Neurocommunication and Affective-State Analysis: A Physiological-Proxy-Gated Bimodal Framework for Masked-Distress Scenarios

Facial displays can be ambiguous indicators of internal affect, but physiological-proxy-gated fusion has rarely been evaluated with transparent failure analysis. SilentDepress AI is examined here as an affect-fusion prototype rather than a clinical depression detector. We audited a local corpus of 30 video sequences from five participants, covering six nominal affective categories, and ran a publicly versioned seven-output facial classifier on every decoded frame. Mean frame logits produced a sequence-level accuracy of 20.0% (participant-block 95% CI 16.7–26.7%); the corresponding six-category restricted accuracy was 26.7%. We then performed 1000 paired constructed Monte Carlo cohorts per prespecified scenario using empirical visual Dirichlet priors and label-conditioned heuristic physiological proxies. In the primary sensitivity scenario, the highest mean constructed-case accuracy was 44.6% for 40/60 fusion. An independent external WESAD analysis using nested subject-level LOSO yielded 87.22% balanced accuracy (95% CI 77.22–95.56%) and 88.00% accuracy for native baseline-versus-stress discrimination. This external physiological evaluation was not synchronized with the local facial-video corpus and was not used as a seven-class fusion input. The simulation therefore evaluates the numerical behavior of the gating rule only and exposes the evidence still required for clinical evaluation.