# Large Language Models for EEG: A Comprehensive Survey and Taxonomy

arXiv: https://arxiv.org/abs/2506.06353 · HTML: https://mlsanity.com/paper/2506.06353/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 104 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 30 of 40)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [Large Language Model Alignment: A Survey](https://mlsanity.com/paper/2309.15025/) (2023)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [BERT: A Review of Applications in Natural Language Processing and Understanding](https://mlsanity.com/paper/2103.11943/) (2021)
- [The Temple University Hospital Seizure Detection Corpus](https://mlsanity.com/paper/1801.08085/) (2018)
- [Deep Learning Human Mind for Automated Visual Classification](https://mlsanity.com/paper/1609.00344/) (2016)
- [ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation](https://mlsanity.com/paper/1912.00903/) (2019)
- [Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features](https://mlsanity.com/paper/1810.10974/) (2018)
- [EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model](https://mlsanity.com/paper/2401.10278/) (2024)
- [EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals](https://mlsanity.com/paper/1806.01875/) (2018)
- [DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation](https://mlsanity.com/paper/2309.14030/) (2023)
- [EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation](https://mlsanity.com/paper/2401.18006/) (2024)
- [EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer](https://mlsanity.com/paper/2405.02165/) (2024)
- [Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)](https://mlsanity.com/paper/2410.07507/) (2024)
- [Exploring Large-Scale Language Models to Evaluate EEG-Based Multimodal Data for Mental Health](https://mlsanity.com/paper/2408.07313/) (2024)
- [Large Transformers are Better EEG Learners](https://mlsanity.com/paper/2308.11654/) (2023)
- [BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding](https://mlsanity.com/paper/2409.00121/) (2024)
- [Large Cognition Model: Towards Pretrained EEG Foundation Model](https://mlsanity.com/paper/2502.17464/) (2025)
- [Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding](https://mlsanity.com/paper/2312.09430/) (2023)

## Built on by (most cited first, 5 of 5)

- [Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning](https://mlsanity.com/paper/2509.24222/) (2025)
- [Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies](https://mlsanity.com/paper/2510.22095/) (2025)
- [E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis](https://mlsanity.com/paper/2601.07877/) (2026)
- [Large language models in medical time series analysis](https://mlsanity.com/paper/2609.22262/) (2026)
- [Lightweight Semantic EEG Foundation Model for Frozen Cross-Disorder Transfer](https://mlsanity.com/paper/2610.05503/) (2026)

## Abstract (arXiv)

The growing convergence between Large Language Models (LLMs) and electroencephalography (EEG) research is enabling new directions in neural decoding, brain-computer interfaces (BCIs), and affective computing. This survey offers a systematic review and structured taxonomy of recent advancements that utilize LLMs for EEG-based analysis and applications. We organize the literature into four domains: (1) LLM-inspired foundation models for EEG representation learning, (2) EEG-to-language decoding, (3) cross-modal generation including image and 3D object synthesis, and (4) clinical applications and dataset management tools. The survey highlights how transformer-based architectures adapted through fine-tuning, few-shot, and zero-shot learning have enabled EEG-based models to perform complex tasks such as natural language generation, semantic interpretation, and diagnostic assistance. By offering a structured overview of modeling strategies, system designs, and application areas, this work serves as a foundational resource for future work to bridge natural language processing and neural signal analysis through language models.