# REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects

arXiv: https://arxiv.org/abs/2510.21585 · HTML: https://mlsanity.com/paper/2510.21585/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 125 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies](https://mlsanity.com/paper/2404.06395/) (2024)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)

## Built on by (most cited first, 30 of 80)

- [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)
- [OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models](https://mlsanity.com/paper/2606.00815/) (2026)
- [Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signal](https://mlsanity.com/paper/2602.11558/) (2026)
- [NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces](https://mlsanity.com/paper/2605.14698/) (2026)
- [BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression](https://mlsanity.com/paper/2602.16951/) (2026)
- [Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes](https://mlsanity.com/paper/2604.23091/) (2026)
- [Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders](https://mlsanity.com/paper/2605.13930/) (2026)
- [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)
- [Laya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction](https://mlsanity.com/paper/2603.16281/) (2026)
- [LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling](https://mlsanity.com/paper/2603.19100/) (2026)
- [A Negative-Control Protocol for Clinical EEG Foundation-Model Benchmarks: Dataset Identity and External-Cohort Stress Testing](https://mlsanity.com/paper/2607.24519/) (2026)
- [Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods](https://mlsanity.com/paper/2604.27033/) (2026)
- [Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models](https://mlsanity.com/paper/2605.17562/) (2026)
- [NeuroSkill(tm): Proactive Real-Time Agentic System Capable of Modeling Human State of Mind](https://mlsanity.com/paper/2603.03212/) (2026)
- [Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks](https://mlsanity.com/paper/2605.02500/) (2026)
- [Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability](https://mlsanity.com/paper/2605.07212/) (2026)
- [EEG-FM-Audit: A Systematic Evaluation and Analysis Pipeline for EEG Foundation Models](https://mlsanity.com/paper/2605.26910/) (2026)
- [The Identity Trap in EEG Foundation Models: A Diagnostic Audit](https://mlsanity.com/paper/2606.06647/) (2026)
- [A Systematic Evaluation of Sample-Level Tokenization Strategies for MEG Foundation Models](https://mlsanity.com/paper/2602.16626/) (2026)
- [SpecMoE: Spectral Mixture-of-Experts Foundation Model for Cross-Species EEG Decoding](https://mlsanity.com/paper/2603.16739/) (2026)
- [CLEF: EEG Foundation Model for Learning Clinical Semantics](https://mlsanity.com/paper/2605.10817/) (2026)
- [Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Models](https://mlsanity.com/paper/2605.26434/) (2026)
- [CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models](https://mlsanity.com/paper/2605.28792/) (2026)
- [A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning](https://mlsanity.com/paper/2606.08583/) (2026)
- [Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding](https://mlsanity.com/paper/2607.03094/) (2026)
- [Cross-Cohort Spectral-Temporal Dissociation in Frozen EEG Foundation-Model Representations](https://mlsanity.com/paper/2607.24834/) (2026)
- [Lightweight Adaptation of EEG Foundation Models for Stroke Motor Imagery Decoding: Domain Shift and Subject-Level Robustness](https://mlsanity.com/paper/2609.00282/) (2026)
- [NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis](https://mlsanity.com/paper/2609.01971/) (2026)

## Abstract (arXiv)

Foundation models have transformed AI by reducing reliance on task-specific data through large-scale pretraining. While successful in language and vision, their adoption in EEG has lagged due to the heterogeneity of public datasets, which are collected under varying protocols, devices, and electrode configurations. Existing EEG foundation models struggle to generalize across these variations, often restricting pretraining to a single setup, resulting in suboptimal performance, in particular under linear probing. We present REVE (Representation for EEG with Versatile Embeddings), a pretrained model explicitly designed to generalize across diverse EEG signals. REVE introduces a novel 4D positional encoding scheme that enables it to process signals of arbitrary length and electrode arrangement. Using a masked autoencoding objective, we pretrain REVE on over 60,000 hours of EEG data from 92 datasets spanning 25,000 subjects, representing the largest EEG pretraining effort to date. REVE achieves state-of-the-art results on 10 downstream EEG tasks, including motor imagery classification, seizure detection, sleep staging, cognitive load estimation, and emotion recognition. With little to no fine-tuning, it demonstrates strong generalization, and nuanced spatio-temporal modeling. We release code, pretrained weights, and tutorials to support standardized EEG research and accelerate progress in clinical neuroscience.