TY - GEN
T1 - Integrating Biometric Sensors, VR, and Machine Learning to Classify EEG Signals in Alternative Architecture Designs
AU - Zou, Zhengbo
AU - Yu, Xinran
AU - Ergan, Semiha
N1 - Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2019
Y1 - 2019
N2 - Design of office spaces plays an essential role in people's day-to-day work productivity. Research in environmental psychology and neuroscience indicates distinct architectural design features (e.g., color coding, texture, and space layouts, etc.) impact human performance and motivation to work in office spaces. In the current practice, occupants evaluate work space designs via after-the-fact post-construction surveys subjectively. Limited studies exist in the literature on objectively quantifying motivational impact of space design on occupants. This research stems from the need for having objective ways to assess human experience in the built environment for design improvement. Integration of electro-encephalograph (EEG) and virtual reality (VR) equips researchers with the tools to measure human responses when subjects are immersed in alternative virtual designed spaces. This study proposed a machine learning based method to label subjects' experience in spaces using their EEG data collected when they were in distinctly designed spaces. Results showed this method provided around 85% classification accuracy, which is comparable to other state-of-the-art EEG classification methods. Practitioners in the architecture engineering and construction (AEC) domain can use this method to identify if proposed design options have positive or negative impacts on future occupants.
AB - Design of office spaces plays an essential role in people's day-to-day work productivity. Research in environmental psychology and neuroscience indicates distinct architectural design features (e.g., color coding, texture, and space layouts, etc.) impact human performance and motivation to work in office spaces. In the current practice, occupants evaluate work space designs via after-the-fact post-construction surveys subjectively. Limited studies exist in the literature on objectively quantifying motivational impact of space design on occupants. This research stems from the need for having objective ways to assess human experience in the built environment for design improvement. Integration of electro-encephalograph (EEG) and virtual reality (VR) equips researchers with the tools to measure human responses when subjects are immersed in alternative virtual designed spaces. This study proposed a machine learning based method to label subjects' experience in spaces using their EEG data collected when they were in distinctly designed spaces. Results showed this method provided around 85% classification accuracy, which is comparable to other state-of-the-art EEG classification methods. Practitioners in the architecture engineering and construction (AEC) domain can use this method to identify if proposed design options have positive or negative impacts on future occupants.
UR - http://www.scopus.com/inward/record.url?scp=85068805269&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068805269&partnerID=8YFLogxK
U2 - 10.1061/9780784482421.022
DO - 10.1061/9780784482421.022
M3 - Conference contribution
AN - SCOPUS:85068805269
T3 - Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
SP - 169
EP - 176
BT - Computing in Civil Engineering 2019
A2 - Cho, Yong K.
A2 - Leite, Fernanda
A2 - Behzadan, Amir
A2 - Wang, Chao
PB - American Society of Civil Engineers (ASCE)
T2 - ASCE International Conference on Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation, i3CE 2019
Y2 - 17 June 2019 through 19 June 2019
ER -