Detecting mild depression from EEG signal in a non-clinical environment using machine learning technique

• Main Authors: Thulasi, K., Balakrishnan, Sumathi, Yap, Jia Suan, Yan, Xiao Qing, Malarvili, M. B., Murugesan, R. K., Devandran, Pagupathi
• Format: Conference or Workshop Item
• Published: 2023
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/107700/; http://dx.doi.org/10.1109/NBEC58134.2023.10352586

Depression can be detected through screening tests and non-invasive examinations at specific clinics, yet a professional must verify the severity. If mild depressions are not detected, it can lead to major depressions. Eventually, this could also be fatal. Collectively, the studies from the literature review outline the critical role of EEG in revolutionising the recognition of depression disability through machine learning prediction models. This project aims to find a solution that enables the acquisition of EEG signals using commercially available headsets, apply machine learning algorithms for processing, and determine mild depression detection. The system's accuracy is tested to ensure it reaches a safe percentage of precision. Additionally, the paper addresses open issues encountered during the project.