Quantitative Assessment of Stress Through EEG During a Virtual Reality Stress-Relax Session
Metadatos
Mostrar el registro completo del ítemAutor
Pérez Valero, Eduardo; Vaquero Blasco, Miguel Ángel; López Gordo, Miguel Ángel; Morillas Gutiérrez, Christian AgustínEditorial
Frontiers Research Foundation
Materia
EEG Stress Regression Machine learning Virtual reality
Fecha
2021-07-14Referencia bibliográfica
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]
Patrocinador
Spanish Ministry of Science, Innovation and Universities, by European Regional Development Funds PGC2018-098813-B-C31Resumen
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.