mlsanityData to 14 Oct 2026

Large Language Models for EEG: A Comprehensive Survey and Taxonomy

arXiv 2506.06353 ↗First posted 2 Jun 2025eess.SP cs.AI cs.ETcited by 5

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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.

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

40 of 104 references.

References · 40

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
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28 more
  1. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 163
  2. 2020A Review of Generalized Zero-Shot Learning Methodscited by 159
  3. 2021BERT: A Review of Applications in Natural Language Processing and Understandingcited by 109
  4. 2018The Temple University Hospital Seizure Detection Corpuscited by 83
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  6. 2019ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotationcited by 66
  7. 2018Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Featurescited by 52
  8. 2024EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Modelcited by 52
  9. 2018EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalscited by 48
  10. 2023DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translationcited by 36
  11. 2024EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretationcited by 27
  12. 2024EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformercited by 20
  13. 2024Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)cited by 18
  14. 2024Exploring Large-Scale Language Models to Evaluate EEG-Based Multimodal Data for Mental Healthcited by 13
  15. 2023Large Transformers are Better EEG Learnerscited by 10
  16. 2024BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decodingcited by 10
  17. 2025Large Cognition Model: Towards Pretrained EEG Foundation Modelcited by 10
  18. 2023Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decodingcited by 9
  19. 2025EEG-CLIP : Learning EEG representations from natural language descriptionscited by 9
  20. 2024From Word Embedding to Reading Embedding Using Large Language Model, EEG and Eye-trackingcited by 6
  21. 2024EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotypingcited by 6
  22. 2024SEE: Semantically Aligned EEG-to-Text Translationcited by 6
  23. 2024EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record Generationcited by 6
  24. 2024Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video Understandingcited by 4
  25. 2024Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleepcited by 4
  26. 2025Mind2Matter: Creating 3D Models from EEG Signalscited by 4
  27. 2023Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehensioncited by 2
  28. 2024EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Modelcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learningcited by 10
  2. 2025Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologiescited by 5
  3. 2026E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysiscited by 0
  4. 2026Large language models in medical time series analysiscited by 0
  5. 2026Lightweight Semantic EEG Foundation Model for Frozen Cross-Disorder Transfercited by 0