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

作者： Cédric Cannard1,2, Helané Wahbeh2,3, Arnaud Delorme1,2,4 1Centre de Recherche Cerveau et Cognition (CerCo), CNRS,Toulouse III University, 2Institute of Noetic Sciences (IONS), 3Department of Neurology,Oregon Health & Science University, 4Swartz Center of Computational Neuroscience (SCCN), INC,UCSD

简介：

Overview

The BrainBeats toolbox is an open-source EEGLAB plugin aimed at facilitating the joint analysis of EEG and cardiovascular (ECG/PPG) signals. This toolbox enhances the study of the brain-heart interplay by providing tools for heartbeat-evoked potential (HEP) assessments and heart artifact extraction from EEG signals, ultimately improving reproducibility and accessibility in research.

Key Study Components

Area of Science

• Neuroscience
• Cardiovascular Research
• Signal Processing

Background

• Historically, the brain and heart systems have been studied separately, limiting understanding of their interactions.
• The BrainBeats toolbox addresses this gap by allowing researchers to explore the dynamic interplay between these systems.
• Insights into cognitive and emotional states' effects on cardiovascular function can lead to new therapeutic interventions.

Purpose of Study

• To provide a standardized tool for multimodal analysis of EEG and cardiovascular signals.
• To enhance understanding of brain-heart interactions and their implications for mental and physical health.
• To improve accessibility and reproducibility in related research.

Methods Used

• The study utilized the BrainBeats toolbox within the EEGLAB framework on MATLAB.
• EEG and cardiovascular signals (both ECG and PPG) were analyzed through graphical interfaces to assess HEP and heart artifacts.
• The protocol included setting paths in MATLAB, loading datasets, and performing feature extraction.
• Critical steps involved changing power line noise settings and validating outputs throughout the analysis.

Main Results

• Notable findings included significant heartbeat-evoked oscillations in the alpha band post-heartbeat.
• Inter-trial coherence analysis indicated no significant phase locking of EEG phase with heartbeats.
• Key outputs also included HRV features and EEG data, revealing important patterns associated with cardiac rhythms.

Conclusions

• The BrainBeats toolbox demonstrates potential for bridging the gap between brain and heart research.
• It enables a more comprehensive understanding of cognitive and emotional influences on cardiovascular health.
• The insights gained can inform future therapeutic strategies and interventions.

Frequently Asked Questions