README.md

metadata

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

pip install braindecode

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:

@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.