  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.
