AI Based EEG Analysis for Depression Detection: A Critical Evaluation of Current Approaches and Future Directions

This review paper looks at the use of EEG signals to detect depression in people. Depression is a common mental health illness that can have a substantial influence on a person's quality of life. Current diagnostic approaches for depression rely on self-reporting, which can be subjective and incorrect. Because EEG waves may assess brain activity in real-time, they provide a non-invasive means of detecting depression. This study looks at the existing literature on the use of EEG signals for depression detection, covering the various forms of EEG data and the machine learning methods used to analyse them. The review concluded that EEG signals show potential for detecting depression, but more research is needed to create a reliable and accurate diagnostic tool. Overall, the use of EEG signals may improve depression diagnosis and treatment by giving a more objective and accurate assessment of the condition.