# EEG Foundation Models: A Critical Review of Current Progress and Future Directions

arXiv: https://arxiv.org/abs/2507.11783 · HTML: https://mlsanity.com/paper/2507.11783/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 178 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 30 of 71)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [Scaling Vision Transformers to 22 Billion Parameters](https://mlsanity.com/paper/2302.05442/) (2023)

## Built on by (most cited first, 30 of 32)

- [Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning](https://mlsanity.com/paper/2509.24222/) (2025)
- [OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models](https://mlsanity.com/paper/2606.00815/) (2026)
- [NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces](https://mlsanity.com/paper/2605.14698/) (2026)
- [Laya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction](https://mlsanity.com/paper/2603.16281/) (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)
- [Neuroprobe: Evaluating Intracranial Brain Responses to Naturalistic Stimuli](https://mlsanity.com/paper/2509.21671/) (2025)
- [ZUNA: Flexible EEG Superresolution with Position-Aware Diffusion Autoencoders](https://mlsanity.com/paper/2602.18478/) (2026)
- [Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models](https://mlsanity.com/paper/2605.17562/) (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)
- [SpecMoE: Spectral Mixture-of-Experts Foundation Model for Cross-Species EEG Decoding](https://mlsanity.com/paper/2603.16739/) (2026)
- [Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Models](https://mlsanity.com/paper/2605.26434/) (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)
- [MANAS-2: Constrained Reconstruction for EEG Foundation Models](https://mlsanity.com/paper/2609.13717/) (2026)
- [Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts](https://mlsanity.com/paper/2604.16926/) (2026)
- [EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks](https://mlsanity.com/paper/2606.01767/) (2026)
- [NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models](https://mlsanity.com/paper/2607.03925/) (2026)
- [CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding](https://mlsanity.com/paper/2607.09543/) (2026)
- [DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations](https://mlsanity.com/paper/2607.11578/) (2026)
- [Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices](https://mlsanity.com/paper/2607.18149/) (2026)
- [Multimodal Pretraining for Generalizable EEG Representation Learning](https://mlsanity.com/paper/2607.21384/) (2026)
- [ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution](https://mlsanity.com/paper/2607.27308/) (2026)
- [EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models](https://mlsanity.com/paper/2608.13676/) (2026)
- [Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets](https://mlsanity.com/paper/2608.24727/) (2026)
- [A Roadmap for MEG Foundation Models](https://mlsanity.com/paper/2609.04461/) (2026)
- [EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models](https://mlsanity.com/paper/2609.15687/) (2026)
- [iMINDBench: iEEG Multi-Institution Neural Decoding Benchmark](https://mlsanity.com/paper/2609.18104/) (2026)
- [Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding](https://mlsanity.com/paper/2609.33880/) (2026)
- [PhysioTRACE: Provenance-Aware Stress Tests for Physiological Foundation Models](https://mlsanity.com/paper/2609.34466/) (2026)
- [NeurDuo-EEG: A Long-Sequence EEG Foundation Model with Persistent State and Explicit Memory](https://mlsanity.com/paper/2609.38587/) (2026)

## Abstract (arXiv)

Premise. Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigations. The inability of supervised EEG encoders to learn robust EEG patterns and their over-reliance on expensive signal annotations have sparked a transition towards general-purpose self-supervised EEG encoders, i.e., EEG foundation models (EEG-FMs), for robust and scalable EEG feature extraction. However, the real-world readiness of early EEG-FMs and the rubrics for long-term research progress remain unclear. Objective. In this work, we conduct a review of ten early EEG-FMs to capture common trends and identify key directions for future development of EEG-FMs. Methods. We comparatively analyze each EEG-FM using three fundamental pillars of foundation modeling, namely the representation of input data, self-supervised modeling, and the evaluation strategy. Based on this analysis, we present a critical synthesis of EEG-FM methodology, empirical findings, and outstanding research gaps. Results. We find that most EEG-FMs adopt a sequence-based modeling scheme that relies on transformer-based backbones and the reconstruction of masked temporal EEG sequences for self-supervision. However, model evaluations remain heterogeneous and largely limited, making it challenging to assess their practical off-the-shelf utility. In addition to adopting standardized and realistic evaluations, future work should demonstrate more substantial scaling effects and make principled and trustworthy choices throughout the EEG representation learning pipeline. Significance. Our review indicates that the development of benchmarks, software tools, technical methodologies, and applications in collaboration with domain experts may advance the translational utility and real-world adoption of EEG-FMs.