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license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
- eeg
- biosignal
- pytorch
- neuroscience
- braindecode
- foundation-model
- convolutional
---
# MEDFormer
Medformer from Wang et al (2024) [Medformer2024].
> **Architecture-only repository.** Documents the
> `braindecode.models.MEDFormer` 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 MEDFormer
model = MEDFormer(
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.MEDFormer.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/medformer.py#L20>
## Architecture
## Parameters
| Parameter | Type | Description |
|---|---|---|
| `patch_len_list` | list of int, optional | Patch lengths for multi-granularity patching; each entry selects a temporal scale. The default is `[14, 44, 45]`. |
| `embed_dim` | int, optional | Embedding dimensionality. The default is `128`. |
| `num_heads` | int, optional | Number of attention heads, which must divide :attr:`d_model`. The default is `8`. |
| `drop_prob` | float, optional | Dropout probability. The default is `0.1`. |
| `no_inter_attn` | bool, optional | If `True`, disables inter-granularity attention. The default is `False`. |
| `num_layers` | int, optional | Number of encoder layers. The default is `6`. |
| `dim_feedforward` | int, optional | Feedforward dimensionality. The default is `256`. |
| `activation_trans` | nn.Module, optional | Activation module used in transformer encoder layers. The default is :class:`nn.ReLU`. |
| `single_channel` | bool, optional | If `True`, processes each channel independently, increasing capacity and cost. The default is `False`. |
| `output_attention` | bool, optional | If `True`, returns attention weights for interpretability. The default is `True`. |
| `activation_class` | nn.Module, optional | Activation used in the final classification layer. The default is :class:`nn.GELU`. |
## References
1. Wang, Y., Huang, N., Li, T., Yan, Y., & Zhang, X. (2024). Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), Advances in Neural Information Processing Systems (Vol. 37, pp. 36314-36341). doi:10.52202/079017-1145.
## 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.
|