Work

Biosignal-based screening of depressive symptoms during affective conversations with virtual humans

Year: 2026

Type: article

Abstract: This study investigates psychophysiological biomarkers of depressive symptoms during socially grounded, ecologically valid casual social interactions. Using an AI-based virtual human, 102 adults were recruited; 98 (51% women; 18-59 years) were analyzed after signal-quality screening (40 with depressive symptoms: PHQ-9 ≥10; 58 healthy controls: PHQ-9 ≤9) during six semi-guided, emotion-eliciting conversations. We recorded electroencephalogram (EEG), heart rate variability, galvanic skin response and eye-tracking data. Unimodal and multimodal voting models were evaluated with nested cross-validation. The emotion-wise multimodal model, trained separately within each of the six narratives, achieved 72% accuracy (AUC = 0.76; specificity = 83%), while EEG alone performed similarly (AUC = 0.75). Other modalities were less informative (AUC = 0.60-0.68). SHAP analyses revealed emotion-dependent, modality-specific patterns underlying predictions. Conversational emotional context improved discrimination over resting baselines, particularly for EEG and the multimodal ensemble, suggesting that virtual humans may reveal depression-related socio-affective signatures beyond passive recordings.

Source: npj Digital Medicine

DOI: 10.1038/s41746-026-03017-2

Authors: Javier Marín-Morales, Alberto Altozano, Francesca Mura, Lucía Gómez-Zaragozá, Maria Eleonora Minissi, Mariano Alcañiz Raya

Institutions: Universitat Politècnica de València, University of Padua, IT University of Copenhagen

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: gold

Estimated APC paid: $4,090