# A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives arXiv: https://arxiv.org/abs/2504.20069 ยท HTML: https://mlsanity.com/paper/2504.20069/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 122 references that are in the map. - First posted on arXiv 24 Apr 2025. ## Builds on (most cited first, 24 of 24) - [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021) - [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022) - [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021) - [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024) - [BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers](https://mlsanity.com/paper/2208.06366/) (2022) - [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024) - [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024) - [NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals](https://mlsanity.com/paper/2409.00101/) (2024) - [Neuro-GPT: Towards A Foundation Model for EEG](https://mlsanity.com/paper/2311.03764/) (2023) - [BrainBERT: Self-supervised representation learning for intracranial recordings](https://mlsanity.com/paper/2302.14367/) (2023) - [EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model](https://mlsanity.com/paper/2401.10278/) (2024) - [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022) - [Inter-database validation of a deep learning approach for automatic sleep scoring](https://mlsanity.com/paper/2009.10365/) (2020) - [A review of feature extraction and performance evaluation in epileptic seizure detection using EEG](https://mlsanity.com/paper/1908.00492/) (2019) - [ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification](https://mlsanity.com/paper/1802.00308/) (2018) - [EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation](https://mlsanity.com/paper/2401.18006/) (2024) - [MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals](https://mlsanity.com/paper/2306.13102/) (2023) - [BIOT: Cross-data Biosignal Learning in the Wild](https://mlsanity.com/paper/2305.10351/) (2023) - [Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts](https://mlsanity.com/paper/2209.11233/) (2022) - [FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling](https://mlsanity.com/paper/2409.12454/) (2024) - [Prediction of Reaction Time and Vigilance Variability from Spatiospectral Features of Resting-State EEG in a Long Sustained Attention Task](https://mlsanity.com/paper/1910.10076/) (2019) - [Large Language Models Meet Computer Vision: A Brief Survey](https://mlsanity.com/paper/2311.16673/) (2023) - [GEFM: Graph-Enhanced EEG Foundation Model](https://mlsanity.com/paper/2411.19507/) (2024) - [Assessing Fatigue with Multimodal Wearable Sensors and Machine Learning](https://mlsanity.com/paper/2205.00287/) (2022) ## Built on by (most cited first, 10 of 10) - [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025) - [EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models](https://mlsanity.com/paper/2508.17742/) (2025) - [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026) - [NeuroTTT: Bridging Pretraining-Downstream Task Misalignment in EEG Foundation Models via Test-Time Training](https://mlsanity.com/paper/2509.26301/) (2025) - [BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals](https://mlsanity.com/paper/2510.02276/) (2025) - [A Systematic Evaluation of Self-Supervised Learning for Label-Efficient Sleep Staging with Wearable EEG](https://mlsanity.com/paper/2510.07960/) (2025) - [EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks](https://mlsanity.com/paper/2606.01767/) (2026) - [Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model](https://mlsanity.com/paper/2606.30104/) (2026) - [NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models](https://mlsanity.com/paper/2607.03925/) (2026) - [Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices](https://mlsanity.com/paper/2607.18149/) (2026) ## Abstract (arXiv) Electroencephalogram (EEG) signals play a crucial role in understanding brain activity and diagnosing neurological diseases. Because supervised EEG encoders are unable to learn robust EEG patterns and rely too heavily on expensive signal annotation, research has turned to general-purpose self-supervised EEG encoders, known as EEG-based models (EEG-FMs), to achieve robust and scalable EEG feature extraction. However, the readiness of early EEG-FMs for practical applications and the standards for long-term research progress remain unclear. Therefore, a systematic and comprehensive review of first-generation EEG-FMs is necessary to understand their current state-of-the-art and identify key directions for future EEG-FMs. To this end, this study reviews 14 early EEG-FMs and provides a critical comprehensive analysis of their methodologies, empirical findings, and unaddressed research gaps. This review focuses on the latest developments in EEG-based models (EEG-FMs), which have shown great potential for processing and analyzing EEG data. We discuss various EEG-FMs, including their architectures, pretraining strategies, pretraining and downstream datasets, and other details. This review also highlights challenges and future directions in the field, aiming to provide a comprehensive overview for researchers and practitioners interested in EEG analysis and related EEG-FM.