A Big-data Study of Well-being and its EEG Correlates  Cedric Cannard , Helane Wahbeh, Arnaud Delorme (Research, Institute of Noetic Sciences; Paul Sabatier University, San Francisco, CA )   C15

Over the last several decades, research into well-being has painted a large and complex picture of the numerous factors contributing to an indiviudals' perceived sense of well-being (e.g. social relationships, socioeconomic status). Certain forms of training such as physical exercise, education, or meditation can also influence well-being over the lifespan by inducing neuroplastic changes in the brain structure and function. Recent technological and methodological advancements now enable big-data analyses which can highlight previously unidentified population-level trends in behavior, medicine, brain function, and more. Understanding these trends will help us to identify the nuanced relationships between these factors, the brain, and their impact on perceived well-being. Furthermore, identifying population-level neural correlates and markers of well-being may help to illuminate the underlying mechanisms implicated in states and traits of well-being, and inform the development of novel interventions and applications. Methods: Over 400 participants were recruited at the Institute of Noetic Sciences are included in this study. Well-being scores were assessed using the Arizona Integrative Outcomes Scale (AIOS). Other questionnaires included in the survey assessed sociodemographics, creativity scores, subjective interconnectedness, compassion, and positive and negative affect levels. Once the survey was completed, the participants' resting EEG activity was recorded during a breath-counting exercise using a wearable EEG headset to allow collection of a whole group within a short time frame. An automatic artifact rejection method has been developed to preprocess all the EEG data. Research aims: To identify the specific factors that correlate strongly with well-being across the population. To determine what types of resting EEG activity are associated with high versus low well-being scores. Hypothesis: Low well-being scores will correspond with increased broad-band gamma activity, increased delta-beta coherence (suggested to reflect emotion regulation processes via the synchronization between the limbic and cortical systems), and increased resting frontal asymmetry (i.e. a hyperactivation of alpha and a hypoactivation of theta in the right hemisphere). Results: Preliminary results show that age, creativity scores, interconnectedness (with others and with nature), negative affect, and compassion levels are significantly correlated with AIOS scores (p < 0.001, Pearson correlation). EEG results are currently being analyzed and will be included in the presentation. They will include the differences in EEG activity between the participants with high well-being scores versus low well-being scores: in the broad-band gamma, delta-beta coherence, and frontal asymmetry ratios. Conclusion: New factors were found to be significantly associated with well-being. EEG results will be presented and further our understanding of the interactions between the brain and well-being such as EEG asymmetries and EEG dynamics between the limbic and cortical systems. Further directions include interventions such as neurofeedback training based on these findings that can reshape functional and structural features of the cortex, and therefore elevate well-being levels in the long term.