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

• URL: http://arxiv.org/abs/2401.18006v2
• Date: Sat, 3 Feb 2024 23:32:08 GMT
• Title: EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation
• Authors: Jonathan W. Kim and Ahmed Alaa and Danilo Bernardo
• Abstract summary: We propose EEG-GPT, a unifying approach to EEG classification that leverages advances in large language models (LLM) EEG-GPT achieves excellent performance comparable to current state-of-the-art deep learning methods in classifying normal from abnormal EEG in a few-shot learning paradigm utilizing only 2% of training data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In conventional machine learning (ML) approaches applied to electroencephalography (EEG), this is often a limited focus, isolating specific brain activities occurring across disparate temporal scales (from transient spikes in milliseconds to seizures lasting minutes) and spatial scales (from localized high-frequency oscillations to global sleep activity). This siloed approach limits the development EEG ML models that exhibit multi-scale electrophysiological understanding and classification capabilities. Moreover, typical ML EEG approaches utilize black-box approaches, limiting their interpretability and trustworthiness in clinical contexts. Thus, we propose EEG-GPT, a unifying approach to EEG classification that leverages advances in large language models (LLM). EEG-GPT achieves excellent performance comparable to current state-of-the-art deep learning methods in classifying normal from abnormal EEG in a few-shot learning paradigm utilizing only 2% of training data. Furthermore, it offers the distinct advantages of providing intermediate reasoning steps and coordinating specialist EEG tools across multiple scales in its operation, offering transparent and interpretable step-by-step verification, thereby promoting trustworthiness in clinical contexts.

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