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.

翻译：传统机器学习（ML）方法应用于脑电图（EEG）时，往往局限于对特定脑活动的研究，这些活动发生在不同的时间尺度（从持续毫秒的瞬时尖峰到持续数分钟的癫痫发作）和空间尺度（从局部高频振荡到全局睡眠活动）。这种孤立的方式限制了具备多尺度电生理学理解与分类能力的EEG机器学习模型的发展。此外，典型的EEG机器学习方法采用黑箱模型，限制了其在临床情境下的可解释性与可信度。因此，我们提出EEG-GPT，一种利用大型语言模型（LLM）进展的统一EEG分类方法。EEG-GPT在少样本学习范式下，仅使用2%的训练数据，即在区分正常与异常EEG方面取得了与当前最先进深度学习方法相媲美的优异性能。此外，它具备独特优势：提供中间推理步骤，并在运行中协调多尺度的专业EEG工具，实现透明且可解释的逐步验证，从而提升其在临床语境中的可信度。