| --- |
| 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. |
|
|
