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.
