An Online Stress Monitoring System Based On Single-channel EEG

The rapid development of social economy makes people facing increasing stress from work, life and character. Before further development to mental disorder illness, how to detect the stress and take timely measures are very necessary. For example, the monitoring of the stress plays a key role in the process of detection and intervention of depressionThe traditional mean of stress monitoring is a variety of user self-rating scale, but its outstanding problems is the difficulty of avoiding subjectivity. Meanwhile, we need an online stress monitoring system that can be applied in daily life.In order to monitor stress easily and objectively in daily life, we build a pervasive online stress detection system. By collecting EEG from Fpz point, the system can make objective evaluation and conduct long-line monitoring of users’ stress, at same time, can assist physicians to assess the users’ state. Based soap protocol, the proposed system adopts client/server framework. Client-side consists of "User Panel" and "Doctor Panel". The "User Panel" integrates modules including biological signals acquisition, self-assessment questionnaire, history record and doctor chatting. The "Doctor Panel" includes history record module and user chatting panel. Server-side is mainly responsible for the effective management of users’data, as well as EEG processing.For the purpose of adopting effective EEG features and algorithms in the system, an experiment has been conducted. After collecting subjects’ EEG signal, denoising, feature extraction and classification, three features effective for stress classification were screened out, namely LZ-complexity, alpha relative power and the ratio of alpha power to beta power. By comparing results the k-nearest neighbor classifier were determined as classification algorithm in system. Meanwhile, we introduced the stress index for indicating stress level intuitionisticly.