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EEG-RAG is a Retrieval-Augmented Generation (RAG) system specifically designed for electroencephalography (EEG) research. It enables researchers, clinicians, and data scientists to ask natural language questions about EEG literature and receive evidence-based answers with proper citations.
This project implements PDDL-INSTRUCT with Logical Chain-of-Thought (LCoT), a novel approach to improve Large Language Model (LLM) performance on automated planning tasks. The system enhances planning capabilities through: