Large Language Models for EEG: A Comprehensive Survey and Taxonomy

Abstract--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 recent studies into four categories: (1) LLM-inspired foundation models for EEG analysis, (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 presenting a structured overview of the employed models and application domains, this survey establishes a comprehensive framework to advance neural signal analysis through the application of language models. The growing use of language models is shaping new developments in both neuroscience and artificial intelligence. Language models such as GPT, BERT [1], [2], [3], and their multi-modal variants have shown remarkable success across tasks involving natural language processing and understanding, generation, and even vision-language fusion. The transformer-based architecture enables these models to effectively process sequential data, contributing to their success across a range of domains beyond natural language processing.