PBT / README.md

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metadata

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

PBT

Patched Brain Transformer (PBT) model from Klein et al (2025) [pbt].

Architecture-only repository. Documents the braindecode.models.PBT 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 PBT

model = PBT(
    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.PBT.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/patchedtransformer.py#L17

Architecture

Parameters

Parameter	Type	Description
`d_input`	int, optional	Size (in samples) of each patch (token) extracted along the time axis.
`embed_dim`	int, optional	Transformer embedding dimensionality.
`num_layers`	int, optional	Number of Transformer encoder layers.
`num_heads`	int, optional	Number of attention heads.
`drop_prob`	float, optional	Dropout probability used in Transformer components.
`learnable_cls`	bool, optional	Whether the classification token is learnable.
`bias_transformer`	bool, optional	Whether to use bias in Transformer linear layers.
`activation`	nn.Module, optional	Activation function class to use in Transformer feed-forward layers.

References

Klein, T., Minakowski, P., & Sager, S. (2025). Flexible Patched Brain Transformer model for EEG decoding. Scientific Reports, 15(1), 1-12. https://www.nature.com/articles/s41598-025-86294-3
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J. & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR).
Krell, M. M., Kosec, M., Perez, S. P., & Fitzgibbon, A. (2021). Efficient sequence packing without cross-contamination: Accelerating large language models without impacting performance. arXiv preprint arXiv:2107.02027.

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.