PDF(Pubmed)
Abstract：
UNASSIGNED: Attachment 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.
UNASSIGNED: We 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.
UNASSIGNED: The 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.
UNASSIGNED: These 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.
UNASSIGNED: We 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.
UNASSIGNED: The 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.
UNASSIGNED: These 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.
摘要：
■依恋风格在人际关系中至关重要，并已通过神经生理反应和脑电图数据分析进行了探索。这项研究调查了脑电图数据在预测和区分安全和不安全的依恋方式方面的潜力，有助于理解人际动力学的神经基础。
■我们在研究中招募了27名参与者，采用XGBoost分类器分析各种特征域的EEG数据，包括时域，基于复杂性，和基于频率的属性。
■该研究发现，依恋风格预测的精确度存在显着差异：预测不安全依恋的准确率高达96.18％，和一个较低的精度55.34%的安全附件。平衡精度指标表明，总体模型精度约为84.14%，考虑到数据集的不平衡。
■这些结果突出了由于依恋不安全感的复杂性，将EEG模式用于依恋风格预测的挑战。具有高度不安全感的个人主要与不安全依恋类别保持一致，这表明他们的情绪反应性和对社交线索的敏感性增加。该研究强调了时域特征在预测准确性中的重要性，其次是基于复杂性的特征，同时注意到基于频率的特征的影响较小。我们的发现促进了对依恋神经相关性的理解，并为未来的研究铺平道路。包括扩大人口多样性和整合多模式数据以完善预测模型。
■我们在研究中招募了27名参与者，采用XGBoost分类器分析各种特征域的EEG数据，包括时域，基于复杂性，和基于频率的属性。
■该研究发现，依恋风格预测的精确度存在显着差异：预测不安全依恋的准确率高达96.18％，和一个较低的精度55.34%的安全附件。平衡精度指标表明，总体模型精度约为84.14%，考虑到数据集的不平衡。
■这些结果突出了由于依恋不安全感的复杂性，将EEG模式用于依恋风格预测的挑战。具有高度不安全感的个人主要与不安全依恋类别保持一致，这表明他们的情绪反应性和对社交线索的敏感性增加。该研究强调了时域特征在预测准确性中的重要性，其次是基于复杂性的特征，同时注意到基于频率的特征的影响较小。我们的发现促进了对依恋神经相关性的理解，并为未来的研究铺平道路。包括扩大人口多样性和整合多模式数据以完善预测模型。
