Techniques for Detecting Depression-related Emotions, Challenges and Future Prospects

Current depression diagnosis remains subjective. While multimodal HCI methods enable objective assessment, a definitive systematic review remains lacking. The paper will cover the recent changes in technological advances. The article initially reviews depression detection methods using electroencephalographic signals and explains the existing controversies on the dependability of neural markers. It then examines the technology of passive behavior observers based on eye motions and facial recognition, the ways it could be applied in real-life context and the privacy issues involved, and lastly, discusses the multi-source data techniques, which make use of multimodal information integration. These approaches are the trend of mainstream in terms of raising the detection accuracy and robustness; however, it also emerged with novel challenges that include the model complexity, interpretability and privacy ethics. New directions in the lightweight, interpretable and privacy-preserving AI are essential in order to translate those techniques into proactive mental health services.