Key result
A Support Vector Machine classifier using multi-domain EEG features achieved 93.2% accuracy in classifying mental stress induced by arithmetic tasks.
Why the study?
Prolonged exposure to mental stress leads to health problems, making early detection of acute or episodic stress essential to prevent negative health consequences.
Can a fusion of EEG functional connectivity networks and time-frequency features accurately detect mental stress in healthy subjects?
Can a fusion of EEG functional connectivity networks and time-frequency features accurately detect mental stress in healthy subjects?
A hybrid multi-domain feature set from EEG signals combined with machine learning can accurately detect acute mental stress.
Provides protocol for inducing graded acute stress; hypothesis-generating for sensor-based detection and requires validation before clinical use.
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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Hag et al. (2021) studied Mental stress (n=22). Mental arithmetic task with time pressure and negative feedback vs. Unstressed state was evaluated on Stress classification accuracy using Support Vector Machine (SVM) classifier. A Support Vector Machine classifier using multi-domain EEG features achieved 93.2% accuracy in classifying mental stress induced by arithmetic tasks.
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