Identifying uncertainty states during wayfinding in indoor environments: An EEG classification study |
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| Affiliation: | 1. College of Management and Economics, Tianjin University, Tianjin 300072, China;2. Department of Civil and Environmental Engineering, University of Alberta, Edmonton T6G 2R3, Canada;3. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region;1. School of Management, Northwestern Polytechnical University, Xi’an, PR China;2. Mechanical Engineering and Design Department, Université de Bourgogne Franche-Comté, Université de technologie de Belfort-Montbéliard, Belfort Cedex, France;3. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, PR China;4. Guangdong Provincial Key Laboratory of Advanced Welding Technology for Ships, CSSC Huangpu Wenchong Shipbuilding Company Limited, Guangzhou, PR China;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore;3. Department of Mechanical and Electromechanical Engineering, National ILan University, ILan 26041, Taiwan;4. Huazhong University of Science and Technology – Wuxi Research Institute, Wuxi 214000, China;5. School of Mechanical Engineering, Hubei University of Technology, Wuhan 430072, China;1. School of Economics and Management, Beihang University, Beijing 100191, China;2. Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education, Beijing 100191, China;3. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China |
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| Abstract: | The researchers used a machine-learning classification approach to better understand neurological features associated with periods of wayfinding uncertainty. The participants (n = 30) were asked to complete wayfinding tasks of varying difficulty in a virtual reality (VR) hospital environment. Time segments when participants experienced navigational uncertainty were first identified using a combination of objective measurements (frequency of inputs into the VR controller) and behavioral annotations from two independent observers. Uncertainty time-segments during navigation were ranked on a scale from 1 (low) to 5 (high). The machine-learning model, a Random Forest classifier implemented using scikit-learn in Python, was used to evaluate common spatial patterns of EEG spectral power across the theta, alpha, and beta bands associated with the researcher-identified uncertainty states. The overall predictive power of the resulting model was 0.70 in terms of the area under the Receiver Operating Characteristics curve (ROC-AUC). These findings indicate that EEG data can potentially be used as a metric for identifying navigational uncertainty states, which may provide greater rigor and efficiency in studies of human responses to architectural design variables and wayfinding cues. |
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| Keywords: | Wayfinding Uncertainty Mobile brain/body imaging Architectural design Classification |
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