EEG based stress analysis using rhythm specific spectral feature for video gameplay
Shidhartho Roy, Monira Islam, Md. Salah Uddin Yusuf, Nushrat Jahan

TL;DR
This study uses EEG spectral features, specifically the Beta-to-Alpha ratio, to analyze mental stress during different types of video gameplay and relaxation methods, providing insights into stress levels and brain activity.
Contribution
It introduces a novel EEG-based method for stress analysis during video gaming using rhythm-specific spectral features and compares regression models for stress-relaxation curves.
Findings
Gamer and non-gamer stress levels differ during gameplay.
Music relaxation reduces stress to near baseline levels.
4PL sigmoid regression outperforms quartic regression in modeling stress curves.
Abstract
For the emerging significance of mental stress, various research directives have been established over time to better understand the causes of stress and how to deal with it. In recent years, the rise of video gameplay is unprecedented, further triggered by the lockdown imposed due to the COVID-19 pandemic. This paper presents an end-to-end stress analysis for video gaming stimuli using EEG. The PSD value of the Alpha and Beta bands is computed to calculate the Beta-to-Alpha ratio (BAR). In this article, BAR is used to denote mental stress. Subjects are chosen based on various factors such as gender, gameplay experience, age, and BMI. EEG is recorded using Scan SynAmps2 Express equipment. There are three types of video gameplay: strategic, puzzle, and combinational. Relaxation is accomplished in this study by the use of music of various pitches. Two types of regression analysis are done…
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Taxonomy
TopicsHeart Rate Variability and Autonomic Control · Mind wandering and attention · EEG and Brain-Computer Interfaces
