A support vector machine classifier using Beta-band absolute power from a single EEG electrode (Fp2) achieved 98% accuracy in distinguishing three levels of stress states.
Observational (n=50)
A machine learning approach using SVM and single-electrode EEG Beta-band power can accurately classify stress levels.
Stress is the body’s natural reaction to life events and chronic stress disrupts the physiological equilibrium of the body which ultimately contributes to a negative impact on physical and mental health. Hence, an endeavor to develop a stress level monitoring system is necessary and important to clinical intervention and disease prevention. Electroencephalography (EEG) acquisition tool was used in this study to capture the brainwave signals at the prefrontal cortex (Fp1 and Fp2) from 50 participants and investigate the brain states related to stress-induced by virtual reality (VR) horror video and intelligence quotient (IQ) test. The collected EEG signals were pre-processed to remove artifacts and the EEG features associated with stress were done through frequency domain analysis to extract power spectral density (PSD) values of Theta, Alpha and Beta frequency bands. The Wilcoxon signed-rank test was carried out to find the significant difference in the absolute power between resting baseline and post-stimuli. The test reported that EEG features using a single electrode, in particular, Theta absolute power was significantly increased at Fp1 electrode (p<0.001) and Fp2 electrode (p<0.015) during post-IQ. Whereas Beta absolute power at Fp2 electrode was observed to significantly increase during both conditions, the post-VR (p<0.024) and post-IQ (p<0.011) respectively. Following this, the significant features were clustered into three groups of stress level using k-means clustering method and the labelled data was fed into support vector machine (SVM) to classify the stress levels. 10-fold cross validation was applied to evaluate the classifier’s performance, with the result confirming the highest performance of 98% accuracy in distinguishing three levels of stress states by using only the feature of Beta-band absolute power from a single electrode (Fp2).
Wen et al. (Mon,) conducted a observational in Stress (n=50). Support vector machine (SVM) classification using single-electrode EEG was evaluated on Stress level classification accuracy. A support vector machine classifier using Beta-band absolute power from a single EEG electrode (Fp2) achieved 98% accuracy in distinguishing three levels of stress states.