EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features
Autor: | Ala Ahmed Yahya Hag, Thulasyammal Ramiah Pillai, Teddy Mantoro, Mun Hou Kit, Fares Al-Shargie, Dini Oktarina Dwi Handayani |
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Rok vydání: | 2021 |
Předmět: |
Support Vector Machine
Computer science Feature extraction TP1-1185 Electroencephalography Biochemistry Article Analytical Chemistry time-frequency features Machine Learning Negative feedback Stress (linguistics) Classifier (linguistics) Feature (machine learning) medicine Humans Electrical and Electronic Engineering Instrumentation medicine.diagnostic_test business.industry Chemical technology feature extraction Pattern recognition functional connectivity network Atomic and Molecular Physics and Optics Time–frequency analysis Support vector machine mental stress Artificial intelligence business Stress Psychological |
Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 6300, p 6300 (2021) Sensors Volume 21 Issue 18 |
ISSN: | 1424-8220 |
Popis: | 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. |
Databáze: | OpenAIRE |
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