MindCare: An Innovative Application for Depression Diagnosis and Treatment Support

Boyu Yang, Dongsheng Cheng, Ming-Chun Huang

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Keywords: Depression screening, EEG biomarkers, Mobile health applications, Artificial intelligence, Digital therapeutics

TL;DR: MindCare for Depression

Abstract: Depression screening remains challenging due to reliance on subjective assessments and limited accessibility of mental health services. This paper presents MindCare, an integrated mobile platform combining standardized questionnaires, AI-powered therapeutic interactions, emotional journaling, and EEG-based neurophysiological assessment. The system implements a novel visual stimulation protocol using 60 emotionally evocative images from the Open Affective Standardized Image Set (OASIS) to elicit measurable neural responses. User evaluation with 7 participants demonstrated high satisfaction across all features. EEG analysis of 4 participants during emotional stimulation revealed strong correlations between frontal channel neural features and PHQ-9 depression scores. Machine learning classification achieved 97.9% accuracy in distinguishing depression status using segment-based analysis of 240 stimulus-response pairs. The integration of objective neurophysiological markers with subjective assessment tools demonstrates significant potential for enhancing digital mental health screening capabilities.

Track: 1. Digital Health Solutions (i.e. sensors and algorithms) for diagnosis, progress, and self-management

NominateReviewer: Ming-Chun Huang, [e-mail]

Submission Number: 38