EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation

Deep learning has the potential for advancing EEG analysis and interpretation, however widespread implementation has been constrained by the need for extensive labeled datasets and limited transparency. In contrast, generative pretrained transformer (GPT) architectures have enabled efficient learning of small datasets through fine-tuning and also offer verifiable chain-of-thought reasoning in agentic frameworks. Here, we present EEG-GPT, a framework for EEG classification fine-tuned on publicly available large-language models. We benchmark EEG-GPT on the Temple University Hospital (TUH) EEG corpus in a few-shot regime. Utilizing only 2 % of the training data, EEG-GPT classifies normal versus abnormal EEG recordings with AUROC of 0.86, comparable to state-of-the-art deep learning methods. In addition, EEG-GPT demonstrates the capability to be used as an artificial intelligence (AI) agent, orchestrating usage of specialized EEG tools while providing step-by-step verifiability of its reasoning steps. These results underscore GPTs potential to advance EEG analysis and interpretation, highlighting their efficient learning in small data regimes and their potential to automate EEG analysis in a verifiable manner.