Large Language Models for EEG: A Comprehensive Survey and Taxonomy

TL;DR

This survey reviews how large language models are being integrated with EEG analysis, highlighting recent advancements, methodologies, and applications across multiple domains including decoding, generation, and clinical use.

Contribution

It provides a comprehensive taxonomy and systematic overview of recent LLM-based EEG research, organizing diverse approaches and applications in a structured manner.

Findings

Transformer architectures enable complex EEG tasks

LLMs facilitate EEG-to-language decoding and synthesis

Clinical applications benefit from LLM-driven analysis

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 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.