EEG-Driven Personal Comfort Model for Cognitive Efficiency in Human-Centric Environments

Abstract

This study aims to develop a personal comfort model driven by real-time electroencephalogram (EEG) signals for constructing built environments customized to individual emotional states and preferences. EEG signals from a single subject were collected at regular intervals under controlled environmental conditions-temperature, humidity, and illumination. Real-time deep learning methods processed the sensor data, enabling effective prediction of the user's preferred conditions. Model evaluation showed reliable predictions on the personal dataset, allowing for optimized lighting that enhanced concentration and reduced stress. These findings indicate that EEG can inform personalized environmental modifications. This integration of EEG and deep learning provides objective, precise comfort assessment and supports immediate environmental adaptation.