Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System

Frontal Alpha Asymmetry and Its Modulation by Monoaminergic Neurotransmitters in Depression

Frontal alpha asymmetry (FAA) is an electroencephalography (EEG) measure that quantifies trait-like left versus right hemisphere lateralization in alpha power. Increased FAA indicates relatively greater left than right frontal cortex activation and is associated with enhanced reward-related approach behaviors rather than avoidance or withdrawal. Studies dating back several decades have often suggested that having greater FAA supports enhanced positive affect and protection against major depressive disorder (MDD), whereas having greater right frontal activation (i.e., reduced FAA) is associated with negative affect and risk for MDD. While this hypothesis is widely known, a number of other studies instead have found increased FAA in MDD, or evidence that either leftward or rightward bias in FAA is associated with depression. Here we briefly review the literature on leftward or rightward lateralization in FAA in MDD, and find much evidence that MDD is not always characterized by reduced FAA. We also review the limited literature on FAA and monoaminergic neurotransmitter systems, including pharmacologic agents that act on them. Studies of serotonin in particular provide genetic and pharmacologic evidence for modulation of FAA, where some of these data may suggest that serotonin reduces FAA. In a synthesis of the collective literature on FAA and the monoamines, we suggest that serotonin and norepinephrine may differentially affect FAA, with serotonin tending to promote right frontal activation and norepinephrine biased toward left frontal activation. These putative differences in frontal lateralization may influence MDD phenotypes or potential subtypes of the disorder, and suggest pharmacologic treatment strategies.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

Posttraumatic growth EEG neuromarkers: translational neural comparisons with resilience and PTSD in trauma-exposed healthy adults

Background: Supporting wellbeing beyond symptom reduction is necessary in trauma care. Research suggests increased posttraumatic growth (PTG) may promote wellbeing more effectively than posttraumatic stress disorder (PTSD) symptom reduction alone. Understanding neurobiological mechanisms of PTG would support PTG intervention development. However, most PTG research to-date has been cross-sectional data self-reported through surveys or interviews.Objective: Neural evidence of PTG and its coexistence with resilience and PTSD is limited. To advance neural PTG literature and contribute translational neuroscientific knowledge necessary to develop future objectively measurable neural-based PTG interventions.Method: Alpha frequency EEG and validated psychological inventories measuring PTG, resilience, and PTSD symptoms were collected from 30 trauma-exposed healthy adults amidst the COVID-19 pandemic. EEG data were collected using custom MNE-Python software, and a wireless OpenBCI 16-channel dry electrode EEG headset. Psychological inventory scores were analysed in SPSS Statistics and used to categorise the EEG data. Power spectral density analyses, t-tests and ANOVAs were conducted within EEGLab to identify brain activity differentiating high and low PTG, resilience, and PTSD symptoms.Results: Higher PTG was significantly differentiated from low PTG by higher alpha power in the left centro-temporal brain area around EEG electrode C3. A trend differentiating high PTG from PTSD was also indicated in this same location. Whole-scalp spectral topographies revealed alpha power EEG correlates of PTG, resilience and PTSD symptoms shared limited, but potentially meaningful similarities.Conclusion: This research provides the first comparative neural topographies of PTG, resilience and PTSD symptoms in the known literature. Results provide objective neural evidence supporting existing theory depicting PTG, resilience and PTSD as independent, yet co-occurring constructs. PTG neuromarker alpha C3 significantly delineated high from low PTG and warrants further investigation for potential clinical application. Findings provide foundation for future neural-based interventions and research for enhancing PTG in trauma-exposed individuals.

Quantifying Digital Biomarkers for Well-Being: Stress, Anxiety, Positive and Negative Affect via Wearable Devices and Their Time-Based Predictions

Wearable devices have become ubiquitous, collecting rich temporal data that offers valuable insights into human activities, health monitoring, and behavior analysis. Leveraging these data, researchers have developed innovative approaches to classify and predict time-based patterns and events in human life. Time-based techniques allow the capture of intricate temporal dependencies, which is the nature of the data coming from wearable devices. This paper focuses on predicting well-being factors, such as stress, anxiety, and positive and negative affect, on the Tesserae dataset collected from office workers. We examine the performance of different methodologies, including deep-learning architectures, LSTM, ensemble techniques, Random Forest (RF), and XGBoost, and compare their performances for time-based and non-time-based versions. In time-based versions, we investigate the effect of previous records of well-being factors on the upcoming ones. The overall results show that time-based LSTM performs the best among conventional (non-time-based) RF, XGBoost, and LSTM. The performance even increases when we consider a more extended previous period, in this case, 3 past-days rather than 1 past-day to predict the next day. Furthermore, we explore the corresponding biomarkers for each well-being factor using feature ranking. The obtained rankings are compatible with the psychological literature. In this work, we validated them based on device measurements rather than subjective survey responses.

Effectiveness and Changes in Brain Functions by an Occupational Therapy Program Incorporating Mindfulness in Outpatients with Anxiety and Depression: A Randomized Controlled Trial

INTRODUCTION

This study examined the efficacy of an 8-week occupational therapy program incorporating mindfulness (MOT) as a form of psychiatric rehabilitation to ameliorate residual social and occupational impairment in patients with anxiety disorders and depression. The objective was to evaluate the effects of MOT on their personal well-being and to assess the impact of MOT on brain function using quantitative electroencephalography (qEEG).

METHODS

This study was a randomized, wait-list control trial with assessments performed at baseline, post-intervention (9 weeks), and follow-up (18 weeks) in outpatients with anxiety disorders and depression. The MOT was conducted in small groups, comprising eight weekly 1.5-h sessions. The primary outcome was the mean score change between the pre- and post-interventions with Questionnaire about the Process of Recovery (QPR) scale. Other clinical assessments and qEEG served as secondary and biological outcomes, respectively.

RESULTS

A total of 25 patients (mean age: 44.1) were included in the analysis. The MOT group demonstrated a significantly improved QPR compared to the control group after adjusting for baseline covariates (p < 0.01). This improvement was sustained for 9 weeks after the 8-week intervention. In the qEEG analysis, a significant increase in current source density in the β2 band of the left dorsolateral prefrontal cortex was observed in the MOT group compared to the control group (p < 0.02).

CONCLUSION

This study demonstrates that MOT improves subjective well-being and potentially, global function. This suggests that MOT may serve as a viable option for those whose symptoms have abated but who still struggle with social and occupational functioning.

Regulation of brain cognitive states through auditory, gustatory, and olfactory stimulation with wearable monitoring

Inspired by advances in wearable technologies, we design and perform human-subject experiments. We aim to investigate the effects of applying safe actuation (i.e., auditory, gustatory, and olfactory) for the purpose of regulating cognitive arousal and enhancing the performance states. In two proposed experiments, subjects are asked to perform a working memory experiment called n-back tasks. Next, we incorporate listening to different types of music, drinking coffee, and smelling perfume as safe actuators. We employ signal processing methods to seamlessly infer participants' brain cognitive states. The results demonstrate the effectiveness of the proposed safe actuation in regulating the arousal state and enhancing performance levels. Employing only wearable devices for human monitoring and using safe actuation intervention are the key components of the proposed experiments. Our dataset fills the existing gap of the lack of publicly available datasets for the self-management of internal brain states using wearable devices and safe everyday actuators. This dataset enables further machine learning and system identification investigations to facilitate future smart work environments. This would lead us to the ultimate idea of developing practical automated personalized closed-loop architectures for managing internal brain states and enhancing the quality of life.

Analysis of the Correlation between Frontal Alpha Asymmetry of Electroencephalography and Short-Term Subjective Well-Being Changes

Subjective well-being (SWB) describes how well people experience and evaluate their current condition. Previous studies with electroencephalography (EEG) have shown that SWB can be related to frontal alpha asymmetry (FAA). While those studies only considered a single SWB score for each experimental session, our goal is to investigate such a correlation for individuals with a possibly different SWB every 60 or 30 s. Therefore, we conducted two experiments with 30 participants each. We used different temperature and humidity settings and asked the participants to periodically rate their SWB. We computed the FAA from EEG over different time intervals and associated the given SWB, leading to pairs of (FAA, SWB) values. After correcting the imbalance in the data with the Synthetic Minority Over-sampling Technique (SMOTE), we performed a linear regression and found a positive linear correlation between FAA and SWB. We also studied the best time interval sizes for determining FAA around each SWB score. We found that using an interval of 10 s before recording the SWB score yields the best results.

The Noetic Signature Inventory: Development, Exploration, and Initial Validation

Noetic comes from the Greek word noēsis, meaning inner wisdom or direct knowing. Noetic experiences often transcend the perception of our five senses and are ubiquitous worldwide, although no instrument exists to evaluate noetic characteristics both within and between individuals. We developed the Noetic Signature Inventory (NSI) through an iterative qualitative and statistical process as a tool to subjectively assess noetic characteristics. Study 1 developed and evaluated a 175-item NSI using 521 self-selected research participants, resulting in a 46-item NSI with an 11-factor model solution. Study 2 examined the 11-factor solution, construct validity, and test-retest reliability, resulting in a 44-item NSI with a 12-factor model solution. Study 3 confirmed the final 44-item NSI in a diverse population. The 12-factors were: (1) Inner Knowing, (2) Embodied Sensations, (3) Visualizing to Access or Affect, (4) Inner Knowing Through Touch, (5) Healing, (6) Knowing the Future, (7) Physical Sensations from Other People, (8) Knowing Yourself, (9) Knowing Other's Minds, (10) Apparent Communication with Non-physical Beings, (11) Knowing Through Dreams, and (12) Inner Voice. The NSI demonstrated internal consistency, convergent and divergent content validity, and test-retest reliability. The NSI can be used for the future studies to evaluate intra- and inter-individual variation of noetic experiences.