Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep
Journal:
arXiv
Published Date:
Aug 1, 2024
Abstract
We explore the application of large language models (LLMs), pre-trained
models with massive textual data for detecting and improving these altered
states. We investigate the use of LLMs to estimate attention states, sleep
stages, and sleep quality and generate sleep improvement suggestions and
adaptive guided imagery scripts based on electroencephalogram (EEG) and
physical activity data (e.g. waveforms, power spectrogram images, numerical
features). Our results show that LLMs can estimate sleep quality based on human
textual behavioral features and provide personalized sleep improvement
suggestions and guided imagery scripts; however detecting attention, sleep
stages, and sleep quality based on EEG and activity data requires further
training data and domain-specific knowledge.
