Modulation of Beta Power as a Function of Attachment Style and Feedback Valence

Mizrahi, Dor; Laufer, Ilan; Zuckerman, Inon

doi:10.1007/978-3-031-43075-6_2

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-43075-6_2

|View full text |Cite

Modulation of Beta Power as a Function of Attachment Style and Feedback Valence

Dor Mizrahi,

Ilan Laufer,

Inon Zuckerman

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite2

Independent1

Authors

Journals

Cited by 3 publications

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Comparative analysis of ROCKET-driven and classic EEG features in predicting attachment styles

Mizrahi,

Laufer,

Zuckerman

2024

BMC Psychol

Self Cite

View full text Add to dashboard Cite

Predicting attachment styles using AI algorithms remains relatively unexplored in scientific literature. This study addresses this gap by employing EEG data to evaluate the effectiveness of ROCKET-driven features versus classic features, both analyzed using the XGBoost machine learning algorithm, for classifying ‘secure’ or ‘insecure’ attachment styles.Participants, fourth-year engineering students aged 20–35, first completed the ECR-R questionnaire. A subset then underwent EEG sessions while performing the Arrow Flanker Task, receiving success or failure feedback for each trial.Our findings reveal the effectiveness of both feature sets. The dataset with ROCKET-derived features demonstrated an 88.41% True Positive Rate (TPR) in classifying ‘insecure’ attachment styles, compared to the classic features dataset, which achieved a notable TPR as well. Visual representations further support ROCKET-derived features’ proficiency in identifying insecure attachment tendencies, while the classic features exhibited limitations in classification accuracy. Although the ROCKET-derived features exhibited higher TPR, the classic features also presented a substantial predictive ability.In conclusion, this study advances the integration of AI in psychological assessments, emphasizing the significance of feature selection for specific datasets and applications. While both feature sets effectively classified EEG-based attachment styles, the ROCKET-derived features demonstrated a superior performance across multiple metrics, making them the preferred choice for this study.

show abstract

Comparative analysis of ROCKET-driven and classic EEG features in predicting attachment styles

Mizrahi,

Laufer,

Zuckerman

2024

BMC Psychol

Self Cite

View full text Add to dashboard Cite

show abstract

Enhancing EEG-based attachment style prediction: unveiling the impact of feature domains

Laufer,

Mizrahi,

Zuckerman

2024

Front. Psychol.

Self Cite

View full text Add to dashboard Cite

IntroductionAttachment styles are crucial in human relationships and have been explored through neurophysiological responses and EEG data analysis. This study investigates the potential of EEG data in predicting and differentiating secure and insecure attachment styles, contributing to the understanding of the neural basis of interpersonal dynamics.MethodsWe engaged 27 participants in our study, employing an XGBoost classifier to analyze EEG data across various feature domains, including time-domain, complexity-based, and frequency-based attributes.ResultsThe study found significant differences in the precision of attachment style prediction: a high precision rate of 96.18% for predicting insecure attachment, and a lower precision of 55.34% for secure attachment. Balanced accuracy metrics indicated an overall model accuracy of approximately 84.14%, taking into account dataset imbalances.DiscussionThese results highlight the challenges in using EEG patterns for attachment style prediction due to the complex nature of attachment insecurities. Individuals with heightened perceived insecurity predominantly aligned with the insecure attachment category, suggesting a link to their increased emotional reactivity and sensitivity to social cues. The study underscores the importance of time-domain features in prediction accuracy, followed by complexity-based features, while noting the lesser impact of frequency-based features. Our findings advance the understanding of the neural correlates of attachment and pave the way for future research, including expanding demographic diversity and integrating multimodal data to refine predictive models.

show abstract

Utilizing Dry Electrode Electroencephalography and AI Robotics for Cognitive Stress Monitoring in Video Gaming

Alrasheedi,

Alrabeah,

Almuhareb

et al. 2024

ASI

View full text Add to dashboard Cite

This research explores the integration of the Dry Sensor Interface-24 (DSI-24) EEG headset with a ChatGPT-enabled Furhat robot to monitor cognitive stress in video gaming environments. The DSI-24, a cutting-edge, wireless EEG device, is adept at rapidly capturing brainwave activity, making it particularly suitable for dynamic settings such as gaming. Our study leverages this technology to detect cognitive stress indicators in players by analyzing EEG data. The collected data are then interfaced with a ChatGPT-powered Furhat robot, which performs dual roles: guiding players through the data collection process and prompting breaks when elevated stress levels are detected. The core of our methodology is the real-time processing of EEG signals to determine players’ focus levels, using a mental focusing feature extracted from the EEG data. The work presented here discusses how technology, data analysis methods and their combined effects can improve player satisfaction and enhance gaming experiences. It also explores the obstacles and future possibilities of using EEG for monitoring video gaming environments.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Modulation of Beta Power as a Function of Attachment Style and Feedback Valence

Cited by 3 publications

References 24 publications

Comparative analysis of ROCKET-driven and classic EEG features in predicting attachment styles

Comparative analysis of ROCKET-driven and classic EEG features in predicting attachment styles

Enhancing EEG-based attachment style prediction: unveiling the impact of feature domains

Utilizing Dry Electrode Electroencephalography and AI Robotics for Cognitive Stress Monitoring in Video Gaming

Contact Info

Product

Resources

About