Connection Between Alpha Subbands and Self-Reported Overall Health Outcomes

ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)
Taylor E Zurlinden (Creator)
East Carolina University (ECU )
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Abstract: Background: Over the course of the past century , the average life span in the US has increased by approximately three decades (Kindig , Nobles , & Zidan , 2018). Part of this success is due to the ability to detect diseases , and another large portion is the advance in treatment. The shift in medicine is now towards learning how to predict diseases before they become damaging (Chen , 1994). There are known psychological factors that are predictive of health outcomes , and there are known correlations between these factors and alpha rhythms (8-12 Hz) at a subband level. The need to utilize current testing efficiently and reduce medical waste (financially and physically) is the backdrop of this exploratory study. Purpose: The purpose of this study is to expand the current literature on alpha subband analysis , as well as explore the relationship to known predictors of health outcomes. Additionally , the study aims to explore asymmetry at various scalp sites. Methods: Data were collected from 75 undergraduates at East Carolina University. The group consisted of 50 women and 25 men , and the mean age of participants was 20.19 years. Participants completed self-report measures that assessed overall quality of physical and mental health as well as affect. Baseline EEG recording , which consisted of four eyes-open and four eyes-closed trials was also completed. Results: Unidirectional bivariate correlation analysis between behavioral data and alpha subband power was performed for 58 participants , as well as non-direactional analysis of asymmetry and behavioral data. Analysis showed a negative correlation between sleepiness and high alpha (10.7 -12.7 Hz) power. Additionally , there was a negative correlation between Physical [health] Composite Score (PCS) and high alpha , and a positive correlation between high alpha and Mental [health] Composite Score (MCS). Exploratory analysis revealed relationships between various scalp sites and the behavioral data , indicating patterns of alpha subband lateralization and self-reported personality and health characteristics. A correlation between self-reported pain and middle alpha (8.8- 10.7 Hz) was also revealed through exploratory analysis. Discussion: The results of this study were interpreted in light of current research on alpha subband analysis as well as the literature on asymmetry.

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Language: English
Date: 2019
Alpha Subband

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