Attention-Emotion Assessment of ASD Children via Representation Learning Based on Cross-Modal Disentanglement and Attention Alignment

Comprehensive cognitive-affective profiling for precision ASD assessment is hindered by inadequate computational modeling of emotion-behavior dynamics. This gap persists due to limited methods for reconciling EEG, eye-tracking, and facial expression modalities-critical for capturing ASD heterogeneity drivers. This study proposes the Cognitive-Affective Ability Assessment (CA3) framework, focusing on attention control in conjunction with valence-arousal, to address these limitations through: (1)Ability-Specific Neural Disentanglementextracting attention- and emotion-specific representations from EEG using ET and facial expression recordings as anchors; (2)Symmetrical Cross-Ability Alignmentmodeling contextual dependencies via a symmetric cross-attention mechanism; and (3)Uncertainty-Aware Ability Integrationfusing per-ability predictions using Dirichlet modeling and Dempster-Shafer theory while quantifying confidence. Extensive experiments have been conducted to evaluate CA$^{3}$vs. state-of-the-arts counterparts on the multimodel datasets (EEG, ET and facial expression recordings) with CCNU (27 ASD vs. 30 typically developing (TD) children) and BNU (49 ASD vs. 48 TD), and the results demonstrate that: 1) In the ASD cognitive-affective abilities assessment task, accuracy is improved by 2.7% for attention control and 2.5% for emotion perception, and by 3.5% for cognitive-affective abilities assessment, compared with current state-of-the-art methods. 2) For attention control and valence-arousal, composite ability gaps are measured between ASD and typically developing children: 15.6% and 19.9% for mild ASD, 28.2% and 39.1% for moderate ASD, and 44.4% and 61.3% for severe ASD, respectively. Overall, the framework effectively bridges computational assessment with clinically actionable insights, enabling robust decision-making through personalized cognitive-affective ASD profiles.