File size: 2,273 Bytes
56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc 07f46fd 56a1cbc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | ---
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
## 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.
|