BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

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.

Trends in waking salivary alpha-amylase levels following healing lucid dreams.

Introduction: Salivary alpha-amylase (sAA) is considered a marker of autonomic nervous system activity in stress research, and atypical waking sAA responses have been reported for traumatized individuals. Lucid dreams, characterized by a dreamer's awareness of their dream state while remaining asleep, have shown promising preliminary evidence of their potential to enhance mental health. This study's objective was to evaluate sAA in relation to healing lucid dreams. Methods: Participants experiencing PTSD symptoms attended a six-day workshop delivered via live video designed to teach techniques for transforming trauma through dreamwork and dream lucidity. Participants (n = 20) collected saliva samples each morning, immediately upon awakening (Time 1) and 30 min afterward (Time 2). sAA levels were determined by enzymatic assay, and the waking sAA slope was calculated as the difference of Time 2 minus Time 1. Participants completed dream reports each morning, with a dream classified as a 'healing lucid dream' when they reported attaining lucidity and remembered their intention to manifest a healing experience within the dreamscape. Results: Of eight participants experiencing healing lucid dreams, four were able to provide usable saliva samples. Statistical tests on these four participants were not significant because of low power. However, nonsignificant positive associations were observed between experiencing more healing lucid dreams and increased waking sAA slope. Conclusion: The results did not reveal a consistent effect of healing lucid dreams on waking sAA slope. Identifying meaningful patterns in this relationship will require larger samples and more stringent control over saliva collection procedures in future studies.