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 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 aim 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 or interventions and treatments.

To begin, open MATLAB on the computer. In MATLAB's home panel, click the Set Path button to add the path to the EEG lab folder. Click the Add Folder button and select the EEG lab folder that has been unzipped.

Then click Save, followed by Close. In MATLAB's command window, type EEG lab to launch EEG lab. To install the BrainBeats plugin, click file followed by manage EEG lab extensions.

Then in the search bar, type BrainBeats, select the BrainBeats plugin in the list and click install. For loading the sample dataset into EEG lab, click File and load existing data. Navigate to the EEG lab folder, then go to the plugins and BrainBeats folders.

Open the sample data folder and select the file dataset.set. In EEG lab, sequentially click tools, BrainBeats, and first level, subject level to open the brain beats 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 recommended and save outputs selected and click OK.In the second GUI window, from the pre-process EEG section, change the power line noise to 50 hertz 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 EEG lab, sequentially click tools, BrainBeats, and a first 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 recommended and save outputs selected and click OK.In the second GUI window, from the pre-process EEG section, change the power line noise to 50 hertz 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 intra-trial 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.

Inter-trial coherence analysis suggesting no significant phase locking or resetting of the EEG phase. To load the sample dataset into EEG lab, click File and load existing data. Navigate to the EEG lab 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 EEG lab, sequentially click tools, BrainBeats, and first level subject level to open the main GUI and to select the parameters. Select extract EEG and 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 pre-processing and extracting HRV and EEG features. In the pre-process 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 first level subject level to open the main GUI to select the parameters. Select extract EEG and 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 pre-process EEG section, change the power line noise to 50 hertz and click okay 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 first 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 pre-processing 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.