Quantitative Assessment of Stress Through EEG During a Virtual Reality Stress-Relax Session
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AuthorPérez Valero, Eduardo; Vaquero Blasco, Miguel Ángel; López Gordo, Miguel Ángel; Morillas Gutiérrez, Christian Agustín
Frontiers Research Foundation
EEGStressRegressionMachine learningVirtual reality
Perez-Valero E... [et al.] (2021) Quantitative Assessment of Stress Through EEG During a Virtual Reality Stress-Relax Session. Front. Comput. Neurosci. 15:684423. doi: [10.3389/fncom.2021.684423]
SponsorshipSpanish Ministry of Science, Innovation and Universities, by European Regional Development Funds PGC2018-098813-B-C31
Recent studies have addressed stress level classification via electroencephalography (EEG) and machine learning. These works typically use EEG-based features, like power spectral density (PSD), to develop stress classifiers. Nonetheless, these classifiers are usually limited to the discrimination of two (stress and no stress) or three (low, medium, and high) stress levels. In this study we propose an alternative for quantitative stress assessment based on EEG and regression algorithms. To this aim, we conducted a group of 23 participants (mean age 22.65 5.48) over a stress-relax experience while monitoring their EEG. First, we stressed the participants via the Montreal imaging stress task (MIST), and then we led them through a 360-degree virtual reality (VR) relaxation experience. Throughout the session, the participants reported their self-perceived stress level (SPSL) via surveys. Subsequently, we extracted spectral features from the EEG of the participants and we developed individual models based on regression algorithms to predict their SPSL. We evaluated stress regression performance in terms of the mean squared percentage error (MSPE) and the correlation coefficient (R2). The results yielded from this evaluation (MSPE = 10.62 2.12, R2 = 0.92 0.02) suggest that our approach predicted the stress level of the participants with remarkable performance. These results may have a positive impact in diverse areas that could benefit from stress level quantitative prediction. These areas include research fields like neuromarketing, and training of professionals such as surgeons, industrial workers, or firefighters, that often face stressful situations.