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

Large language models (LLMs) such as ChatGPT have garnered substantial attention in the media and among the machine learning (ML) community. LLMs represent a pivotal paradigm shift in artificial intelligence (AI), consisting of transformer architectures substantially larger in scale compared to their predecessors, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks [1], and leverage internet-scale text corpora, thus excelling not only on text completion tasks but demonstrating emergent capabilities in rudimentary language reasoning [2, 3]. LLMs display several features conducive to the small data regime present in most EEG datasets, where the largest datasets typically have on the order of only thousands of EEGs. Primarily, LLMs have the capability to perform fewand even zero-shot learning [4]. Recent research has investigated how LLMs can perform few-shot learning in domains ranging from cancer drug synergy prediction to cardiac signal analysis [5, 6]. Other work has demonstrated the ability of LLMs to outperform experts in annotating political Twitter messages with zero-shot learning [7]. Additionally, previous work has shown that transformer architectures are capable of utilizing in-context learning for zero-shot tasks - in other words, utilizing information provided in the prompt in order to yield better performance on various tasks [8].