Add architecture-only model card

README.md

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+---
+license: bsd-3-clause
+library_name: braindecode
+pipeline_tag: feature-extraction
+tags:
+  - eeg
+  - biosignal
+  - pytorch
+  - neuroscience
+  - braindecode
+  - convolutional
+---
+
+# EEGNeX
+
+EEGNeX model from Chen et al (2024) .
+
+> **Architecture-only repository.** This repo 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.
+
+## Quick start
+
+```bash
+pip install braindecode
+```
+
+```python
+from braindecode.models import EEGNeX
+
+model = EEGNeX(
+    n_chans=22,
+    sfreq=250,
+    input_window_seconds=4.0,
+    n_outputs=4,
+)
+```
+
+The signal-shape arguments above are example defaults — adjust them
+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:
+  <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
+
+     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 = EEGNeX.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:
+
+```bibtex
+@article{aristimunha2025braindecode,
+  title   = {Braindecode: a deep learning library for raw electrophysiological data},
+  author  = {Aristimunha, Bruno and others},
+  journal = {Zenodo},
+  year    = {2025},
+  doi     = {10.5281/zenodo.17699192},
+}
+```
+
+## License
+
+BSD-3-Clause for the model code (matching braindecode).
+Pretraining-derived weights, if you fine-tune from a checkpoint,
+inherit the licence of that checkpoint and its training corpus.