EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation
This addresses the need for more interpretable and trustworthy EEG classification tools in clinical contexts, though it is an incremental application of existing LLM technology to a new domain.
The authors tackled the problem of limited multi-scale understanding and interpretability in EEG classification by proposing EEG-GPT, a method leveraging large language models, which achieved performance comparable to state-of-the-art deep learning methods using only 2% of training data in a few-shot learning paradigm.
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.