RT Journal Article
SR Electronic
T1 Identifying Uncertainty States during Wayfinding in Indoor Environments: An EEG Classification Study
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.12.14.453704
DO 10.1101/2021.12.14.453704
A1 Zhu, Bingzhao
A1 Cruz-Garza, Jesus G.
A1 Shoaran, Mahsa
A1 Kalantari, Saleh
YR 2021
UL http://biorxiv.org/content/early/2021/12/16/2021.12.14.453704.abstract
AB 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 StatementThe authors have declared no competing interest.