# LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis

arXiv: https://arxiv.org/abs/2510.22257 · HTML: https://mlsanity.com/paper/2510.22257/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 22 of 22)

- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://mlsanity.com/paper/2107.14795/) (2021)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [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)
- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [Transformer-based Spatial-Temporal Feature Learning for EEG Decoding](https://mlsanity.com/paper/2106.11170/) (2021)
- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [Transformer Convolutional Neural Networks for Automated Artifact Detection in Scalp EEG](https://mlsanity.com/paper/2208.02405/) (2022)
- [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)
- [Self-supervised EEG Representation Learning for Automatic Sleep Staging](https://mlsanity.com/paper/2110.15278/) (2021)
- [Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models](https://mlsanity.com/paper/2211.11176/) (2022)
- [Population Transformer: Learning Population-level Representations of Neural Activity](https://mlsanity.com/paper/2406.03044/) (2024)
- [Neural decoding from stereotactic EEG: accounting for electrode variability across subjects](https://mlsanity.com/paper/2411.10458/) (2024)
- [Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering](https://mlsanity.com/paper/2010.13694/) (2020)
- [FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model](https://mlsanity.com/paper/2502.06438/) (2025)
- [CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention](https://mlsanity.com/paper/2501.10885/) (2025)
- [Energy-Efficient Tree-Based EEG Artifact Detection](https://mlsanity.com/paper/2204.09577/) (2022)

## Built on by (most cited first, 26 of 26)

- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026)
- [TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge](https://mlsanity.com/paper/2512.15729/) (2025)
- [Neural Signals Generate Clinical Notes in the Wild](https://mlsanity.com/paper/2601.22197/) (2026)
- [NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines](https://mlsanity.com/paper/2602.13473/) (2026)
- [LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling](https://mlsanity.com/paper/2603.19100/) (2026)
- [Neural Brain Fields: A NeRF-Inspired Approach for Generating Nonexistent EEG Electrodes](https://mlsanity.com/paper/2601.00012/) (2025)
- [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)
- [NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals](https://mlsanity.com/paper/2605.00871/) (2026)
- [Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Models](https://mlsanity.com/paper/2605.26434/) (2026)
- [A Multi-dimensional Framework for Evaluating Generalization in EEG Foundation Models](https://mlsanity.com/paper/2605.28563/) (2026)
- [CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models](https://mlsanity.com/paper/2605.28792/) (2026)
- [Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding](https://mlsanity.com/paper/2607.03094/) (2026)
- [PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence](https://mlsanity.com/paper/2604.04297/) (2026)
- [Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU](https://mlsanity.com/paper/2606.20074/) (2026)
- [CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding](https://mlsanity.com/paper/2607.09543/) (2026)
- [Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices](https://mlsanity.com/paper/2607.18149/) (2026)
- [S-CEReBrO: Breaking the Memory Barrier in Continuous EEG Monitoring](https://mlsanity.com/paper/2607.27913/) (2026)
- [EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models](https://mlsanity.com/paper/2608.13676/) (2026)
- [ReMAP: Self-supervised learning to unveil brain representations and vulnerability](https://mlsanity.com/paper/2608.22042/) (2026)
- [Neural State Prediction: Obstructing Shortcut Learning in EEG Foundation Models](https://mlsanity.com/paper/2609.31167/) (2026)
- [What masking geometry works best for EEG foundation models?](https://mlsanity.com/paper/2609.33487/) (2026)
- [Representational and Functional Robustness to Electrode Montages in EEG Foundation Models](https://mlsanity.com/paper/2609.36288/) (2026)
- [NeurDuo-EEG: A Long-Sequence EEG Foundation Model with Persistent State and Explicit Memory](https://mlsanity.com/paper/2609.38587/) (2026)
- [Lightweight Semantic EEG Foundation Model for Frozen Cross-Disorder Transfer](https://mlsanity.com/paper/2610.05503/) (2026)
- [Brain2Face: A Study on the Relation of EEG Signals and Spatio-temporal Head Avatars](https://mlsanity.com/paper/2610.17356/) (2026)

## Abstract (arXiv)

Electroencephalography (EEG) offers a non-invasive lens into human brain activity, but building large-scale models is hampered by topological heterogeneity: each public EEG data defines its own electrode layout, limiting generalization. We introduce LUNA (Latent Unified Network Architecture), a self-supervised foundation model that reconciles disparate electrode geometries while scaling linearly -- not quadratically -- with channel count. LUNA compresses multi-channel EEG into a fixed-size, topology-agnostic latent space via learned queries and cross-attention. Downstream transformer blocks then operate exclusively on this latent representation using patch-wise temporal self-attention, decoupling computation from electrode count. Pre-trained on TUEG and Siena (over 21,000 hours of raw EEG across diverse montages) using a masked-patch reconstruction objective, LUNA transfers effectively to four downstream tasks: abnormality detection, artifact rejection, slowing classification, and emotion recognition. It demonstrates highly competitive performance across several benchmarks, achieving state-of-the-art results on TUAR and TUSL, e.g., 0.921 AUROC on TUAR, while reducing FLOPs by 300x and trimming GPU memory use by up to 10x. Critically, these gains are consistent across all evaluated electrode configurations. Code is available at https://github.com/pulp-bio/BioFoundation