# Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep

arXiv: https://arxiv.org/abs/2408.07822 · HTML: https://mlsanity.com/paper/2408.07822/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [Neuro-GPT: Towards A Foundation Model for EEG](https://mlsanity.com/paper/2311.03764/) (2023)
- [Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data](https://mlsanity.com/paper/2401.06866/) (2024)
- [EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation](https://mlsanity.com/paper/2401.18006/) (2024)

## Built on by (most cited first, 4 of 4)

- [NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences](https://mlsanity.com/paper/2503.07599/) (2025)
- [Large Language Models for EEG: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2506.06353/) (2025)
- [Foundation Models for Cross-Domain EEG Analysis Application: A Survey](https://mlsanity.com/paper/2508.15716/) (2025)
- [Smarter Together: Combining Large Language Models and Small Models for Physiological Signals Visual Inspection](https://mlsanity.com/paper/2501.16215/) (2025)

## Abstract (arXiv)

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, 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.