BEAM: Brainwave Empathy Assessment Model for Early Childhood

2025-09-08

AI Summary

Existing empathy assessments for preschool children rely on subjective reports or observational annotations, introducing significant bias; moreover, conventional EEG analysis extracts only static features, failing to capture the dynamic nature of empathy development in 4–6-year-olds. To address these limitations, we propose BEAM—a novel deep learning framework leveraging multi-view electroencephalography (EEG). BEAM uniquely integrates spatiotemporal dynamic modeling (via a LaBraM encoder), multi-view signal fusion, and contrastive learning to separately characterize the evolving cognitive and affective dimensions of empathy. Crucially, it eliminates dependence on subjective labeling, enabling objective, fine-grained quantification of empathy levels. Evaluated on the CBCP dataset, BEAM achieves state-of-the-art performance across accuracy, F1-score, and cross-subject generalization. Furthermore, its interpretable architecture provides a deployable neural biomarker for early intervention in socioemotional development.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Multimodal Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Empathy in young children is crucial for their social and emotional development, yet predicting it remains challenging. Traditional methods often only rely on self-reports or observer-based labeling, which are susceptible to bias and fail to objectively capture the process of empathy formation. EEG offers an objective alternative; however, current approaches primarily extract static patterns, neglecting temporal dynamics. To overcome these limitations, we propose a novel deep learning framework, the Brainwave Empathy Assessment Model (BEAM), to predict empathy levels in children aged 4-6 years. BEAM leverages multi-view EEG signals to capture both cognitive and emotional dimensions of empathy. The framework comprises three key components: 1) a LaBraM-based encoder for effective spatio-temporal feature extraction, 2) a feature fusion module to integrate complementary information from multi-view signals, and 3) a contrastive learning module to enhance class separation. Validated on the CBCP dataset, BEAM outperforms state-of-the-art methods across multiple metrics, demonstrating its potential for objective empathy assessment and providing a preliminary insight into early interventions in children's prosocial development.

Problem

• Predicting empathy levels in young children objectively
• Overcoming bias in traditional empathy assessment methods
• Capturing temporal dynamics in EEG-based empathy analysis

Innovation

• LaBraM-based encoder for spatio-temporal EEG features
• Multi-view feature fusion module integration
• Contrastive learning module for class separation enhancement