查看文章

By improving the accuracy of depression recognition and designing a consumer-oriented depression detection system, consumers are expected to receive convenient and fast e-health services. Recently, depression recognition methods based on the analysis of physiological and behavioral data have attracted attention. In particular, the research on Electroencephalography (EEG) and speech signals becomes hotspots. However, EEG is susceptible to individual differences, while speech signal is susceptible to environmental factors. In this study, we propose an auxiliary decision-making system for depression detection that considers both physiological and behavioral factors by fusing EEG and speech signals. Compared to existing studies, our proposed multi-modal fusion strategy exploits more linear and nonlinear features to support the recognition of task classifications. In addition, we analyze the functional …