Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140866
Title: Human-building interaction under various indoor temperatures through neural-signal electroencephalogram (EEG) methods
Authors: Shan, Xin
Yang, En-Hua
Zhou, Jin
Chang, Victor Wei-Chung
Keywords: Engineering::Civil engineering
Issue Date: 2017
Source: Shan, X., Yang, E.-H., Zhou, J., & Chang, V. W.-C. (2018). Human-building interaction under various indoor temperatures through neural-signal electroencephalogram (EEG) methods. Building and Environment, 129, 46-53. doi:10.1016/j.buildenv.2017.12.004
Journal: Building and Environment
Abstract: In this study, potential of neural-signal electroencephalogram (EEG)-based methods for enhancing human-building interaction under various indoor temperatures were explored. Correlations between EEG and subjective perceptions/tasks performance were experimentally investigated. Machine learning-based EEG pattern recognition was further studied. Results showed that the EEG frontal asymmetrical activity related well to the subjective questionnaire and objective tasks performance, which can be used as a more objective metric to corroborate traditional subjective questionnaire-based methods and task-based methods. Machine learning-based EEG pattern recognition with linear discriminant analysis (LDA) classifiers can well classify the different mental states under different thermal conditions. Utilization of the EEG frontal asymmetrical activities and the machine learning-based EEG pattern recognition method as a feedback mechanism of occupants, which can be implemented on a routine basis, has a great potential to enhance the human-building interaction in a more objective and holistic way.
URI: https://hdl.handle.net/10356/140866
ISSN: 0360-1323
DOI: 10.1016/j.buildenv.2017.12.004
Rights: © 2017 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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