Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Identifying Uncertainty States during Wayfinding in Indoor Environments: An EEG Classification Study

Bingzhao Zhu, View ORCID ProfileJesus G. Cruz-Garza, Mahsa Shoaran, View ORCID ProfileSaleh Kalantari
doi: https://doi.org/10.1101/2021.12.14.453704
Bingzhao Zhu
aSchool of Applied and Engineering Physics, Cornell University, Ithaca, 14850, NY, USA
cInstitute of Electrical Engineering and Center for Neuroprosthetics, EPFL, Geneva, 1202, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jesus G. Cruz-Garza
bDepartment of Design and Environmental Analysis, Cornell University, Ithaca, 14850, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jesus G. Cruz-Garza
Mahsa Shoaran
cInstitute of Electrical Engineering and Center for Neuroprosthetics, EPFL, Geneva, 1202, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Saleh Kalantari
bDepartment of Design and Environmental Analysis, Cornell University, Ithaca, 14850, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Saleh Kalantari
  • For correspondence: sk3268@cornell.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted December 16, 2021.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Identifying Uncertainty States during Wayfinding in Indoor Environments: An EEG Classification Study
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Identifying Uncertainty States during Wayfinding in Indoor Environments: An EEG Classification Study
Bingzhao Zhu, Jesus G. Cruz-Garza, Mahsa Shoaran, Saleh Kalantari
bioRxiv 2021.12.14.453704; doi: https://doi.org/10.1101/2021.12.14.453704
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Identifying Uncertainty States during Wayfinding in Indoor Environments: An EEG Classification Study
Bingzhao Zhu, Jesus G. Cruz-Garza, Mahsa Shoaran, Saleh Kalantari
bioRxiv 2021.12.14.453704; doi: https://doi.org/10.1101/2021.12.14.453704

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4222)
  • Biochemistry (9095)
  • Bioengineering (6733)
  • Bioinformatics (23916)
  • Biophysics (12066)
  • Cancer Biology (9484)
  • Cell Biology (13720)
  • Clinical Trials (138)
  • Developmental Biology (7614)
  • Ecology (11644)
  • Epidemiology (2066)
  • Evolutionary Biology (15459)
  • Genetics (10610)
  • Genomics (14281)
  • Immunology (9447)
  • Microbiology (22749)
  • Molecular Biology (9056)
  • Neuroscience (48811)
  • Paleontology (354)
  • Pathology (1478)
  • Pharmacology and Toxicology (2558)
  • Physiology (3817)
  • Plant Biology (8299)
  • Scientific Communication and Education (1466)
  • Synthetic Biology (2285)
  • Systems Biology (6163)
  • Zoology (1295)