Human and indoor environment interaction through EEG-based methods and implications in life cycle perspective

• Main Author: Shan, Xin
• Other Authors: Yang En-Hua
• Format: Theses and Dissertations
• Language: English
• Published: 2018
• Subjects: DRNTU::Engineering::Civil engineering
• Online Access: http://hdl.handle.net/10356/74101
• Institution: Nanyang Technological University

This research has the long-term vision that the wellbeing and work productivity of building occupants should be incorporated into the more balanced building design and operation. To achieve this vision, the thesis focused on improving the understanding and implementation of the two-way human-building interaction, i.e. the impact of indoor environment (thermal environment and indoor air quality) on occupants and occupants‘ feedback to the building. The core of this research established EEG (electroencephalogram) based methods for a potentially more accurate and objective human-building interaction. Specifically, the impacts of indoor environment on occupants‘ wellbeing and performance were more objectively and accurately quantified by EEG indices, namely asymmetrical activity and frequency bands. These indices also helped to explain and correlate with traditional subjective indicators and task-based indicators. Machine learning-based EEG methods in human-computer interaction domain were also explored in this research. Together with EEG indices, the machine learning-based EEG methods can be the main feedback mechanism of wellbeing and performance to the building. Furthermore, the incorporation of the wellbeing and performance of occupants into the building life cycle platform was also explored and extended to other building types other than office buildings, namely the educational buildings. Both a better human-building interaction through EEG-based methods and the perspective through life cycle platform can contribute to the incorporation of occupants‘ wellbeing and performance into the more balanced building design and operation.