Exploring EEG Features for Differentiating Between Secure and Insecure Attachment Styles

Zuckerman, Inon; Mizrahi, Dor; Laufer, Ilan

doi:10.1007/978-3-031-47718-8_29

Lecture Notes in Networks and Systems

2024

DOI: 10.1007/978-3-031-47718-8_29

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Exploring EEG Features for Differentiating Between Secure and Insecure Attachment Styles

Inon Zuckerman,

Dor Mizrahi,

Ilan Laufer

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2024

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Cited by 2 publications

References 28 publications

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

Mizrahi,

Laufer,

Zuckerman

2024

BMC Psychol

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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.

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

Mizrahi,

Laufer,

Zuckerman

2024

BMC Psychol

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

Laufer,

Mizrahi,

Zuckerman

2024

Front. Psychol.

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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.

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Exploring EEG Features for Differentiating Between Secure and Insecure Attachment Styles

Cited by 2 publications

References 28 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

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