Abstract
Objective: Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress. Methods: To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers. Results: The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.
Highlights
Stress can increase anxiety and depression levels as a result of emotional dysregulation [1], [2]
The results show that the classification accuracy was 83% using the support vector machine (SVM) classifier with the power values of EEG frequency bands, and 91% with the latent representation of the same EEG’s power feature
The results suggest that the latent representation of these features, especially the power values, increased the classification accuracy by 8%
Summary
Stress can increase anxiety and depression levels as a result of emotional dysregulation [1], [2]. Decreasing the signal-to-noise-ratio in an EEG signal is one of the most main challenges for an EEG data analysis This could be one of the reasons limiting the capability of classifying more than two levels of a lab induced stress task. We recorded the EEG data of 30 clinically active nurses and 50 non-health professionals while performing 3 different stress-inducing tasks: 1. Promising results was obtained in the research by Yin and Zhang [35] as they used a stacked-denoising autoencoder to learn within and across sessions of EEG workload data They achieved an average accuracy of 95.4 % and 87.4 % for classifying two levels of mental workloads within and across sessions, respectively. In total there was 600 x 75 data points for the baseline, 600 x 75 data points for the preparation task, 300 X 75 data points for the mental arithmetic task and 300 x 75 data points for speech task
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