Stress status classification based on EEG signals

Title & Authors
Stress status classification based on EEG signals
Kang, Jun-Su; Jang, Giljin; Lee, Minho;

Abstract
In daily life, humans get stress very often. Stress is one of the important factors of healthy life and closely related to the quality of life. Too much stress is known to cause hormone imbalance of our body, and it is observed by the brain and bio signals. Based on this, the relationship between brain signal and stress is explored, and brain signal based stress index is proposed in our work. In this study, an EEG measurement device with 32 channels is adopted. However, only two channels (FP1, FP2) are used to this study considering the applicability of the proposed method in real enveironment, and to compare it with the commercial 2 channel EEG device. Frequency domain features are power of each frequency bands, subtraction, addition, or division by each frequency bands. Features in time domain are hurst exponent, correlation dimension, lyapunov exponent, etc. Total 6 subjects are participated in this experiment with English sentence reading task given. Among several candidate features, $\small{{\frac{{\theta}\;power}{mid\;{\beta}\;power}}}$ shows the best test performance (70.8%). For future work, we will confirm the results is consistent in low price EEG device.
Keywords
EEG;stress status;stress classification;features of time and frequency domain;
Language
Korean
Cited by
1.
The Intelligent Healthcare Data Management System Using Nanosensors, Journal of Sensors, 2017, 2017, 1
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