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

pip install braindecode

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:

@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.