The application in electroencephalogram technology for investigating indoor lighting environments: A review

警觉 脑电图 计算机科学 电灯 可用性 照度 人机交互 大脑活动与冥想 模拟 心理学 工程类 神经科学 物理 电气工程 天文 精神科
作者
Nan Zhang,Chao Liu,Caixia Hou,Ziyan Wang,Xinyu He,Weijun Gao
出处
期刊:Journal of building engineering [Elsevier]
卷期号:94: 110020-110020 被引量:1
标识
DOI:10.1016/j.jobe.2024.110020
摘要

The study of indoor light environments is crucial for achieving a balance between user comfort and building energy efficiency. Electroencephalogram (EEG), known for its non-invasiveness and high temporal resolution, has emerged as a valuable tool in this field, allowing for the visualization of brain activity. This review synthesizes findings from 52 studies conducted over the past two decades that utilized EEG technology to investigate the indoor light environment. It specifically examines the impacts of light spectrum, illuminance, correlated color temperature (CCT), and lighting setup on human brain activity. Furthermore, the review addresses factors influencing the evaluation of the light environment and proposes strategies for improvement and future research directions. The review reveals that a significant majority (92.31 %) of the studies were experimental studies, with limited emphasis on practical applications. Approximately 78.85 % of experimental studies were carried out in controlled chambers, with young college students as the primary participants, and the male to female ratio was 1.1/1. Blue light, high illuminance, and high CCT significantly inhibited low-frequency EEG activity (δ, θ, and α) and activated high-frequency EEG activity (β and γ), contributing to increased alertness and improved cognitive performance. To enhance the precision of EEG-based assessments of indoor light environments, it is imperative to consider a broader range of environmental, personal, and technical factors, and to employ standardized data collection and processing techniques. Additionally, future research should focus on expanding the application of EEG in indoor lighting design and creating personalized light comfort models and comprehensive high-quality datasets.
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