README.md

---
license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
  - eeg
  - biosignal
  - pytorch
  - neuroscience
  - braindecode
  - foundation-model
  - convolutional
---

# SignalJEPA_PostLocal

Post-local downstream architecture introduced in signal-JEPA Guetschel, P et al (2024) .

> **Architecture-only repository.** This repo documents the
> `braindecode.models.SignalJEPA_PostLocal` 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 SignalJEPA_PostLocal

model = SignalJEPA_PostLocal(
    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.SignalJEPA_PostLocal.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/signal_jepa.py#L749>

## Architecture description

The block below is the rendered class docstring (parameters,
references, architecture figure where available).

<div class='bd-doc'><main>
<p>Post-local 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_post-local.jpg
     :align: center
     :alt: sJEPA Pre-Local.

 .. versionadded:: 0.9

 .. rubric:: Pretrained Weights

 Only the feature encoder weights are reused from the shared
 SSL checkpoints. This model has no channel embedding nor transformer,
 so ``strict=False`` is required at load time to skip the unused keys.
 Either hub variant works; the ``_without-chans`` one is slightly
 smaller.

 .. important::
    **Pre-trained Weights Available**

    .. code:: python
        from braindecode.models import SignalJEPA_PostLocal

        model = SignalJEPA_PostLocal.from_pretrained(
            "braindecode/signal-jepa_without-chans",
            n_chans=22,
            input_window_seconds=16.0,
            n_outputs=4,
            strict=False,
        )

    Requires installing ``braindecode[hub]`` for Hub integration.

 .. rubric:: Usage

 .. code:: python
     from braindecode.models import SignalJEPA_PostLocal

     model = SignalJEPA_PostLocal(
         n_chans=22,
         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_PostLocal

     # Train your model
     model = SignalJEPA_PostLocal(n_chans=22, n_outputs=4, n_times=1000)
     # ... training code ...

     # Push to the Hub
     model.push_to_hub(
         repo_id="username/my-signaljepa_postlocal-model",
         commit_message="Initial model upload",
     )

 **Loading a model from the Hub:**

 .. code::
     from braindecode.models import SignalJEPA_PostLocal

     # Load pretrained model
     model = SignalJEPA_PostLocal.from_pretrained("username/my-signaljepa_postlocal-model")

     # Load with a different number of outputs (head is rebuilt automatically)
     model = SignalJEPA_PostLocal.from_pretrained("username/my-signaljepa_postlocal-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 = SignalJEPA_PostLocal.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.