EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features
Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed...
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my.iium.irep.970392022-03-04T01:40:08Z http://irep.iium.edu.my/97039/ EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features Handayani, Dini QA75 Electronic computers. Computer science Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results. 2021-09-20 Article PeerReviewed application/pdf en http://irep.iium.edu.my/97039/1/eegmentalstress.pdf Handayani, Dini (2021) EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features. Sensors. |
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QA75 Electronic computers. Computer science Handayani, Dini EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features |
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Exposure to mental stress for long period leads to serious accidents and health problems.
To avoid negative consequences on health and safety, it is very important to detect mental stress at
its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed
an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic
task with time pressure and negative feedback as the stressors. We assessed the levels of stress on
22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level
(AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion
of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and
spectral domain features. A total of 210 different features were extracted from all domains. Only the
optimum multi-domain features were used for classification. We then quantified stress levels using
statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly
increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity
network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the
highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier.
Other classifiers produced relatively similar results. |
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Article |
author |
Handayani, Dini |
author_facet |
Handayani, Dini |
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Handayani, Dini |
title |
EEG Mental Stress Assessment Using Hybrid Multi-Domain
Feature Sets of Functional Connectivity Network and
Time-Frequency Features |
title_short |
EEG Mental Stress Assessment Using Hybrid Multi-Domain
Feature Sets of Functional Connectivity Network and
Time-Frequency Features |
title_full |
EEG Mental Stress Assessment Using Hybrid Multi-Domain
Feature Sets of Functional Connectivity Network and
Time-Frequency Features |
title_fullStr |
EEG Mental Stress Assessment Using Hybrid Multi-Domain
Feature Sets of Functional Connectivity Network and
Time-Frequency Features |
title_full_unstemmed |
EEG Mental Stress Assessment Using Hybrid Multi-Domain
Feature Sets of Functional Connectivity Network and
Time-Frequency Features |
title_sort |
eeg mental stress assessment using hybrid multi-domain
feature sets of functional connectivity network and
time-frequency features |
publishDate |
2021 |
url |
http://irep.iium.edu.my/97039/1/eegmentalstress.pdf http://irep.iium.edu.my/97039/ |
_version_ |
1726791279213281280 |
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13.160551 |