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Abstract：
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.
摘要：
在科学文献中，使用AI算法预测依恋风格仍然相对未被探索。本研究通过使用EEG数据来评估ROCKET驱动特征与经典特征的有效性来解决这一差距。两者都使用XGBoost机器学习算法进行分析，用于对\'安全\'或\'不安全\'附件样式进行分类。参与者,20-35岁的工程专业四年级学生，首先填写ECR-R问卷。然后，一个子集在执行ArrowFlanker任务时进行了EEG会话，接收每次试验的成功或失败反馈。我们的发现揭示了这两个特征集的有效性。具有ROCKET派生特征的数据集在分类“不安全”附件样式时显示出88.41％的真阳性率（TPR），与经典特征数据集相比，这也取得了显著的TPR。视觉表示进一步支持ROCKET派生的功能，熟练地识别不安全的附件倾向，而经典特征在分类精度上表现出局限性。虽然火箭衍生的特征表现出更高的TPR，经典特征也表现出了实质性的预测能力。总之,这项研究推进了人工智能在心理评估中的整合，强调特征选择对特定数据集和应用程序的重要性。虽然这两个特征集有效地对基于EEG的附件样式进行了分类，ROCKET派生的功能展示了跨多个指标的卓越性能，使他们成为本研究的首选。
