Deep Multimodal Fusion for Depression Detection: Integrating Facial Emotion Recognition, EEG Signals and Audio Cues

Presenting a thorough and advanced multimodal approach to depression identification is the aim of this work. This method combines psychological assessments with state-of-the-art technologies. This study aims to provide a multifaceted understanding of depression diagnosis and treatment. Facial expression recognition, electroencephalography (EEG) signals, audio signals, conventional questionnaire data, and exclusive video courses are all combined to achieve this. Our approach not only pinpoints those who are vulnerable, but it also offers a customized, all-inclusive framework for timely intervention and specialized mental health care. Real-time physiological reactions, emotional expressions, self-reported sentiments, and therapeutic interventions are synthesized to achieve this. Depression is a crippling mental illness that affects people of all different ethnicities and is still a global problem. The intricacies of depressive feelings are difficult to fully capture by traditional diagnostic techniques, which frequently rely on self-reported questionnaires and professional examinations.