M3ADD: A Novel Benchmark for Physiology Signal-based Automatic Depression Detection with Multimodal Multitask Multievent Framework

Abstract

The prevalence of depression is escalating, especially among youth, which has become a critical mental health concern. Current assessment methods, relying heavily on questionnaires, clinical observations, and AI-driven analyses, are limited by their focus on single-event data, failing to encapsulate the nuanced expressions of depressive symptoms. Moreover, a significant oversight in existing research is the underutilization of electromyogram (EMG) alongside electroencephalogram (EEG) data, which could provide a more holistic view of unconscious body behaviors. Additionally, given the high variability of depression among individuals, traditional analysis models are in urgent need of refinement to accommodate the personality of different individuals. To address these limitations, we propose M<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>ADD, a novel benchmark for Automatic Depression Detection that employs a Multimodal, Multitask, and Multievent framework. We collected EEG and EMG data from 97 participants across varied events (interview, reading tasks, walking), coupled with standardized questionnaires assessing depression, wellbeing, and personality, enriching our multitask learning approach. Our benchmark recognition algorithm leverages multitask learning, channel and interactive attention mechanisms to synthesize event-specific and modal-specific features, enhancing adaptability to individual differences and improving data utilization efficiency. M<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>ADD surpasses existing models by achieving 87% accuracy in detecting depression and 95% accuracy in assessing wellbeing, providing a promising avenue for early identification.