Spectral Analysis of the EEG of Subjects with Anxious-Phobic Disorders in a Virtual Reality Environment

Tychkov, Alexander Yu.; Chernyshov, Denis S.; Bofonova, N. S.; Churakov, P. P.; Yuldashev, Z. M.; Gorbunov, V. N.; Zolotarev, R. V.; Alimuradov, Alan K.

doi:10.1007/s10527-023-10250-6

Cited by 2 publications

(4 citation statements)

References 6 publications

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Section: Resultsmentioning

confidence: 99%

“…Compared to Study Tychkov et al ( 2023 ), which explores the use of spectral analysis of EEG power for assessing psychoemotional states in acrophobia using VR technology, our proposed TimeSeries CNN and ANN models offer complementary approaches for fear level classification. While Study Tychkov et al ( 2023 ) focuses on spectral analysis of EEG power to identify markers for anxious-phobic disorders, our models directly classify fear levels based on EEG signals. Our models demonstrate superior accuracy rates for fear level prediction, particularly achieving high accuracy for “Level 1” and “Level 3.” However, similar to Study Tychkov et al ( 2023 ), our models may encounter challenges in distinguishing between closely related emotional states, as evidenced by misclassifications between adjacent fear levels.…”

Section: Resultsmentioning

confidence: 99%

“…While Study Tychkov et al ( 2023 ) focuses on spectral analysis of EEG power to identify markers for anxious-phobic disorders, our models directly classify fear levels based on EEG signals. Our models demonstrate superior accuracy rates for fear level prediction, particularly achieving high accuracy for “Level 1” and “Level 3.” However, similar to Study Tychkov et al ( 2023 ), our models may encounter challenges in distinguishing between closely related emotional states, as evidenced by misclassifications between adjacent fear levels.…”

Section: Resultsmentioning

confidence: 99%

“…Based on the effect sizes observed in Table 2 and considering the number of subjects in each study, our sample size of 18 subjects appears to be sufficient for the current study. While Apicella et al ( 2023 ) and Tychkov et al ( 2023 ) analyzed smaller samples of 8 and 12 subjects, respectively, Bălan et al ( 2020 ) and Balan et al ( 2019 ) employed similar or smaller sample sizes of 8 and 4 subjects, respectively. Despite variations in sample sizes across studies, the effect sizes reported provide valuable context for our own research.…”

Section: Methodology and Implementationmentioning

confidence: 99%

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Analyzing EEG patterns in young adults exposed to different acrophobia levels: a VR study

Russo,

Tibermacine,

Tibermacine

et al. 2024

Front. Hum. Neurosci.

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IntroductionThe primary objective of this research is to examine acrophobia, a widely prevalent and highly severe phobia characterized by an overwhelming dread of heights, which has a substantial impact on a significant proportion of individuals worldwide. The objective of our study was to develop a real-time and precise instrument for evaluating levels of acrophobia by utilizing electroencephalogram (EEG) signals.MethodsEEG data was gathered from a sample of 18 individuals diagnosed with acrophobia. Subsequently, a range of classifiers, namely Support Vector Classifier (SVC), K-nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Adaboost, Linear Discriminant Analysis (LDA), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN), were employed in the analysis. These methodologies encompass both machine learning (ML) and deep learning (DL) techniques.ResultsThe Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) models demonstrated notable efficacy. The Convolutional Neural Network (CNN) model demonstrated a training accuracy of 96% and a testing accuracy of 99%, whereas the Artificial Neural Network (ANN) model attained a training accuracy of 96% and a testing accuracy of 97%. The findings of this study highlight the effectiveness of the proposed methodology in accurately categorizing real-time degrees of acrophobia using EEG data. Further investigation using correlation matrices for each level of acrophobia showed substantial EEG frequency band connections. Beta and Gamma mean values correlated strongly, suggesting cognitive arousal and acrophobic involvement could synchronize activity. Beta and Gamma activity correlated strongly with acrophobia, especially at higher levels.DiscussionThe results underscore the promise of this innovative approach as a dependable and sophisticated method for evaluating acrophobia. This methodology has the potential to make a substantial contribution toward the comprehension and assessment of acrophobia, hence facilitating the development of more individualized and efficacious therapeutic interventions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodology and Implementationmentioning

confidence: 99%

See 2 more Smart Citations

Analyzing EEG patterns in young adults exposed to different acrophobia levels: a VR study

Russo,

Tibermacine,

Tibermacine

et al. 2024

Front. Hum. Neurosci.

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Domain Adaptation for Fear of Heights Classification in a VR Environment Based on EEG and ECG

Apicella,

Arpaia,

Barbato

et al. 2024

Inf Syst Front

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Three levels of fear of heights were detected in subjects with different severities of acrophobia, based on the electroencephalographic (EEG) and electrocardiographic (ECG) signals. The study aims to demonstrate the feasibility of a data-fusion-based method for real-time assessment of the fear of heights intensity to integrate into adaptive Virtual Reality Exposure Therapy for acrophobia. The generalization performance of classification tasks on fear states is improved by exploiting both trait-based clustering and Domain Adaptation methods. Participants were gradually exposed to increasing height levels through a Virtual Reality (VR) scenario representing a canyon. The initial severity of fear of heights, the level of distress at each height, and the anxiety level before and after the exposure were assessed through the Acrophobia Questionnaire, the Subjective Unit of Distress, and the State and Trait Anxiety Inventory, respectively. The Simulator Sickness Questionnaire was administered to exclude possible motion sickness interference in the experiment. The EEG and ECG signals were acquired through a 32-channel headset and 1 Lead ECG derivation during the exposure to the eliciting VR scenario. Four classifiers (i.e. Support Vector Machines, Deep Neural Networks, Random Forests, and k-Nearest Neighbors) were adopted in the experimental environment. Preliminary tests were performed in a within-subject experiment, achieving the best classification accuracy of $$87.1 \% \pm 7.8 \%$$ 87.1 % ± 7.8 % with a Deep Neural Network. As the cross-subject approach is concerned, three strategies, namely Domain Adaptation (DA), data fusion (combining EEG with ECG), and participant clustering (based on the acrophobia severity), were evaluated. DA resulted in the most effective strategies by determining an improvement of more than 20 % in classification accuracy. Random Forest performed the best classification accuracy for the severe acrophobia cluster with a mean of $$63.6 \%$$ 63.6 % and a standard deviation of $$ 13.4 \%$$ 13.4 % over three classes by exploiting Stratified Normalization.

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Spectral Analysis of the EEG of Subjects with Anxious-Phobic Disorders in a Virtual Reality Environment

Cited by 2 publications

References 6 publications

Analyzing EEG patterns in young adults exposed to different acrophobia levels: a VR study

Analyzing EEG patterns in young adults exposed to different acrophobia levels: a VR study

Domain Adaptation for Fear of Heights Classification in a VR Environment Based on EEG and ECG

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