# LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling

arXiv: https://arxiv.org/abs/2603.19100 · HTML: https://mlsanity.com/paper/2603.19100/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Physiological signal modeling, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 19 Mar 2026.

## Placed in

- State-space sequence models (S4, Mamba) / [Physiological signal modeling](https://mlsanity.com/ssm/physiological-signal-modeling/)

## Builds on (most cited first, 15 of 15)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics](https://mlsanity.com/paper/2511.08544/) (2025)
- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects](https://mlsanity.com/paper/2510.21585/) (2025)
- [Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification](https://mlsanity.com/paper/2405.19363/) (2024)
- [EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model](https://mlsanity.com/paper/2401.10278/) (2024)
- [EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs](https://mlsanity.com/paper/2402.17772/) (2024)
- [LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis](https://mlsanity.com/paper/2510.22257/) (2025)
- [BIOT: Cross-data Biosignal Learning in the Wild](https://mlsanity.com/paper/2305.10351/) (2023)
- [FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model](https://mlsanity.com/paper/2502.06438/) (2025)
- [Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting](https://mlsanity.com/paper/2404.15772/) (2024)
- [CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention](https://mlsanity.com/paper/2501.10885/) (2025)
- [BioMamba: Leveraging Spectro-Temporal Embedding in Bidirectional Mamba for Enhanced Biosignal Classification](https://mlsanity.com/paper/2503.11741/) (2025)

## Built on by (most cited first, 4 of 4)

- [Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU](https://mlsanity.com/paper/2606.20074/) (2026)
- [Capturing Cardiac Cyclicity through Phase-Equivariant Self-Supervised Learning](https://mlsanity.com/paper/2608.21147/) (2026)
- [Representational and Functional Robustness to Electrode Montages in EEG Foundation Models](https://mlsanity.com/paper/2609.36288/) (2026)
- [SPERA: Spherical Prior EEG Foundation Model with Geometry- and Frequency-Aware Latent Prediction](https://mlsanity.com/paper/2610.10571/) (2026)

## Abstract (arXiv)

Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains challenging due to differing electrode topologies and computational scalability, as Transformer architectures incur quadratic sequence complexity. As a joint solution, we propose LuMamba (Latent Unified Mamba), a self-supervised framework combining topology-invariant encodings with linear-complexity state-space modeling, using LUNA's learned-query cross-attention mechanism for channel unification, and FEMBA's bidirectional Mamba blocks for efficient temporal modeling. Within this architecture, we provide the first systematic investigation of the Latent-Euclidean Joint-Embedding Predictive Architecture (LeJEPA) for biosignal learning. Pre-trained on over 21,000 hours of unlabeled EEG from the TUEG corpus, LuMamba is evaluated on five downstream tasks spanning abnormality detection, artifact recognition, and mental condition classification across electrode configurations ranging from 16 to 26 channels. In the pre-training objective, masked reconstruction alone yields structured but less generalizable representations, while LeJEPA alone produces diffuse embeddings; combining both objectives achieves the most robust performance. With only 4.6M parameters, LuMamba attains 80.99% balanced accuracy on TUAB and achieves state-of-art performance on Alzheimer's detection (0.97 AUPR), while requiring 377x fewer FLOPS than state-of-art models at equivalent sequence lengths and scaling to 12x longer sequences before reaching typical GPU memory limits. Code is available at https://github.com/pulp-bio/biofoundation.