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

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 …

brain capabilities classification cs.lg eeg eess.sp focus global gpt interpretation language language models large language large language models machine machine learning q-bio.qm sleep spatial temporal