Integrating Brain Machine Interface and ANOVA for Comprehensive Analysis of Stress Parameters: A Multidisciplinary Study
Keywords:
ANOVA-based statistical evaluation, Brain Machine Interface, Mental health, Open BCI for EEG analysis, Physiological indicators, StressAbstract
Modern life is rife with stress, detrimentally impacting both physical and mental well-being. The study delves into this issue, utilizing Open BCI for experimental analysis and ANOVA to assess stress's influence on physiological and psychological indicators, such as Blood Pressure (BP), Heart Rate (HR), Electroencephalogram (EEG), and the Perceived Stress Scale (PSS). While prior research has centered on mindfulness meditation, the study extends its focus to Heartfulness meditation. By correlating PSS with vital parameters, the relationship between perceived stress and physiological responses is explored, shedding light on stress's potential health ramifications. The rigorous statistical analysis quantifies variations in physiological parameters triggered by negative emotions, enhancing precision and dependability. The study's primary aim is to predict stress levels by analyzing factors including BP, HR, EEG, and PSS, with Heartfulness meditation as the intervention. Results reveal notable shifts in physiological indicators due to negative emotions, with increased HR and BP indicating increased arousal. Following meditation, these parameters decreased by around 10%, suggesting Heartfulness relaxation meditation's efficacy in mitigating negative emotions. These findings hold significance for healthcare professionals and stress management researchers, offering insights into devising more effective interventions based on quantified impacts of negative emotions on physiological indicators. Furthermore, the study sets the stage for future investigations into the intricate interplay between negative emotions, physiological metrics, and overall well-being.
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