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Updated: Jun 26, 2025
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Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
Published on: April 26, 2024
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BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
Cédric Cannard1, Helané Wahbeh2, Arnaud Delorme3
1Centre de Recherche Cerveau et Cognition (CerCo), CNRS, Toulouse III University; Institute of Noetic Sciences (IONS); ccannard@noetic.org.
Journal of Visualized Experiments : Jove
|May 13, 2024
Summary
BrainBeats, an open-source EEGLAB plugin, offers standardized tools for analyzing brain-heart interactions. It enhances reproducibility and accessibility in multimodal physiological signal research.
Area of Science:
- Neuroscience
- Cardiovascular Physiology
- Biomedical Engineering
Background:
- Multimodal analysis of brain-cardiovascular signals is complex due to a lack of standardization.
- Existing methods for electroencephalography (EEG) and heart-rate variability (HRV) feature extraction lack standardization, impacting clinical diagnostics and machine learning (ML) models.
- Reproducibility and automation challenges hinder large-scale analysis of brain-heart interplay.
Purpose of the Study:
- To introduce the BrainBeats toolbox, an open-source EEGLAB plugin designed to address limitations in multimodal brain-heart signal analysis.
- To provide standardized protocols for assessing brain-heart interplay, extracting EEG and HRV features, and removing cardiac artifacts from EEG.
- To enhance the accessibility and reproducibility of research on brain-heart interactions.
Main Methods:
- Development of the BrainBeats toolbox as an EEGLAB plugin.
- Integration of three core protocols: Heartbeat-evoked potentials (HEP) and oscillations (HEO) analysis, EEG and HRV feature extraction, and automated artifact removal.
- Utilization of a graphical user interface (GUI) and command-line options for user-friendly parameter adjustment.
Main Results:
- BrainBeats enables millisecond-accurate assessment of time-locked brain-heart interplay via HEP and HEO.
- Facilitates examination of associations between brain and heart metrics and the development of robust ML models using extracted EEG and HRV features.
- Automates the removal of cardiac artifacts from EEG signals, improving the integrity of EEG analysis.
Conclusions:
- The BrainBeats toolbox offers a standardized, accessible, and reproducible solution for multimodal brain-heart signal analysis.
- It empowers researchers to investigate brain-heart interactions more effectively, supporting advancements in physiological understanding and health outcomes.
- The integrated protocols and user-friendly interface facilitate diverse research applications, from clinical diagnostics to ML model development.
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