What type of study is this?

This is a Quantitative Study study.

October 23, 2025

BioArt-Net: A computational framework for cross-modal fusion of visual semantics and physiological signals in viewer-centric art emotion recognition

Key Points

Accuracy in emotion recognition improved by cross-modal fusion of physiological signals and visual analysis.
Performance metrics included 0.87 accuracy and 0.85 F1 score using the BioArt-Emotion Dataset.
Methodology involved wavelet transform for EEG and compound loss for model optimization, reducing redundancy by 27%.
Significance includes advancing interdisciplinary frameworks within computational neuroaesthetics, highlighting innovative approaches.

Abstract

Emotional responses to visual art involve perceptual and physiological interactions, but existing methods isolate these modalities, limiting individualized affective modeling. This study presents BioArt-Net, a computational framework integrating visual semantic analysis and physiological signal processing for viewer-centric art emotion recognition, focusing on cross-modal fusion methodologies. Key modules: Visual semantics via fine-tuned ViT (16 × 16 patches, 32% dimensionality reduction); EEG (wavelet transform), GSR/PPG (1D CNNs) encoding with 94% data integrity; token-level attention aligning modalities; compound loss (CE + MSE + entropy) reducing redundancy by 27%; optimized via distillation (48% fewer parameters, 89 ms latency). On 520-session BioArt-Emotion Dataset, accuracy reaches 0.87 (vs 0.76 unimodal), F1 = 0.85. Cross-modal attention boosts accuracy by 11%; physiological reweighting stabilizes results. This advances computational neuroaesthetics, fitting the journal’s focus on innovative interdisciplinary frameworks.

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