LuMamba (Latent Unified Mamba) is an EEG foundation model built on efficient Mamba state-space learning, capable of handling heterogeneous channel topologies.
LuMamba addresses varying channel layouts with LUNA channel unification, projecting a given EEG channel layout to a fixed latent topology, and overcomes the quadratic complexity of transformers with FEMBA's efficient bidirectional Mamba encoder.
🔒 License & Usage Policy (Weights)
Weights license: The released model weights are licensed under Creative Commons Attribution–NoDerivatives 4.0 (CC BY-ND 4.0). This section summarizes the practical implications for users. This is not legal advice; please read the full license text.
✅ You may
Use and redistribute the unmodified LuMamba weights (including in commercial settings) with proper attribution to the LuMamba authors.
Fine-tune / adapt the weights for your internal use (research or production) without redistributing the modified weights.
Publish your code, configs, logs, and papers describing experiments with LuMamba (please cite the paper).
🚫 You may not
Share, host, or redistribute any modified weights (including LoRA/adapter/delta checkpoints or pruned/quantized variants). Any parameter set that encodes an adaptation is considered a derivative and cannot be shared under CC BY-ND 4.0.
Imply endorsement by the LuMamba authors for any derivative or evaluation without our written permission.
Use the LuMamba name in a way that suggests your modified model is an official LuMamba release.
🤝 How to contribute improvements (PR-gated releases)
We welcome community improvements via a pull-request (PR) workflow. If you believe your improvements should become an official LuMamba release:
Open a PR in the BioFoundation repository describing the change (architecture/head/training recipe, datasets, preprocessing, compute).
Include reproducibility artifacts: configs, seeds, scripts, environment details, training/validation logs, and the evaluation protocol (e.g., TUAB/TUAR/TUSL) with exact splits.
Provide comprehensive results (AUROC/AUPR/BA, FLOPs, memory) vs. the baselines reported in the LuMamba paper.
After maintainer review, approved changes will be retrained/validated and, if accepted, released by the maintainers as a new official LuMamba checkpoint under CC BY-ND 4.0.
Rationale: CC BY-ND protects users from fragmented, lower-quality “LuMamba variants,” while still enabling internal fine-tuning and a path for the community to upstream improvements through review.
🔎 Model Summary
Goal: Efficient and topology-agnostic EEG modeling with linear complexity in sequence length.
Core idea:Channel-Unification Module uses learned queries (Q) with cross-attention to map any set of channels to a fixed latent space. bidirectional Mamba blocks then operate on that latent sequence.
Pre-training data: TUEG, >21,000 hours of raw EEG; downstream subjects removed to avoid leakage.
Efficiency: Up to 377× fewer FLOPs relative to transformer-based baselines and supporting up to 500x longer EEG windows, thanks to the efficient FEMBA bi-Mamba encoder.
🧠 Intended Use & Limitations
Intended use. Research on EEG representation learning & classification (abnormality, artifacts, slowing, emotion), especially when montages vary or channel counts are high.
Limitations.
Not a medical device. Do not use for clinical decisions without proper validation & regulatory clearance.
Unseen topologies: Zero-shot transfer to very different/dense layouts (e.g., SEED-V) can underperform SOTA despite positive scaling; consider augmenting pre-training montage diversity and spatial encodings.
Distribution shifts: Performance varies across cohorts, devices, and label protocols; validate locally and consider domain adaptation.
🏗️ Architecture & Training
LUNA Tokenizer & features. EEG is patch-segmented; temporal features via 1D conv w/ GroupNorm+GELU; frequency features (FFT mag/phase → MLP) are added; 3D electrode coordinates encoded via NeRF-style sinusoids → MLP (positional enc).
LUNA Channel-Unification Module.Q learned queries cross-attend to channel-wise patch features to produce a fixed Q×E latent per patch; FFN + Transformer layers refine the query tokens. Complexity is O(Q·C) (linear in channels).
FEMBA Bi-Mamba Temporal encoder.Mamba blocks process the embeddings in separate forward and backward streams.
Pre-training objectives.Masked-patch reconstruction is used to reconstruct masked tokens. In parallel, the LeJEPA loss encourages an isotropic Gaussian embedding distribution to minimize downstream prediction risk.
🔧 How to Use
LuMamba weights are organized by pre-training configuration:
Reconstruction-only → variants pre-trained with masked reconstruction exclusively
LeJEPA-reconstruction → variants pre-trained with a balanced mixture of masked reconstruction and LeJEPA losses. Variants exist for two different LeJEPA hyperparameters: 128 and 300 projection slices.
LeJEPA-only → variant pre-trained with LeJEPA exclusively.
All variants are pre-trained on TUEG.
LuMamba experiments are categorized by two Hydra configurations, in BioFoundation/config/experiments:
LuMamba_finetune.yaml → configuration for fine-tuning experiments.
LuMamba_pretrain.yaml → configuration for pre-training experiments.
🔧 Fine-tuning — General Checklist
Install & read data prep: clone the BioFoundation repo, set up the environment as described there, then open make_datasets/README.md for dataset-specific notes (naming, expected folder layout, and common pitfalls).
Point to weights: set pretrained_safetensors_path: /path/to/LuMamba_*.safetensors in the experiment YAML.
Preprocess data: acquire fine-tuning dataset and follow preprocessing protocol (see guide in /make_datasets/README.md) to generate train/test/val.h5 files.
Update data module of LuMamba_finetune.yaml config:
TUH datasets (TUAB/TUSL/TUAR) → change _target_ in /data_module: to datasets.tuh_dataset.TUH_Dataset.
Other → change /data_module:_target_ to corresponding dataset.py file in BioFoundation/datasets (e.g., for TDBrain dataset use _target_:datasets.tdbrain_dataset.TDBrain_Dataset)
HDF5 file location → change /data_module:hdf5_file for train, test, and val with the path to the corresponding HDF5 data split file.
Task settings:
Task type: override with /task:finetune_task_LUNA for classification and /task:finetune_regression_task_LuMamba for regression tasks
Classification type: set classification_type (bc, mcc) and model.num_classes to match your downstream task. In a regression scenario,mcc is used and model.num_classes describes the number of features in the output.
Classifier choice: set /model:classifier_option (mamba for FEMBA classifier, linear for single-layer linear classifier,null for default LUNA classifier)
Configuration file includes further #CHANGEME tags and instructions for a working example.
Env vars: export DATA_PATH (dataset root) and CHECKPOINT_DIR (artifacts).
Trainer/optimizer: adjust gpus/devices, batch_size, max_epochs, LR/scheduler if needed.
I/O: set io.base_output_path and confirm io.checkpoint_dirpath exists.
Paper: LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling (arxiv:2603.19100)
📜 Citation
If you use LuMamba, please cite:
bibtex
1@misc{broustail2026lumambalatentunifiedmamba,
2 title={LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling},
3 author={Danaé Broustail and Anna Tegon and Thorir Mar Ingolfsson and Yawei Li and Luca Benini},
4 year={2026},
5 eprint={2603.19100},
6 archivePrefix={arXiv},
7 primaryClass={cs.AI},
8 url={https://arxiv.org/abs/2603.19100},
9}
🛠️ Maintenance & Contact
Issues & support: please open a GitHub issue in the BioFoundation repository.
🔗 Related Models
LUNA — Transformer-based topology-agnostic EEG foundation model (NeurIPS 2025). Source of the channel-unification cross-attention module that LuMamba reuses.
FEMBA — Bidirectional Mamba foundation model for EEG. Source of the linear-complexity temporal backbone that LuMamba reuses.
TinyMyo — Tiny foundation model for flexible EMG signal processing at the edge.
🗒️ Changelog
v1.0: Initial release of LuMamba model card with task-specific checkpoints and instructions.
