Are you afraid of heights and suitable for working at height?

Wang, Hong; Wang, Qiaoxiu; Hu, Fo

doi:10.1016/j.bspc.2019.03.011

Cited by 12 publications

(8 citation statements)

References 33 publications

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“…Fear of heights makes the brain think that visual information is insufficient and difficult to judge. Therefore, people with acrophobia will have excessive self-protection and abnormal fear [39,47,48]. According to researches on motor imaginary, the central area of the cerebral cortex is closely related to body movement.…”

Section: Topological Features Vs Community Structure Featuresmentioning

confidence: 99%

Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia

Wang

et al. 2021

J. Neural Eng.

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Objective. The prevalence of acrophobia is high, especially with the rise of many high-rise buildings. In the recent few years, researchers have begun to analyze acrophobia from the neuroscience perspective, especially to improve the virtual reality exposure therapy (VRET). Electroencephalographic (EEG) is an informative neuroimaging technique, but it is rarely used for acrophobia. The purpose of this study is to evaluate the effectiveness of using EEGs to identify the degree of acrophobia objectively. Approach. EEG data were collected by virtual reality (VR) exposure experiments. We classified all subjects’ degrees of acrophobia into three categories, where their questionnaire scores and behavior data showed significant differences. Using synchronization likelihood, we computed the functional connectivity between each pair of channels and then obtained complex networks named functional brain networks (FBNs). Basic topological features and community structure features were extracted from the FBNs. Statistical results demonstrated that FBN features can be used to distinguish different groups of subjects. We trained machine learning (ML) algorithms with FBN features as inputs and trained convolutional neural networks (CNNs) with FBNs directly as inputs. Main results. It turns out that using FBN to identify the severity of acrophobia is feasible. For ML algorithms, the community structure features of some cerebral cortex regions outperform typical topological features of the whole brain, in terms of classification accuracy. The performances of CNN algorithms are better than ML algorithms. The CNN with ResNet performs the best (accuracy reached 98.46 ± 0.42%). Significance. These observations indicate that community structures of certain cerebral cortex regions could be used to identify the degree of acrophobia. The proposed CNN framework can provide objective feedback, which could help build closed-loop VRET portable systems.

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Section: Topological Features Vs Community Structure Featuresmentioning

confidence: 99%

Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia

Wang

et al. 2021

J. Neural Eng.

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“…Studies demonstrate that people uploaded at VR may be afraid of walking across a wooden plank that is on the ground in the real world but placed in virtual reality between roofs of skyscrapers. The experimental environment forces similar psycho-emotional reactions of a probationer as if it happens in the real world [18]. Dizziness, nausea, stomachache, and shaking body are just a few of the reactions to virtual heights [17].…”

Section: Vr As a Potential Tool To Commit A Crimementioning

confidence: 99%

New Properties of Crimes in Virtual Environments

Дремлюга¹,

Prisekina²,

Yakovenko³

2020

Adv. sci. technol. eng. syst. j.

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“…Over the last five years, there have been significant advances in stress assessment through VR environments. Nonetheless, only a few have utilized multiple biomarkers including EEG to evaluate stress [ 20 , 21 , 22 , 23 ]. Stolz et al utilized a VR room with avatars with different facial expressions and different sounds within a threat-conditioned context and used Event-Related Potentials (ERP) of EEG to investigate cortical processing [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…In other research, Fadeev et al performed a small-scale ad hoc study examining stress, utilizing multiple VR scenarios and observing the EEG, respiration rate, and heart rate alterations [ 22 ]. Wang et al [ 23 ] used VR Richie’s Plank Experience and obtained EEG recordings. They then performed classification of the subjects based on their subjective evaluation of fear of heights by utilizing their EEG signal.…”

Section: Introductionmentioning

confidence: 99%

Assessing Electroencephalography as a Stress Indicator: A VR High-Altitude Scenario Monitored through EEG and ECG

Aspiotis

Miltiadous

Kalafatakis

et al. 2022

Sensors

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Over the last decade, virtual reality (VR) has become an increasingly accessible commodity. Head-mounted display (HMD) immersive technologies allow researchers to simulate experimental scenarios that would be unfeasible or risky in real life. An example is extreme heights exposure simulations, which can be utilized in research on stress system mobilization. Until recently, electroencephalography (EEG)-related research was focused on mental stress prompted by social or mathematical challenges, with only a few studies employing HMD VR techniques to induce stress. In this study, we combine a state-of-the-art EEG wearable device and an electrocardiography (ECG) sensor with a VR headset to provoke stress in a high-altitude scenarios while monitoring EEG and ECG biomarkers in real time. A robust pipeline for signal clearing is implemented to preprocess the noise-infiltrated (due to movement) EEG data. Statistical and correlation analysis is employed to explore the relationship between these biomarkers with stress. The participant pool is divided into two groups based on their heart rate increase, where statistically important EEG biomarker differences emerged between them. Finally, the occipital-region band power changes and occipital asymmetry alterations were found to be associated with height-related stress and brain activation in beta and gamma bands, which correlates with the results of the self-reported Perceived Stress Scale questionnaire.

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Are you afraid of heights and suitable for working at height?

Cited by 12 publications

References 33 publications

Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia

Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia

New Properties of Crimes in Virtual Environments

Assessing Electroencephalography as a Stress Indicator: A VR High-Altitude Scenario Monitored through EEG and ECG

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