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A method of identifying chronic stress by EEG

Authors:

Qingcui CaiAuthors Info & Claims

Personal and Ubiquitous Computing, Volume 17, Issue 7

Pages 1341 - 1347

https://doi.org/10.1007/s00779-012-0593-3

Published: 01 October 2013 Publication History

Abstract

There are a lot of studies on chronic stress assessment applying psychology instruments or hormones analysis. However, there are only few studies using electroencephalogram (EEG), which is a non-invasive method providing objective inspection on brain functioning. In this paper, we analyzed overall complexity and spectrum power of certain EEG bands (theta, alpha and beta) collected from two groups of human subjects--high stress versus moderate stress at prefrontal sites (Fp1, Fp2 and Fpz). The results showed that the differences of nonlinear features (C0, LZC, D2, L1 and RE) and linear features (power and alpha asymmetry score) between two groups are significant. C0, LZC and D2 significantly increased in stress group at Fp1 and Fp2, while L1 and RE significantly decreased. And those with chronic stress have higher left prefrontal power. Finally, we suggest that it may be effective to discriminate the high-stress people from moderate-stress people by EEG.

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Cited By

Badr YTariq UAl-Shargie FBabiloni FAl Mughairbi FAl-Nashash H(2024)A review on evaluating mental stress by deep learning using EEG signalsNeural Computing and Applications10.1007/s00521-024-09809-536:21(12629-12654)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09809-5
García-Martínez BFernández-Caballero AAlcaraz RMartínez-Rodrigo A(2021)Cross-sample entropy for the study of coordinated brain activity in calm and distress conditions with electroencephalographic recordingsNeural Computing and Applications10.1007/s00521-021-05694-433:15(9343-9352)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s00521-021-05694-4
García-Martínez BMartínez-Rodrigo AFernández-Caballero AMoncho-Bogani JAlcaraz R(2020)Nonlinear predictability analysis of brain dynamics for automatic recognition of negative stressNeural Computing and Applications10.1007/s00521-018-3620-032:17(13221-13231)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s00521-018-3620-0
Show More Cited By

A method of identifying chronic stress by EEG
1. Applied computing
  1. Life and medical sciences
  2. Operations research

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cover image Personal and Ubiquitous Computing

Personal and Ubiquitous Computing Volume 17, Issue 7

October 2013

246 pages

ISSN:1617-4909

Issue’s Table of Contents

Copyright © Copyright © 2013 Springer-Verlag London.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 October 2013

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Cited By

Badr YTariq UAl-Shargie FBabiloni FAl Mughairbi FAl-Nashash H(2024)A review on evaluating mental stress by deep learning using EEG signalsNeural Computing and Applications10.1007/s00521-024-09809-536:21(12629-12654)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09809-5
García-Martínez BFernández-Caballero AAlcaraz RMartínez-Rodrigo A(2021)Cross-sample entropy for the study of coordinated brain activity in calm and distress conditions with electroencephalographic recordingsNeural Computing and Applications10.1007/s00521-021-05694-433:15(9343-9352)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s00521-021-05694-4
García-Martínez BMartínez-Rodrigo AFernández-Caballero AMoncho-Bogani JAlcaraz R(2020)Nonlinear predictability analysis of brain dynamics for automatic recognition of negative stressNeural Computing and Applications10.1007/s00521-018-3620-032:17(13221-13231)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s00521-018-3620-0
Kumar PSaini RRoy PSahu PDogra D(2018)Envisioned speech recognition using EEG sensorsPersonal and Ubiquitous Computing10.1007/s00779-017-1083-422:1(185-199)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s00779-017-1083-4
Xue YLi QZhao LJia JFeng LYu FClifton D(2016)Analysis of Teens' Chronic Stress on Micro-blogProceedings of the 17th International Conference on Web Information Systems Engineering - Volume 1004210.1007/978-3-319-48743-4_10(121-136)Online publication date: 7-Nov-2016
https://dl.acm.org/doi/10.1007/978-3-319-48743-4_10
Li NHu BChen JPeng HZhao QZhao M(2013)Investigation of Chronic Stress Differences between Groups Exposed to Three Stressors and Normal Controls by Analyzing EEG RecordingsProceedings, Part II, of the 20th International Conference on Neural Information Processing - Volume 822710.1007/978-3-642-42042-9_64(512-521)Online publication date: 3-Nov-2013
https://dl.acm.org/doi/10.1007/978-3-642-42042-9_64

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