# FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model

arXiv: https://arxiv.org/abs/2502.06438 · HTML: https://mlsanity.com/paper/2502.06438/

## Key facts

- Cited by 16 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 14 of its 29 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Placed in

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

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [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)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [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)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Neuro-GPT: Towards A Foundation Model for EEG](https://mlsanity.com/paper/2311.03764/) (2023)
- [EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model](https://mlsanity.com/paper/2401.10278/) (2024)
- [EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification](https://mlsanity.com/paper/2407.20254/) (2024)
- [Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting](https://mlsanity.com/paper/2404.15772/) (2024)
- [Energy-Efficient Tree-Based EEG Artifact Detection](https://mlsanity.com/paper/2204.09577/) (2022)

## Built on by (most cited first, 16 of 16)

- [CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model](https://mlsanity.com/paper/2506.09110/) (2025)
- [LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis](https://mlsanity.com/paper/2510.22257/) (2025)
- [OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models](https://mlsanity.com/paper/2606.00815/) (2026)
- [TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge](https://mlsanity.com/paper/2512.15729/) (2025)
- [LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling](https://mlsanity.com/paper/2603.19100/) (2026)
- [Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks](https://mlsanity.com/paper/2605.02500/) (2026)
- [FEMBA on the Edge: Physiologically-Aware Pre-Training, Quantization, and Deployment of a Bidirectional Mamba EEG Foundation Model on an Ultra-low Power Microcontroller](https://mlsanity.com/paper/2603.26716/) (2026)
- [CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models](https://mlsanity.com/paper/2605.28792/) (2026)
- [Brain-to-Language Decoding: Tasks, Signals, Methods, Evaluation, Practical Use and Beyond](https://mlsanity.com/paper/2609.27650/) (2026)
- [A Systematic Evaluation of Self-Supervised Learning for Label-Efficient Sleep Staging with Wearable EEG](https://mlsanity.com/paper/2510.07960/) (2025)
- [PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence](https://mlsanity.com/paper/2604.04297/) (2026)
- [Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices](https://mlsanity.com/paper/2607.18149/) (2026)
- [ZIPBrain: Can EEG Foundation Models Be Faster, Locally Deployable, but Accurate?](https://mlsanity.com/paper/2608.07033/) (2026)
- [BRIDGE-EEG: Bridging Self-Supervised Pretraining and Efficient Deployment for Cross-Dataset EEG Classification](https://mlsanity.com/paper/2609.12218/) (2026)
- [Beyond Flattened Tokens: Structure-Preserving EEG Decoding with Reusable TriDim Blocks](https://mlsanity.com/paper/2609.19842/) (2026)
- [NeurDuo-EEG: A Long-Sequence EEG Foundation Model with Persistent State and Explicit Memory](https://mlsanity.com/paper/2609.38587/) (2026)

## Abstract (arXiv)

Accurate and efficient electroencephalography (EEG) analysis is essential for detecting seizures and artifacts in long-term monitoring, with applications spanning hospital diagnostics to wearable health devices. Robust EEG analytics have the potential to greatly improve patient care. However, traditional deep learning models, especially Transformer-based architectures, are hindered by their quadratic time and memory complexity, making them less suitable for resource-constrained environments. To address these challenges, we present FEMBA (Foundational EEG Mamba + Bidirectional Architecture), a novel self-supervised framework that establishes new efficiency benchmarks for EEG analysis through bidirectional state-space modeling. Unlike Transformer-based models, which incur quadratic time and memory complexity, FEMBA scales linearly with sequence length, enabling more scalable and efficient processing of extended EEG recordings. Trained on over 21,000 hours of unlabeled EEG and fine-tuned on three downstream tasks, FEMBA achieves competitive performance in comparison with transformer models, with significantly lower computational cost. Specifically, it reaches 81.82% balanced accuracy (0.8921 AUROC) on TUAB and 0.949 AUROC on TUAR, while a tiny 7.8M-parameter variant demonstrates viability for resource-constrained devices. These results pave the way for scalable, general-purpose EEG analytics in both clinical and highlight FEMBA as a promising candidate for wearable applications.