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README.md

@@ -13,13 +13,12 @@ tags:
 
 # EEGMiner
 
-EEGMiner from Ludwig et al (2024) .
+EEGMiner from Ludwig et al (2024) [eegminer].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.EEGMiner` 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
 
@@ -38,174 +37,42 @@ model = EEGMiner(
 )
 ```
 
-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.EEGMiner.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.EEGMiner.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/eegminer.py#L21>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>EEGMiner from Ludwig et al (2024) [eegminer]_.</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:#E69F00;color:white;font-size:11px;font-weight:600;margin-right:4px;">Interpretability</span>
-
-
-
- .. figure:: https://content.cld.iop.org/journals/1741-2552/21/3/036010/revision2/jnead44d7f1_hr.jpg
-    :align: center
-    :alt: EEGMiner Architecture
-
- EEGMiner is a neural network model for EEG signal classification using
- learnable generalized Gaussian filters. The model leverages frequency domain
- filtering and connectivity metrics or feature extraction, such as Phase Locking
- Value (PLV) to extract meaningful features from EEG data, enabling effective
- classification tasks.
-
- The model has the following steps:
-
- - **Generalized Gaussian** filters in the frequency domain to the input EEG signals.
-
- - **Connectivity estimators** (corr, plv) or **Electrode-Wise Band Power** (mag), by default (plv).
-     - `'corr'`: Computes the correlation of the filtered signals.
-     - `'plv'`: Computes the phase locking value of the filtered signals.
-     - `'mag'`: Computes the magnitude of the filtered signals.
-
- - **Feature Normalization**
-     - Apply batch normalization.
-
- - **Final Layer**
-     - Feeds the batch-normalized features into a final linear layer for classification.
-
- Depending on the selected method (`mag`, `corr`, or `plv`),
- it computes the filtered signals' magnitude, correlation, or phase locking value.
- These features are then normalized and passed through a batch normalization layer
- before being fed into a final linear layer for classification.
-
- The input to EEGMiner should be a three-dimensional tensor representing EEG signals:
-
- ``(batch_size, n_channels, n_timesteps)``.
-
- Notes
- -----
- EEGMiner incorporates learnable parameters for filter characteristics, allowing the
- model to adaptively learn optimal frequency bands and phase delays for the classification task.
- By default, using the PLV as a connectivity metric makes EEGMiner suitable for tasks requiring
- the analysis of phase relationships between different EEG channels.
-
- The model and the module have patent [eegminercode]_, and the code is CC BY-NC 4.0.
-
- .. versionadded:: 0.9
-
- Parameters
- ----------
- method : str, default="plv"
-     The method used for feature extraction. Options are:
-     - "mag": Electrode-Wise band power of the filtered signals.
-     - "corr": Correlation between filtered channels.
-     - "plv": Phase Locking Value connectivity metric.
- filter_f_mean : list of float, default=[23.0, 23.0]
-     Mean frequencies for the generalized Gaussian filters.
- filter_bandwidth : list of float, default=[44.0, 44.0]
-     Bandwidths for the generalized Gaussian filters.
- filter_shape : list of float, default=[2.0, 2.0]
-     Shape parameters for the generalized Gaussian filters.
- group_delay : tuple of float, default=(20.0, 20.0)
-     Group delay values for the filters in milliseconds.
- clamp_f_mean : tuple of float, default=(1.0, 45.0)
-     Clamping range for the mean frequency parameters.
-
- References
- ----------
- .. [eegminer] Ludwig, S., Bakas, S., Adamos, D. A., Laskaris, N., Panagakis,
-    Y., & Zafeiriou, S. (2024). EEGMiner: discovering interpretable features
-    of brain activity with learnable filters. Journal of Neural Engineering,
-    21(3), 036010.
- .. [eegminercode] Ludwig, S., Bakas, S., Adamos, D. A., Laskaris, N., Panagakis,
-    Y., & Zafeiriou, S. (2024). EEGMiner: discovering interpretable features
-    of brain activity with learnable filters.
-    https://github.com/SMLudwig/EEGminer/.
-    Cogitat, Ltd. "Learnable filters for EEG classification."
-    Patent GB2609265.
-    https://www.ipo.gov.uk/p-ipsum/Case/ApplicationNumber/GB2113420.0
-
- .. 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 EEGMiner
-
-     # Train your model
-     model = EEGMiner(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-eegminer-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import EEGMiner
-
-     # Load pretrained model
-     model = EEGMiner.from_pretrained("username/my-eegminer-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = EEGMiner.from_pretrained("username/my-eegminer-model", n_outputs=4)
 
- **Extracting features and replacing the head:**
+## Architecture
 
- .. code::
-     import torch
+![EEGMiner architecture](https://content.cld.iop.org/journals/1741-2552/21/3/036010/revision2/jnead44d7f1_hr.jpg)
 
-     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)
+## Parameters
 
- **Saving and restoring full configuration:**
+| Parameter | Type | Description |
+|---|---|---|
+| `method` | str, default="plv" | The method used for feature extraction. Options are: - "mag": Electrode-Wise band power of the filtered signals. - "corr": Correlation between filtered channels. - "plv": Phase Locking Value connectivity metric. |
+| `filter_f_mean` | list of float, default=[23.0, 23.0] | Mean frequencies for the generalized Gaussian filters. |
+| `filter_bandwidth` | list of float, default=[44.0, 44.0] | Bandwidths for the generalized Gaussian filters. |
+| `filter_shape` | list of float, default=[2.0, 2.0] | Shape parameters for the generalized Gaussian filters. |
+| `group_delay` | tuple of float, default=(20.0, 20.0) | Group delay values for the filters in milliseconds. |
+| `clamp_f_mean` | tuple of float, default=(1.0, 45.0) | Clamping range for the mean frequency parameters. |
 
- .. code::
-     import json
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
+## References
 
-     model2 = EEGMiner.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.
+1. Ludwig, S., Bakas, S., Adamos, D. A., Laskaris, N., Panagakis, Y., & Zafeiriou, S. (2024). EEGMiner: discovering interpretable features of brain activity with learnable filters. Journal of Neural Engineering, 21(3), 036010.
+2. Ludwig, S., Bakas, S., Adamos, D. A., Laskaris, N., Panagakis, Y., & Zafeiriou, S. (2024). EEGMiner: discovering interpretable features of brain activity with learnable filters. https://github.com/SMLudwig/EEGminer/. Cogitat, Ltd. "Learnable filters for EEG classification." Patent GB2609265. https://www.ipo.gov.uk/p-ipsum/Case/ApplicationNumber/GB2113420.0
 
- 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,