---
license: cc-by-nd-4.0
language:
- en
tags:
- foundation-model
- neuroscience
- eeg
- mamba
---
LuMamba: Latent Unified Mamba for Electrode
Topology-Invariant and Efficient EEG Modeling
**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**:
1. **Open a PR** in the [BioFoundation repository](https://github.com/pulp-bio/BioFoundation) describing the change (architecture/head/training recipe, datasets, preprocessing, compute).
2. Include **reproducibility artifacts**: configs, seeds, scripts, environment details, training/validation logs, and the **evaluation protocol** (e.g., TUAB/TUAR/TUSL) with exact splits.
3. Provide **comprehensive results** (AUROC/AUPR/BA, FLOPs, memory) vs. the baselines reported in the LuMamba paper.
4. 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.
- **Downstream tasks:** **TUAB** (abnormal), **TUAR** (artifacts), **TUSL** (slowing), **SEED-V** (emotion; unseen 62-ch montage), **APAVA** (Alzheimer's disease; unseen 16-ch layout, **TDBrain** (Parkinson's disease; unseen 26-ch layout)
---
## 🚀 Model Variants
The model currently exists in a Tiny Variant, with the following parameters:
| Variant | Parameters | FEMBA parameters |LUNA parameters |
|-----------------|------------|-----------------------------|------------------------------------|
| LuMamba_tiny | 4.1M |(`num_blocks` = 2, `exp` = 2)|(`num_queries` = 6, `embed_dim` = 64)
Larger model sizes can be attained by increasing the number of bi-Mamba blocks `num_blocks` (e.g. 8 bi-Mamba blocks yields 15M parameters).
---
## 📊 Results (Highlights)
- **TUAB (abnormal vs normal):** 80.99 % Bal. Acc., 0.883 AUROC, 0.892 AUPR.
(LuMamba-Tiny, pre-trained with LeJEPA-reconstruction).
- **APAVA (Alzheimer's detection)**: 0.955 AUROC, 0.970 AUPR (LuMamba-Tiny, pre-trained with LeJEPA-reconstruction).
- **TDBrain (Parkinson's detection)**: 0.961 AUROC, 0.960 AUPR (LuMamba-Tiny, pre-trained with LeJEPA-reconstruction).
**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
0. **Install & read data prep**: clone the [BioFoundation repo](https://github.com/pulp-bio/BioFoundation), set up the environment as described there, then open `make_datasets/README.md` for dataset-specific notes (naming, expected folder layout, and common pitfalls).
1. **Point to weights**: set `pretrained_safetensors_path: /path/to/LuMamba_*.safetensors` in the experiment YAML.
2. **Preprocess data**: acquire fine-tuning dataset and follow preprocessing protocol (see guide in `/make_datasets/README.md`) to generate `train/test/val.h5` files.
3. **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.
4. **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.
5. **Env vars**: export `DATA_PATH` (dataset root) and `CHECKPOINT_DIR` (artifacts).
6. **Trainer/optimizer**: adjust `gpus/devices`, `batch_size`, `max_epochs`, LR/scheduler if needed.
7. **I/O**: set `io.base_output_path` and confirm `io.checkpoint_dirpath` exists.
To launch fine-tuning (Hydra):
```bash
python -u run_train.py +experiment=LuMamba_finetune
```
---
## ⚖️ Responsible AI, Risks & Biases
- **Clinical safety:** research-only; human oversight required.
- **Bias & drift:** montage/device/population differences can induce shifts; validate and monitor.
- **Artifacts & rare events:** robustness varies; use QC and task-appropriate preprocessing.
---
## 🔗 Sources
- **Code:** https://github.com/pulp-bio/BioFoundation
- **Paper:** LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling (arxiv:2603.19100)
---
## 📜 Citation
If you use LuMamba, please cite:
```bibtex
@misc{broustail2026lumambalatentunifiedmamba,
title={LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling},
author={Danaé Broustail and Anna Tegon and Thorir Mar Ingolfsson and Yawei Li and Luca Benini},
year={2026},
eprint={2603.19100},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2603.19100},
}
```
---
## 🛠️ Maintenance & Contact
- **Issues & support:** please open a GitHub issue in the BioFoundation repository.
---
## 🗒️ Changelog
- **v1.0:** Initial release of LuMamba model card with task-specific checkpoints and instructions.