| --- |
| license: bsd-3-clause |
| library_name: braindecode |
| pipeline_tag: feature-extraction |
| tags: |
| - eeg |
| - biosignal |
| - pytorch |
| - neuroscience |
| - braindecode |
| - foundation-model |
| - convolutional |
| --- |
| |
| # LUNA |
|
|
| LUNA from Döner et al [LUNA]. |
|
|
| > **Architecture-only repository.** Documents the |
| > `braindecode.models.LUNA` 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 LUNA |
| |
| model = LUNA( |
| 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.LUNA.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/luna.py#L30> |
|
|
|
|
| ## Architecture |
|
|
|  |
|
|
|
|
| ## Parameters |
|
|
| | Parameter | Type | Description | |
| |---|---|---| |
| | `patch_size` | int | Number of time samples per patch. Default: 40. | |
| | `num_queries` | int | Number of learned queries for channel unification. Paper uses: 4 (Base), 6 (Large), 8 (Huge). Default: 4. | |
| | `embed_dim` | int | Embedding dimension for patch features. Paper uses: 64 (Base), 96 (Large), 128 (Huge). Default: 64. | |
| | `depth` | int | Number of transformer encoder blocks. Paper uses: 8 (Base), 10 (Large), 24 (Huge). Default: 8. | |
| | `num_heads` | int | Number of attention heads in channel unification. Default: 2. | |
| | `mlp_ratio` | float | Ratio of MLP hidden dimension to embedding dimension. Default: 4.0. | |
| | `norm_layer` | nn.Module | Normalization layer class. Default: nn.LayerNorm. | |
| | `drop_path` | float | Stochastic depth rate. Default: 0.0. | |
|
|
|
|
| ## References |
|
|
| 1. Döner, B., Ingolfsson, T. M., Benini, L., & Li, Y. (2025). LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis. The Thirty-Ninth Annual Conference on Neural Information Processing Systems - NeurIPS. Retrieved from https://openreview.net/forum?id=uazfjnFL0G |
|
|
|
|
| ## 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. |
|
|
