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) [1].

> **Architecture-only repository.** Documents the
> `braindecode.models.SignalJEPA_PostLocal` class. **No pretrained weights are
> distributed here.** Instantiate the model and train it on your own
> data.

## 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 illustrative defaults — adjust to
match your recording.

## Documentation
- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.SignalJEPA_PostLocal.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/signal_jepa.py#L749>


## Architecture

![SignalJEPA_PostLocal architecture](https://braindecode.org/dev/_static/model/sjepa_post-local.jpg)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `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


## Citation

Cite the original architecture paper (see *References* 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.