Large Language Models for EEG: A Comprehensive Survey and Taxonomy
This work provides a structured taxonomy for researchers in EEG, brain-computer interfaces, and affective computing, but it is incremental as it surveys existing literature rather than introducing new methods.
This survey systematically reviews and organizes recent advancements in using Large Language Models (LLMs) for EEG-based analysis, covering domains like representation learning, EEG-to-language decoding, cross-modal generation, and clinical applications, to serve as a foundational resource for integrating natural language processing with neural signal analysis.
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.