Wearable EEG-Based Real-Time System for Depression Monitoring

It has been reported that depression can be detected by electrophysiological signals. However, few studies investigate how to daily monitor patient's electrophysiological signals through a more convenient way for a doctor, especially on the monitoring of electroencephalogram (EEG) signals for depression diagnosis. Since a person's mental state and physiological state are changing over time, the most insured diagnosis of depression requires doctors to collect and analyze subject's EEG signals every day until two weeks for the clinical practice. In this work, we designed a real-time depression monitoring system to capture the user's EEG data by a wearable device and to perform real-time signal filtering, artifacts removal and power spectrum visualization, which could be combined with psychological test scales as an auxiliary diagnosis. In addition to collecting the resting EEG signals for real-time analysis or diagnosis of depression, we also introduced an external audio stimulus paradigm to further make a detection of depression. Through the machine learning method, system can give a credible probability of depression under each stimulus as a user's self-rating score from continuous EEG data. EEG signals collected from 81 early-onset patients and 89 normal controls are used to build the final classification model and to verify the practical performance.