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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
```bash
pip install braindecode
```
```python
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
1. 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
2. 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).
3. 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:
```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.
|