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The BrainBeats toolbox, implemented as an EEGLAB plugin, allows joint processing and analysis of EEG and cardiovascular signals (ECG and PPG) for brain-heart interplay research. Both the general user interface (GUI) and command line are supported (see tutorial). BrainBeats currently supports: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO); 2) Extraction of EEG and HRV features; 3) Extraction of heart artifacts from EEG signals; 4) brain-heart coherence.
1) Process EEG data for heartbeat-evoked potentials (HEP) analysis using ECG or PPG signals. Steps include signal processing of EEG and cardiovascular signals, inserting R-peak markers into the EEG data, segmentation around the R-peaks with optimal window length, time-frequency decomposition.
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1) Process EEG data for heartbeat-evoked potentials (HEP) analysis using ECG or PPG signals. Steps include signal processing of EEG and cardiovascular signals, inserting R-peak markers into the EEG data, segmentation around the R-peaks with optimal window length, and time-frequency decomposition.
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Example of HEP at the subject level, obtained from simultaneous EEG-ECG signals
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Example of HEP at the subject level, obtained from simultaneous EEG-ECG signals (the cardiac field artifact was preserved here for illustration).
Example of HEP at the subject level, obtained from simultaneous EEG-PPG signals
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Example of HEP at the subject level, obtained from simultaneous EEG-PPG signals (note that with PPG, we must correct for the delay between the electrical and mechanical cardiac events so that the estimated heartbeat times correspond to the R-peaks of an ECG; ~200-400 ms).
- Some data containing EEG and cardiovascular signals (ECG or PPG) within the same file (i.e. recorded simultaneously).
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Or use the tutorial dataset provided in this repository located in the "sample_data" folder.
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Or use the tutorial dataset provided in this repository located in the "sample_data" folder. Source: sub-32 in https://nemar.org/dataexplorer/detail?dataset_id=ds003838
Cannard, C., Wahbeh, H., & Delorme, A. (2024). BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals. Journal of visualized experiments: JoVE, (206).
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## BrainBeats was used and cited in:
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Carbone, F., Silva, M., Leemann, B., Hund-Georgiadis, M., & Hediger, K. (2025). Registered Report Stage I: Neurological and physiological effects of animal-assisted treatments for patients in a minimally conscious state: a randomized, controlled cross-over study. Neuroscience.
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Balasubramanian, K. et al. (2025). Complexity Measures in Biomedical Signal Analysis: A Clinically-Grounded Survey Across EEG, ECG, Intracranial Pressure, and Photoplethysmogram Modalities. IEEE Access.
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Park, S. et al. (2025). Improving single-trial detection of error-related potentials by considering the effect of heartbeat-evoked potentials in a motor imagery-based brain-computer interface. Computers in Biology and Medicine, 195, 110563.
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Abdullah, J. et al. (2025). Mathematical Decoding of the Correlation Between Different Organs' Activities: A Review. Fractals.
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Carbone, F. et al. (2025). Registered Report Stage I: Neurological and physiological effects of animal-assisted treatments for patients in a minimally conscious state: a randomized, controlled cross-over study. Neuroscience.
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Remiszewski, M. (2025). Long-term Aerobic Exercise Enhances Interoception and Reduces Symptoms of Depression and Anxiety in Physically Inactive Young Adults: A Randomized Controlled Trial. Psychology of Sport and Exercise, 102939.
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Chowdhury, et al. (2025). Neural Signals, Machine Learning, and the Future of Inner Speech Recognition. Frontiers in Human Neuroscience, 19, 1637174.
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Naaz, R., & Ahmad, S. (2025). ECG Data Mining Approach for Detection of Arrhythmia Using Machine Learning. In 2025 3rd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) (pp. 52-57). IEEE.
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Cheng, X., Maess, B., & Schirmer, A. (2025). A Pleasure That Lasts: Convergent neural processes underpin comfort with prolonged gentle stroking. Cortex.
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Georgaras, E., & Vourvopoulos, A. (2025). Physiological assessment of brain, cardiovascular, and respiratory changes in multimodal motor imagery brain-computer interface training. Research in Biomedical Engineering and Technology, 12(1), 2471680.
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Park, S., Ha, J., & Kim, L. (2025). Improving single-trial detection of error-related potentials by considering the effect of heartbeat-evoked potentials in a motor imagery-based brain-computer interface. Computers in Biology and Medicine, 195, 110563.
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Perez, T. M., Drake, E., & Sullivan, S. (2024). Assessing central nervous system and peripheral nervous system functioning in resting and non-resting conditions in a healthy adult population: A feasibility study. Chiropractic Journal of Australia (Online), 51(1), 1-32.
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Akuthota, S., Rajkumar, K., & Janapati, R. (2024). Intelligent EEG Artifact Removal in Motor ImageryBCI: Synergizing FCIF, FCFBCSP, and Modified DNN with SNR, PSD, and Spectral Coherence Evaluation. In 2024 International Conference on Circuit, Systems and Communication (ICCSC) IEEE.
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Ingolfsson et al. (2024). Brainfusenet: Enhancing wearable seizure detection through eeg-ppg-accelerometer sensor fusion and efficient edge deployment. IEEE Transactions on Biomedical Circuits and Systems.
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Fields, C., et al. (2024). Search for entanglement between spatially separated Living systems: Experiment design, results, and lessons learned. Biophysica, 4(2), 168-181.
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Cannard, C., Delorme, A., & Wahbeh, H. (2024). Identifying HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements. bioRxiv, 2024-02.
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Arao, H., Suwazono, S., Kimura, A., Asano, H., & Suzuki, H. (2023). Measuring auditory event‐related potentials at the external ear canal: A demonstrative study using a new electrode and error‐feedback paradigm. European Journal of Neuroscience, 58(11), 4310-4327.
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Goodwin, A. J., et al. (2023). The truth Hertz—synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit. Physiological Measurement, 44(8), 085002.
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