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322c961 verified 10 days ago

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

InterpolatedSignalJEPA

Channel-interpolating wrapper around :class:SignalJEPA.

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

Quick start

pip install braindecode

from braindecode.models import InterpolatedSignalJEPA

model = InterpolatedSignalJEPA(
    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.InterpolatedSignalJEPA.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/interpolated.py#L1

Parameters

(see source for parameter list)

References

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:

@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.