Connection Between Alpha Subbands and Self-Reported Overall Health Outcomes

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.