TRI-DEP: A Trimodal Comparative Study for Depression Detection Using Speech, Text, and EEG

ArXi:2510.14922v2 Announce Type: replace Depression is a widespread mental health disorder, yet its automatic detection remains challenging. Prior work has explored unimodal and multimodal approaches, with multimodal systems showing promise by leveraging complementary signals. However, existing studies are limited in scope, lack systematic comparisons of features, and suffer from inconsistent evaluation protocols. We address these gaps by systematically exploring feature representations and modelling strategies across EEG, together with speech and text.