Classification of pleasantness of wind by electroencephalography

Thermal comfort of humans depends on the surrounding environment and affects their productivity. Several environmental factors, such as air temperature, relative humidity, wind or airflow, and radiation, have considerable influence on the thermal comfort or pleasantness; hence, these are generally c...

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Bibliographic Details
Published in:PloS one 2024-02, Vol.19 (2), p.e0299036-e0299036
Main Authors: Maruyama, Yasuhisa, Nakamura, Ryuto, Tsuji, Shota, Xuan, Yingli, Mizutani, Kunio, Okaze, Tsubasa, Yoshimura, Natsue
Format: Article
Language:English
Subjects:
Air flow
Analysis
Biology and Life Sciences
Computer and Information Sciences
Earth Sciences
Electrical equipment and supplies
Electrical machinery
Electroencephalography
Evaluation
Influence
Machine learning
Medicine and Health Sciences
Neural networks
Research and Analysis Methods
Social Sciences
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Summary:Thermal comfort of humans depends on the surrounding environment and affects their productivity. Several environmental factors, such as air temperature, relative humidity, wind or airflow, and radiation, have considerable influence on the thermal comfort or pleasantness; hence, these are generally controlled by electrical devices. Lately, the development of objective measurement methods for thermal comfort or pleasantness using physiological signals is receiving attention to realize a personalized comfortable environment through the automatic control of electrical devices. In this study, we focused on electroencephalography (EEG) and investigated whether EEG signals contain information related to the pleasantness of ambient airflow reproducing natural wind fluctuations using machine learning methods. In a hot and humid artificial climate chamber, we measured EEG signals while the participants were exposed to airflow at four different velocities. Based on the reported pleasantness levels, we performed within-participant classification from the source activity of the EEG and obtained a classification accuracy higher than the chance level using both linear and nonlinear support vector machine classifiers as well as an artificial neural network. The results of this study showed that EEG is useful in identifying people's transient pleasantness when exposed to wind.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0299036