Quantitative Assessment of Stress Through EEG During a Virtual Reality Stress-Relax Session

Abstract

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.

Real-time mental stress detection using multimodality expressions with a deep learning framework

“…Minguillon collected multiple biosignals from 10 subjects (Minguillon et al, 2018). Perez-Valero conducted a group of 23 participants over the MIST experiment (Perez-Valero et al, 2021). To confirm whether the participants developed acute stress during the MIST experiment, we asked each participant to fill out a questionnaire after the MIST experiment.…”

“…As a well-established psychological experiment employed in stress assessment, it has been proven to put people into a stress state by measuring the amount of cortisol in their saliva (Lederbogen et al, 2011;Kiem et al, 2013;Sioni and Chittaro, 2015). To date, a large number of studies on stress have been carried out on the basis of MIST and its modified experiments (Boehringer et al, 2015;Chung et al, 2016;Wheelock et al, 2016;Gossett et al, 2018;Hakimi and Setarehdan, 2018;Li et al, 2018;Xia et al, 2018;Noack et al, 2019;Perez-Valero et al, 2021). The MIST is a computer-based standardized psychological experimental designed to assess the effects of psychological stress on people's physiology and behavior (Dedovic et al, 2005).…”

Recent Advances in Multiplexed Wearable Sensor Platforms for Real-Time Monitoring Lifetime Stress: A Review

“…When analyzing stress using an EEG signal, ‘feature extraction’ for the raw signal and ‘classification’ based on it are performed. Support vector machines (SVM), logistic regression (LR), naïve bayes (NB), and K-nearest neighbors (KNN) are common classifier algorithms for stress monitoring, and efforts to improve stress monitoring accuracy are ongoing [ 43 , 44 ]. The ECG records and extracts the electrical signal related to HRV and HR as a waveform, by measuring the time difference between the R peaks, which is the electrical signal that passes the ventricular walls and is observed in the signal derived from QRS complexes [ 61 , 62 ].…”

Research on Stress Reduction Model Based on Transformer

“…Brain is the central part of stress generation and its spectral features are closely related to different psychophysiological conditions. As a result, researches believes that the power of EEG bands, such as alpha, beta, delta, theta and gamma, is the most credible indicators when detecting and analyzing stress [20,21]. Power spectrum has been widely applied in the analysis of stress [22].…”