EEG based stress analysis using rhythm specific spectral feature for video game play.

Area of Science:

• Neuroscience and Cognitive Science
• Human-Computer Interaction
• Psychology

Background:

• Mental stress from video gameplay is a growing concern, exacerbated by increased gaming during the COVID-19 pandemic.
• Existing research often focuses on physical stress symptoms or EEG analysis without addressing post-gameplay relaxation.
• There's a need for coordinated studies to understand gameplay stress and inform game design and public awareness.

Purpose of the Study:

• To present an end-to-end stress analysis for video gaming stimuli using Electroencephalogram (EEG).
• To quantify mental stress using the Beta-to-Alpha ratio (BAR) derived from EEG data.
• To investigate effective relaxation methods post-gaming and model stress/relaxation curves.

Main Methods:

• EEG was recorded from subjects varying in gender, gaming experience, age, and BMI.
• The Beta-to-Alpha ratio (BAR) was computed from Alpha and Beta band power spectral density.
• Stress was induced by strategic, puzzle, and combinational gameplay, followed by relaxation using music of various pitches. Regression analysis and brain topography were employed.

Main Results:

• Non-gamers exhibited higher BAR (2.403) than gamers (2.218) after 1 hour of strategic gameplay.
• Listening to low-pitch music for 12 minutes reduced gamers' BAR to near baseline (0.709).
• Quartic regression demonstrated better performance than the 4PL symmetrical sigmoid function for modeling relaxation curves.

Conclusions:

• Non-gamers experience more stress than gamers, and strategic games induce higher mental stress.
• Low-pitch music is an effective relaxation medium, significantly reducing stress levels.
• Quartic regression provided a better fit for modeling the relaxation curve compared to the 4PL function.