The Institute of Noetic Sciences supported this research. We thank the developers of the original open-source algorithms that were adapted to develop some of BrainBeats&#39; algorithms.

Unveiling Brain-Heart Interplay with EEG and Cardio Signals

The authors have nothing to disclose.

Critical steps in the protocol 
Critical steps are described in steps 1.1-1.4. Warnings and error messages are implemented at various places in the toolbox to help users understand why they may encounter issues (e.g., electrode locations not loaded in the EEG data, file length being too short for calculating a reliable measure of ultra-low frequency HRV, signal quality being too low for any reliable analysis, etc.). Each function is documented for advanced users, and the parameters can be easily fin...

For a long time, the reductionist approach has dominated scientific inquiry in human physiology and cognition. This approach involved dissecting complex bodily and mental processes into smaller, more manageable components, allowing researchers to focus on individual systems in isolation. This strategy arose due to the challenges in studying the intricate and interconnected nature of the human body and mind1. Reductionism has been instrumental in understanding individual subsystems in isolation, such as elucidating the role of ion channels and action potentials for neural2 or cardiac3 communication. However, a significant gap remains in our understanding of how these isolated systems interact on a larger spatial and temporal scale. The multimodal (integrative or ecological) framework considers the human body a complex multidimensional system, where the mind is seen not as a product of the brain but as an activity of the living being, an activity that integrates the brain within the everyday functions of the human body4. The multimodal and reductionist approaches are not exclusive, just like we cannot study one neuron without the whole brain or the whole brain without understanding individual neuron properties. Together, they pave the way for a more comprehensive, synergetic understanding of human health, pathology, cognition, psychology, and consciousness. The present method aims to ease the multimodal investigation of the interplay between the brain and the heart by providing joint analysis of electroencephalography (EEG) and cardiovascular signals, namely electrocardiography (ECG) and photoplethysmography (PPG). This toolbox, implemented as an EEGLAB plugin in MATLAB, addresses existing methodological limitations and is made open source to facilitate accessibility and reproducibility in the scientific area. It implements the latest guidelines and recommendations into its design and default parameters to encourage users to follow known best practices. The proposed toolbox should be a valuable resource for researchers and clinicians interested in 1) studying&#160;heartbeat-evoked potentials&#160;, 2) extracting features from EEG and ECG/PPG signals, or 3) removing heart artifacts from EEG signals.
Heart-brain research 
The relationship between the heart and the brain has been historically studied via neuroimaging methods such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). Using these tools, researchers highlighted some brain regions associated with cardiovascular control (e.g., manipulation of heart rate and blood pressure5), showed the influence of heart rate on the BOLD signal6, or identified potential brain-body pathways contributing to coronary heart disease (i.e., stress-evoked blood pressure7). While these studies have significantly advanced our understanding of the complex interplay between the central nervous system (CNS) and cardiovascular function, these neuroimaging techniques are expensive, have limited availability, and are confined to controlled laboratory settings, which restricts their practicality for real-world and large-scale applications.
In contrast, EEG and ECG/PPG are more affordable and portable tools that offer the potential for studying brain-heart interactions in more diverse settings and populations or over longer periods, providing new opportunities. ECG measures the electrical signals generated by each heartbeat when the heart contracts and relaxes via electrodes placed on the skin (usually on the chest or arms)8. PPG measures blood volume changes in the microvascular tissues (i.e., blood flow and pulse rate) using a light source (e.g., LED) and a photodetector (commonly placed on a fingertip, wrist, or forehead), relying on how blood absorbs more light than the surrounding tissue9. Both methods provide valuable information about cardiovascular function but serve different purposes and offer distinct data types. Like ECG, EEG records the electrical fields generated by the synchronized activity of thousands of cortical neurons that propagate through the extracellular matrix, tissues, skull, and scalp until they reach the electrodes placed on the scalp&#39;s surface10. As such, the use of EEG and ECG/PPG holds great promise for advancing our understanding of the physiological, cognitive, and emotional processes underlying brain-heart interactions and their implications for human health and well-being. Therefore, capturing heart-brain interplay from EEG, ECG/PPG signals with the BrainBeats toolbox may be particularly useful for the following scientific areas: clinical diagnostic and forecasting, big data machine learning (ML), real-world self-monitoring11, and mobile brain/body imaging (MoBI)12,13.
Two approaches for jointly analyzing EEG and ECG signals 
There are two main approaches to studying interactions between EEG and cardiovascular signals:
The heartbeat-evoked potentials (HEP) in the time domain: event-related potentials (ERP), and the heartbeat-evoked oscillations (HEO) in the time-frequency domain: event-related spectral perturbations (ERSP) and inter-trial coherence (ITC). This approach examines how the brain processes each heartbeat. With millisecond (ms) accuracy, this method requires that both time series are perfectly synchronized and the heartbeats to be marked in the EEG signals. This approach has gained interest in recent years14,15,16,17,18,19.
Feature-based approach: this approach extracts EEG and heart-rate variability (HRV) features from continuous signals and examines associations between them. This has been done independently for EEG (often termed quantitative EEG or qEEG20), ECG21,22,23, and PPG24,25,26. This approach presents promising applications by capturing both state- and trait-related variables. Note that, for both EEG and cardiovascular signals, the longer the recording, the more dominant the trait variable27,28,29. Thus, the applications depend on the recording parameters. Feature-based analyses are gaining growing interest, providing new quantitative metrics for forecasting the development of mental and neurological disorders, treatment-response, or relapse30,31,32,33,34,35. This approach is especially compelling with large and real-world datasets (e.g., clinic, remote monitoring), which can be more easily obtained thanks to the recent innovations in wearable neurotechnology11. A less explored application is the identification of associations between specific brain and heart features, highlighting potential underlying central nervous system dynamics. Heart rate variability (HRV) can be calculated from both ECG and PPG signals. It provides information about the autonomous nervous system (ANS) by measuring the variations in time intervals between heartbeats (i.e., the normal-to-normal intervals)27. Increased sympathetic (SNS) activity (e.g., during stress or exercise) typically reduces HRV, while parasympathetic (PNS) activity (e.g., during relaxation) increases it. A slower breathing rate generally increases HRV due to enhanced PNS activity, especially for short recordings (&#60;10 min)27. Higher HRV scores generally suggest a more resilient and adaptable ANS, while a lower HRV can indicate stress, fatigue, or underlying health issues. Long HRV recordings (i.e., at least 24 h) provide a predictive prognosis for various health conditions, including cardiovascular diseases, stress, anxiety, and some neurological conditions27. Measures like blood pressure, heart rate, or cholesterol levels give information about the cardiovascular system&#39;s status. In contrast, HRV adds a dynamic aspect, showing how the heart responds to and recovers from stress.
BrainBeats&#39; advantages over existing methods 
While tools exist, as reviewed below, to process cardiovascular and EEG signals independently from each other, they cannot be jointly analyzed. Furthermore, most available means to process cardiovascular signals involve costly licensing, do not allow automated processing (especially beneficial for large datasets), have proprietary algorithms that prevent transparency and reproducibility, or require advanced programming skills by not providing a graphical user interface (GUI)36. To our knowledge, four open-source MATLAB toolboxes support HEP/HEO analysis with a GUI: the ecg-kit toolbox37, the BeMoBIL pipeline38, the HEPLAB EEGLAB plugin39, and the CARE-rCortex toolbox40. While HEPLAB, BeMoBIL, and ecg-kit facilitate HEP analysis by detecting heartbeats and marking them in the EEG signals, they do not provide statistical analysis or are limited to the time domain (i.e., HEP). The CARE-rCortex plugin addressed these issues by supporting ECG and respiratory signals, time-frequency domain analysis, statistics, and advanced baseline normalization and correction methods adapted to HEP/HEO analysis. However, it uses the Bonferroni method for statistical correction of the type 1 error (i.e., false positives), which is too conservative and not physiologically sound for EEG applications, leading to an increase in type II errors (i.e., false negatives)41. Furthermore, the toolbox does not offer command-line access for automation. Finally, recent studies recommend against baseline correction methods42,43,44, as they reduce the signal-to-noise ratio (SNR) and are statistically unnecessary and undesirable.
To address these limitations, we introduce the BrainBeats toolbox, currently implemented as an open-source EEGLAB plugin in the MATLAB environment. It incorporates the following advantages over previous methods:
1) An easy-to-use GUI and command-line capabilities (for programmers aiming to perform automated processing). 2) Validated algorithms, parameters, and guidelines for processing cardiovascular signals, such as detecting R peaks, interpolating RR artifacts, and computing HRV metrics (e.g., implanting guidelines for windowing, resampling, normalization, etc.27,45,46). This is important because Vest et al. demonstrated how modest differences in these processing steps can lead to divergent results, contributing to the lack of reproducibility and clinical applicability of HRV metrics46. 3) Validated algorithms, default parameters, and guidelines for processing EEG signals, including filtering and windowing44,47, re-referencing48,49, removal of abnormal channels and artifacts50,51,52, optimized ICA decomposition and classification of independent components53,54,55,56. The users can fine-tune all preprocessing parameters or even preprocess their EEG data with their preferred method before using the toolbox to match their needs (e.g., with EEGLAB clean_rawdata plugin50,52, the BeMoBIL pipeline38, the PREP pipeline57, etc.). 4) Heartbeat-evoked potentials (HEP, i.e., time domain) and oscillations (HEO; event-related spectral perturbations with wavelet or FFT methods, and inter-trial coherence are available through the standard EEGLAB software) from ECG signals. Parametric and nonparametric statistics with corrections for type 1 errors are available via EEGLAB&#39;s standard software. Nonparametric statistics include permutation statistics and spatiotemporal corrections for multiple comparisons (e.g., spatiotemporal clustering or threshold-free cluster enhancement)58,59. Users can use the LIMO-EEG plugin to implement hierarchical linear modeling, which accounts well for within and between-subjects variance and implements an assumption-free mass-univariate approach with robust control for type I and II errors60,61. The HEP/HEO data statistical analyses can be performed in the channel and independent component domains. 5) HEP/HEO and HRV analysis from PPG signals (for the first time for HEP/HEO). 6) Supports the joint extraction of EEG and HRV features for the first time. 7) The toolbox provides various data visualizations to inspect signals at various necessary processing steps and outputs at the subject level.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Method</td>
			<td>Detect R-peaks from ECG</td>
			<td>Detect R-waves from PPG</td>
			<td>HEP/HEO</td>
			<td>EEG &#38; HRV features</td>
			<td>Remove heart artifacts from EEG</td>
			<td>GUI</td>
			<td>Command line</td>
		</tr>
		<tr>
			<td>ecg-kit</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td></td>
			<td></td>
			<td></td>
			<td>X</td>
		</tr>
		<tr>
			<td>BeMoBIL</td>
			<td>X</td>
			<td></td>
			<td>X</td>
			<td></td>
			<td></td>
			<td></td>
			<td>X</td>
		</tr>
		<tr>
			<td>HEPLAB</td>
			<td>X</td>
			<td></td>
			<td>X</td>
			<td></td>
			<td></td>
			<td>X</td>
			<td>X</td>
		</tr>
		<tr>
			<td>CARE-rCortex</td>
			<td>X</td>
			<td></td>
			<td>X</td>
			<td></td>
			<td></td>
			<td>X</td>
			<td>X</td>
		</tr>
		<tr>
			<td>BrainBeats</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
		</tr>
	</tbody>
</table>
TABLE 1:&#160;Novelties brought by BrainBeats relative to pre-existing, similar methods.
Information to help readers decide whether the method is appropriate for them 
This toolbox is appropriate for any researcher or clinician with EEG and ECG/PPG data. The plugin does not yet support importing EEG and ECG/PPG signals from separate files (although this feature will be available soon). The toolbox is appropriate for anyone aiming to perform HEP/HEO analysis, extract EEG and/or HRV features with standardized methods, or simply remove heart artifacts from EEG signals. See Figure 1 for a block diagram summarizing BrainBeats&#39; overall flow and methods.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65829/65829fig01.jpg" /> 
FIGURE 1.&#160;Block diagram summarizing BrainBeats&#39; overall architecture and flow.&#160;The operations that are common across the three methods are brown. Operations specific to heartbeat-evoked potentials (HEP) and oscillations (HEO) are green. Operations specific to the extraction of EEG and HRV features are blue. Operations specific to removing heart artifacts from the EEG signals are red. <a href="https://www.jove.com/files/ftp_upload/65829/65829fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>EEGLAB</td>
			<td>Swartz Center for Computational Neuroscience (SCCN)</td>
			<td></td>
			<td>Free/Open-source</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>The Mathworks, Inc.</td>
			<td></td>
			<td>Requires a license</td>
		</tr>
		<tr>
			<td>Windows PC</td>
			<td>Lenovo, Inc.</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

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

The scope of the BrainBeats toolbox is to provide an open source and automated tool for conducting research on brain-heart interplay. It allows researchers to perform multimodal analysis of EEG and cardiovascular signals through standardized signal processing, feature extraction, graphical user interface, and data visualization. As a result of the historical reductionist approach in science, the brain and the heart systems have been separated and isolated, leading to a limited understanding of their interaction and mutual influence.The BrainBeats toolbox aims to bridge this gap. Understanding the interplay between the brain and the heart allows researchers to gain deeper insights into how cognitive and emotional states influence cardiovascular function and vice versa. In turn, these insights can lead to new therapeutic interventions and treatments.

First, the BrainBeats plugin was used to preprocess EEG and ECG data, identify and remove artifacts, and analyze heartbeat-evoked potentials (HEP) and oscillations (HEO). BrainBeats successfully detected the RR intervals from the ECG signal and some RR artifacts (Figure 2). BrainBeats also reported in the command window that 11/305 (3.61%) of the heartbeats were flagged as artifacts and interpolated. The average signal quality index (SQI) of the RR intervals (before interpolation) has a valu...

Read this Scientific Article Protocol about BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals at JoVE.com

The BrainBeats toolbox is an open-source EEGLAB plugin designed to jointly analyze EEG and cardiovascular (ECG/PPG) signals. It includes heartbeat-evoked potentials (HEP) assessment, feature-based analysis, and heart artifact extraction from EEG signals. The protocol will aid in studying brain-heart interplay through two lenses (HEP and features), enhancing reproducibility and accessibility.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human ...

brainbeats-as-an-open-source-eeglab-plugin-to-jointly-analyze-eeg

Research

JoVE Journal

Neuroscience

1.5K visualizações.  Toulouse III University. O escopo da caixa de ferramentas BrainBeats é fornecer uma ferramenta automatizada e de código aberto para a realização de pesquisas sobre a interação cérebro-coração. Ele permite que os pesquisadores realizem análises multimodais de EEG e sinais cardiovasculares por meio de processamento de sinal padronizado, extração de recursos, interface gráfica do usuário e visualização de dados. Como resultado da abordagem reducionista histórica na ciência, o cérebro e os sistemas car...

Vídeo: Autor em destaque: Avançando no estudo da interação cérebro-coração com um plug-in EEGLAB abrangente para análise de sinal multimodal

Setting Up and Analyzing Heartbeat-Evoked Potentials (HEP) with BrainBeats in EEGLAB

To begin, open MATLAB on the computer. In MATLAB's home panel, click the Set Path button to add the path to the EEGLAB folder. Click the Add Folder button and select the eeglab folder that has been unzipped.Then click Save, followed by Close. In MATLAB's command window, type eeglab to launch EEGLAB. To install the BrainBeats plugin, click File, followed by Manage EEGLAB Extensions.Then in the search bar, type brainbeats, select the brainbeats plugin in the list, and click Install. For loading the sample dataset into EEGLAB, click File and Load existing data. Navigate to the eeglab folder, then go to the plugins and BrainBeats folders.Open the sample data folder and select the file dataset.set. In EEGLAB, sequentially click Tools, BrainBeats, and 1st level, subject level, to open the BrainBeats first general user interface or GUI. Select heartbeat-evoked potentials, or HEP, as the analysis to run and ECG as the heart data type.Then click on the button to display the list of channels and select the ECG channel. Keep the options visualize outputs and save outputs selected and click OK.In the second GUI window, from the preprocess EEG section, change the power line noise to 50 Hz and click OK to launch. Once the warning message to remove the detected PPG channel appears, click Yes.For the PPG signal, load the dataset. set file again, then in the EEGLAB, sequentially click Tools, BrainBeats, and 1st level, subject level, to open BrainBeats'first GUI. Select heartbeat-evoked potentials, or HEP, as the analysis to run and PPG as the heart data type.Then click on the button to display the list of channels and select the PPG channel. Keep the options visualize outputs and save outputs selected and click OK.In the second GUI window from the preprocess EEG section, change the power line noise to 50 Hz and click OK to launch. Once the warning message to remove the extra ECG channel appears, click yes.BrainBeats successfully detected the RR intervals from the ECG signal and some RR artifacts. A significant heartbeat-evoked oscillation in the alpha band from 150 to 400 milliseconds post-heartbeat at a fronto-central scalp site was observed. On the other hand, the intertrial coherence analysis suggested no significant phase locking or resetting of the EEG phase with respect to the heartbeats.BrainBeats also detected the RR intervals from the PPG signal and some RR artifacts. A significant effect at the fronto-central scalp site in almost the whole time window following the R-wave and almost the whole frequency range was observed. Intertrial coherence analysis suggesting no significant phase locking or resetting of the EEG phase.

Extracting EEG and Heart-Rate Variability (HRV) Features and Heart Artifacts with BrainBeats in EEGLAB

To load the sample dataset into EEGLAB, click File and Load existing data. Navigate to the EEGLAB folder, then go to the plugins folder and BrainBeats folder. Open the sample data folder and select the file dataset.set.After loading the sample dataset into EEGLAB, sequentially click Tools, BrainBeats, and 1st level subject level to open the main GUI and to select the parameters. Select Extract EEG HRV features for the analysis type and ECG for the heart signal type. Select ECG in the list of electrode labels and click Ok.Then, a second GUI window pops up with different parameters for the EEG preprocessing and extracting HRV and EEG features.In the Preprocess EEG section, change the Power line noise to 50 hertz and click Ok to launch. Once the warning message to remove the extra PPG channel appears, click Yes. At the end of all operations, type eegh in MATLAB's command window to print the command line and repeat all steps via a single command line.After loading the same dataset file, sequentially click Tools, BrainBeats, and 1st level subject level to open the main GUI to select the parameters. Select Extract EEG HRV features for the analysis and PPG for the heart signal type. Select PPG for the channel name and click Ok.In the second GUI window, from the Preprocess EEG section, change the Power line noise to 50 hertz and click Ok to run.Once the warning message to remove the detected ECG channel appears, click yes, and review the generated output. Load the sample dataset file, dataset.set. Then, sequentially click Tools, BrainBeats, and 1st level subject level to open the main GUI.Select Extract heart artifacts from EEG signals for the analysis type and ECG for the heart signal type. Type ECG in the list of electrode labels and click Ok.After selecting preprocessing parameters for EEG signals, select 50 hertz for line noise, check the Boost mode box, and click Ok to launch. Once the warning message to remove the extra PPG channel appears, click Yes.Using BrainBeats, EEG and heart-rate variability features were extracted from ECG signals, revealing a peak in heart-rate variability power spectral density at approximately 0.19 hertz within the high-frequency band, and an EEG at approximately 10.5 hertz in the alpha band. Scalp topographies showed predominant localizations in posterior regions with higher complexity in the frontal right and posterior areas. A subsequent analysis using PPG signals for heart-rate variability indicated a peak at approximately 0.04 hertz in the low-frequency band and a bifurcated peak around approximately 0.19 hertz, suggesting lower PPG signal quality.