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

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

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.