
-     - :class:`torch.nn.Flatten`.
-
- **Role**: Expands the temporal receptive field efficiently to capture rhythms and
- long-range context after condensation.
-
- - **Final Classifier — Max-norm linear.**
-
-     - *Operations.*
-     - :class:`braindecode.modules.LinearWithConstraint` maps the flattened
-       vector to the target classes; the max-norm constraint regularizes the readout.
-
-
- .. rubric:: Convolutional Details
-
- - **Temporal (where time-domain patterns are learned).**
-   Blocks 1-2 learn the primary filter bank (oscillations/transients), while Blocks 4-5
-   use **dilation** to integrate over longer horizons without extra pooling. The final
-   AvgPool in Block-5 sets the output token rate and helps noise suppression.
-
- - **Spatial (how electrodes are processed).**
-   A *single* depthwise spatial conv (Block-3) spans the entire electrode set
-   (kernel ``(n_chans, 1)``), producing per-temporal-filter topographies; no cross-filter
-   mixing occurs at this stage, aiding interpretability.
-
- - **Spectral (how frequency content is captured).**
-   Frequency selectivity emerges from the learned temporal kernels; dilation broadens effective
-   bandwidth coverage by composing multiple scales.
-
- .. rubric:: Additional Mechanisms
-
- - **EEGNeX-8,32 naming.** “8,32” indicates *8 temporal filters* and *kernel length 32*,
-   reflecting the paper's ablation path from EEGNet-8,2 toward thicker temporal kernels
-   and a deeper conv stack.
- - **Max-norm constraints.** Spatial (Block-3) and final linear layers use max-norm
-   regularization—standard in EEG CNNs—to reduce overfitting and encourage stable spatial
-   patterns.
-
- .. rubric:: Usage and Configuration
-
- - **Kernel schedule.** Start with the canonical **EEGNeX-8,32** (``filter_1=8``,
-   ``kernel_block_1_2=32``) and keep **Block-3** depth multiplier modest (e.g., 2) to match
-   the paper's “pure conv” profile.
- - **Pooling vs. dilation.** Use pooling in Blocks 3 and 5 to control compute and variance;
-   increase dilations (Blocks 4-5) to widen temporal context when windows are short.
- - **Regularization.** Combine dropout (Blocks 3 & 5) with max-norm on spatial and
-   classifier layers; prefer ELU activations for stable training on small EEG datasets.
-
-
- - The braindecode implementation follows the paper's conv-only design with five blocks
-   and reproduces the depthwise spatial step and dilated temporal stack. See the class
-   reference for exact kernel sizes, dilations, and pooling defaults. You can check the
-   original implementation at [EEGNexCode]_.
-
- .. versionadded:: 1.1
-
-
- Parameters
- ----------
- activation : nn.Module, optional
-     Activation function to use. Default is `nn.ELU`.
- depth_multiplier : int, optional
-     Depth multiplier for the depthwise convolution. Default is 2.
- filter_1 : int, optional
-     Number of filters in the first convolutional layer. Default is 8.
- filter_2 : int, optional
-     Number of filters in the second convolutional layer. Default is 32.
- drop_prob: float, optional
-     Dropout rate. Default is 0.5.
- kernel_block_4 : tuple[int, int], optional
-     Kernel size for block 4. Default is (1, 16).
- dilation_block_4 : tuple[int, int], optional
-     Dilation rate for block 4. Default is (1, 2).
- avg_pool_block4 : tuple[int, int], optional
-     Pooling size for block 4. Default is (1, 4).
- kernel_block_5 : tuple[int, int], optional
-     Kernel size for block 5. Default is (1, 16).
- dilation_block_5 : tuple[int, int], optional
-     Dilation rate for block 5. Default is (1, 4).
- avg_pool_block5 : tuple[int, int], optional
-     Pooling size for block 5. Default is (1, 8).
-
- References
- ----------
- .. [eegnex] Chen, X., Teng, X., Chen, H., Pan, Y., & Geyer, P. (2024).
-    Toward reliable signals decoding for electroencephalogram: A benchmark
-    study to EEGNeX. Biomedical Signal Processing and Control, 87, 105475.
- .. [EEGNexCode] Chen, X., Teng, X., Chen, H., Pan, Y., & Geyer, P. (2024).
-    Toward reliable signals decoding for electroencephalogram: A benchmark
-    study to EEGNeX. https://github.com/chenxiachan/EEGNeX
-
- .. 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 EEGNeX
-
-     # Train your model
-     model = EEGNeX(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-eegnex-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import EEGNeX
-
-     # Load pretrained model
-     model = EEGNeX.from_pretrained("username/my-eegnex-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = EEGNeX.from_pretrained("username/my-eegnex-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"]
+![EEGNeX architecture](https://braindecode.org/dev/_static/model/eegnex.jpg)
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## Parameters
 
- .. code::
-     import json
+| Parameter | Type | Description |
+|---|---|---|
+| `activation` | nn.Module, optional | Activation function to use. Default is `nn.ELU`. |
+| `depth_multiplier` | int, optional | Depth multiplier for the depthwise convolution. Default is 2. |
+| `filter_1` | int, optional | Number of filters in the first convolutional layer. Default is 8. |
+| `filter_2` | int, optional | Number of filters in the second convolutional layer. Default is 32. |
+| `drop_prob: float, optional` | — | Dropout rate. Default is 0.5. |
+| `kernel_block_4` | tuple[int, int], optional | Kernel size for block 4. Default is (1, 16). |
+| `dilation_block_4` | tuple[int, int], optional | Dilation rate for block 4. Default is (1, 2). |
+| `avg_pool_block4` | tuple[int, int], optional | Pooling size for block 4. Default is (1, 4). |
+| `kernel_block_5` | tuple[int, int], optional | Kernel size for block 5. Default is (1, 16). |
+| `dilation_block_5` | tuple[int, int], optional | Dilation rate for block 5. Default is (1, 4). |
+| `avg_pool_block5` | tuple[int, int], optional | Pooling size for block 5. Default is (1, 8). |
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
 
-     model2 = EEGNeX.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. Chen, X., Teng, X., Chen, H., Pan, Y., & Geyer, P. (2024). Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX. Biomedical Signal Processing and Control, 87, 105475.
+2. Chen, X., Teng, X., Chen, H., Pan, Y., & Geyer, P. (2024). Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX. https://github.com/chenxiachan/EEGNeX
 
- 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,