Year: 2025
Type: article
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.
Source: Buildings
Authors: Se Yeon Kang, Ju Eun Cho, Han Jong Jun
Institution: Hanyang University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G3725632971
OpenAlex ID: W4414199718