Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep (opens in new tab)

arXiv:2408.07822v2 Announce Type: replace-cross Abstract: We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving attention and sleep. 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, numeri...