Research on crime motivation identification and quantitative analysis methods based on EEG signals

Introduction Understanding and quantifying crime motivation is essential for developing effective interventions in criminology and psychology. This research, closely aligned with quantitative psychology and measurement, presents a novel approach to identifying and analyzing crime motivations using EEG signals. Traditional methods often fail to capture the intricate interplay of individual, social, and environmental factors due to data sparsity and the absence of real-time adaptability. Methods In this study, we introduce the Hierarchical Crime Motivation Network (HCM-Net), a multi-layered framework that integrates EEG signal analysis with social and temporal modeling. HCM-Net employs neural network-based individual feature encoders, graph neural networks for social interaction analysis, and temporal predictors to capture the evolution of motivations. To enhance practical applicability, the Dynamic Risk-Adaptive Strategy (DRAS) complements HCM-Net by incorporating real-time adaptation, scenario-based simulations, and targeted interventions. This framework addresses challenges such as ethical considerations and interpretability by employing Shapley values for feature attribution and bias mitigation techniques. Results Experiments with EEG datasets demonstrate the superior performance of the proposed methods in classifying crime motivations and identifying high-risk individuals compared to state-of-the-art techniques. Discussion These findings highlight the potential of integrating EEG analysis with advanced computational methods in crime prevention and psychological research.