Large Language Models for EEG: A Comprehensive Survey and Taxonomy
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916786846302208 |
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| author | Babu, Naseem Mathew, Jimson Vinod, A. P. |
| author_facet | Babu, Naseem Mathew, Jimson Vinod, A. P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06353 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Models for EEG: A Comprehensive Survey and Taxonomy Babu, Naseem Mathew, Jimson Vinod, A. P. Signal Processing Artificial Intelligence Emerging Technologies Human-Computer Interaction Machine Learning 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. |
| title | Large Language Models for EEG: A Comprehensive Survey and Taxonomy |
| topic | Signal Processing Artificial Intelligence Emerging Technologies Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2506.06353 |