Work

Res-PGDnet: A time frequency fusion model with physiologically constrained adversarial training for PPG-based valence–arousal state classification

Year: 2026

Type: article

Abstract: Accurate valence–arousal assessment is crucial for affective computing applications. Photoplethysmography (PPG) is a promising, practical modality for this task, but it poses challenges: signals are noise-prone, the physiological affect mapping is complex and subject-specific, and models generalize poorly. Existing methods often focus narrowly on temporal features, employ physiologically unconstrained robustness strategies, or suffer from a severe performance–efficiency trade-off. We propose Res-PGDnet to address these issues via synergistic time frequency fusion and physiologically constrained adversarial training. Its core innovations are (i) applying adversarial perturbations constrained by physiological priors to model parameters to ensure robust, plausible learning and (ii) hierarchically integrating DCT attention modules to jointly enhance temporal and spectral feature representations. On the DEAP-PPG dataset, Res-PGDnet achieves 93.47% intra-subject accuracy and 92.46% Macro-F1 score, with strong robustness (metric: 0.920) and 85.1% cross-subject accuracy. The model is efficient (12.5 M parameters, 1250 FPS), demonstrating potential for wearable-based valence–arousal classification in settings like mental health monitoring.

Source: Biomedical Signal Processing and Control

DOI: 10.1016/j.bspc.2026.111586

Authors: Zhang Chenhao, Tang Hongying

Institution: Shanghai Normal University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,590