Analysis of Alpha Band Decomposition in Different Level-k Scenarios with Semantic Processing

Mizrahi, Dor; Zuckerman, Inon; Laufer, Ilan

doi:10.1007/978-3-031-15037-1_6

Cited by 4 publications

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“…Our approach aligns with current trends in neuroscience and psychology, where ensemble machine-learning methods have shown effectiveness in interpreting complex neural patterns ( Rahman et al, 2022 ; Li et al, 2023 ). Our study is focused on examining a broad range of EEG features, totaling 45 in number, which encompass elements from the time domain ( Al-Fahoum and Al-Fraihat, 2014 ; Zuckerman et al, 2022 , 2023b ), frequency-based analyses ( Mizrahi et al, 2022a , b , 2023a ), and complexity measures ( Sheehan et al, 2018 ; Ramadoss et al, 2022 ; Mizrahi et al, 2023b ). The aim is to utilize these features to predict whether an individual has a secure or insecure attachment style and to assess the specific contribution of each feature to this prediction.…”

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confidence: 99%

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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