Key result
A Random Forest classifier using Matthew Correlation Coefficient Advanced (MCCA) and multimodal features from EEG signals achieved an average accuracy of 85% ± 10% in classifying human stress levels.
Why the study?
Prolonged stress needs to be identified and controlled before causing physical and mental harm, but methods from previous works remain limited by stress indices.
A random forest classifier using EEG signals and multimodal features achieved 85% average accuracy in classifying human stress into six levels.
May support EEG-based stress monitoring tools; leaves open clinical validation before any adoption.
The prolonged stress needs to be determined and controlled before it harms the physical and mental conditions. This research used questionnaire and physiological approaches in determine stress. EEG signal is an electrophysiological signal to analyze the signal features. The standard features used are peak-to-peak values, mean, standard deviation and root means square (RMS). The unique features in this research are Matthew Correlation Coefficient Advanced (MCCA) and multimodal capabilities in the area of frequency and time-frequency analysis are proposed. In the frequency domain, Power Spectral Density (PSD) techniques were applied while Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were utilized to extract seven features based on time-frequency domain. Various methods applied from previous works are still limited by the stress indices. The merged works between quantities score and physiological measurements were enhanced the stress level from three-levels to six stress levels based on music application will be the second contribution. To validate the proposed method and enhance performance between electroencephalogram (EEG) signals and stress score, support vector machine (SVM), random forest (RF), K- nearest neighbor (KNN) classifier is needed. From the finding, RF gained the best performance average accuracy 85% ±10% in Ten-fold and K-fold techniques compared with SVM and KNN.
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Zahari et al. (2021) studied Stress (n=34). Random Forest classifier with MCCA and multimodal EEG features vs. SVM and KNN classifiers was evaluated on Average classification accuracy. A Random Forest classifier using Matthew Correlation Coefficient Advanced (MCCA) and multimodal features from EEG signals achieved an average accuracy of 85% ± 10% in classifying human stress levels.
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