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.

EEG-GPT 是一种统一的脑电图分类方法，利用大型语言模型的进展实现了在多尺度脑电生理理解和分类能力方面出色的性能，同时提供透明和可解释的逐步验证过程，从而在临床环境中提高了可信度。