Training Brain–Heart Coherence with EEG and HRV
When The Brain and Heart Learn Together with Biofeedback
In the last few years, the science of mind-body connection has evolved beyond just a slogan. We now have measurable, quantifiable ways to observe how the brain and heart coordinate their rhythms to create what we call adaptive resilience – the ability to stay calm under stress, shift focus fluidly, and recover efficiently.
Recent discoveries in EEG brainwave analysis and HRV heart-rate variability during a sub-anaesthetic Ketamine Assisted Psychotherapy (KAP) trial we lead in collaboration with Dr. Mario Scheib revealed something remarkable: both systems, the brain and heart, express flexibility through a shared language of complexity and variability. And now, with the help of biofeedback and neurofeedback tools, we can not only identify this, but train for it as well.
The New Science of Nervous System Flexibility
In neurophysiology, flexibility doesn’t mean chaos – it means adaptive resilience. Healthy systems can explore multiple patterns and reorganize quickly when the environmental demands change.
When the brain becomes rigid (as in depression, PTSD, burnout), its electrical activity shows reduced diversity – a kind of “stuck” rhythm. Similarly, the autonomic nervous system (ANS) reflected in HRV, loses its fluid oscillation between activation and rest.
Measuring Flexibility in the Central Nervous System (CNS)
Within the Central Nervous System (CNS), adaptability is measured through Lempel–Ziv Complexity (LZC), a metric that quantifies how unpredictable and information-rich brain signals are. Higher LZC reflects greater neural flexibility and a brain that can reorganize quickly in response to changing demands (Mediano, 2023).
When EEG complexity drops, the brain becomes more rigid, a pattern often seen in burnout, depression, and PTSD. In contrast, higher LZC correlates with creativity, mindfulness, and resilience. By tracking and training this variability, clinicians can help clients strengthen the brain’s capacity to adapt rather than react (Orlowski, 2023).
Tracking Flexibility in the Autonomic Nervous System (ANS)
The Autonomic Nervous System (ANS) shows flexibility through Heart Rate Variability (HRV), the subtle changes between heartbeats that reveal how smoothly the body shifts between activation and recovery.
Each HRV frequency band reflects a unique regulatory process:
- HF (0.15–0.4 Hz): Parasympathetic calm (Riganello, 2025)
- LF (0.04–0.15 Hz): Baroreflex coordination (Dantas, 2012)
- VLF (<0.04 Hz): Hormonal and thermoregulatory balance (McCraty, 2025)
Optimal flexibility isn’t about holding a steady rhythm but transitioning fluidly between states. When HRV variability and EEG complexity rise together, the nervous system reaches its most adaptive, coherent form: calm, focused, and responsive.
The Central Autonomic Network: The Hidden Bridge
At the core of this communication is the Central Autonomic Network (CAN) a neural bridge connecting the prefrontal cortex, anterior cingulate, insula, amygdala, and brainstem nuclei (Lamotte, 2021).
The CAN constantly exchanges information between cortex and body. It regulates heart rhythms, breath, and emotional tone while receiving input from internal states (interoception) (Garfinkel, 2016).
When this network functions well, the brain and heart oscillate together like two musicians playing in sync. When it falters, the individual experiences stress, dissociation, or loss of self-regulation (Deschodt-Arsac, 2020).
Relation between Lempel–Ziv Complexity and Parasympathetic Calming
To test, we ran an observational study with a few healthy volunteers undergoing KAP procedure in Frankfurt, Germany in October, 2025 in an attempt to observe this coregulation between brain and heart. We discovered that there seems to be some early evidence that suggests a correlation between the neurological complexity measure and the autonomic nervous system measure represented in heart rate variability. The following is the snapshot of measurement data from one of the subjects whose baseline (prior to ketamine infusion) and active (during ketamine infusion) were measured with EEG and ECG data feeds.
The subject volunteered to participate and gave permission for their data to be used by us. The individual was female between the ages of 40 and 45. The first two charts show her beeline measurements during a 5 minute eyes closed period.
LZC:
HRV Frequencies:
During:
Overall, these metrics from the ANS and CNS both suggest that under Ketamine Assisted Psychotherapy (KAP), the subject’s neurological and cardiovascular systems both demonstrated increased flexibility and adaptability.
The above change from baseline to increased flexibility has also been observed in mindful practices such as meditation with respiratory sinusoidal arrhythmia (RSA) breathing.
While the other volunteers showed similar patterns, we chose this particular individual since they had the highest signal quality during measurement and the effects were the most pronounced. More studies on a wider variety of individuals under more controlled conditions are needed to verify these promising results.
Measuring Brain-Heart Flexibility: A Two-Way Mirror
Recent research shows that increases in EEG complexity (LZC or entropy) correspond to moments of heightened neural flexibility – during creativity, mindfulness, and even in the acute phase of ketamine-assisted psychotherapy (KAP).
At the same time, HRV entropy (a measure of how diverse heart rhythms are) rises during emotional recovery, meditation, or adaptive stress response. Ketamine decreases the overall power spectrum for HRV. However, it increases the LF component / total Power ratio. This relates to sympathetic activation in the ANS from low relative sympathetic to high relative sympathetic state, fostering a shift from “at risk” to “resilient” (Weber et al., 2025).
In other words, what we understand from current research on ketamine assisted psychotherapy is that the heart’s rhythm and the brain’s complexity both describe the same underlying capacity: our physiological readiness to adapt.
Training Nervous System Resilience: A New Generation of Biofeedback
This observation from the ketamine trial provides insight on how to improve techniques used in neurofeedback as well. Traditional biofeedback methods often teach calm and focus with amplitude based training on specific parts of our brains. While this can be effective, it does not capture the nuance of physiological readiness to adapt.
The next frontier is training the dynamic relationship between the two states – teaching the nervous system how to move gracefully between calm and activation, between order and exploration.
Here’s how we are starting to incorporate these new modes into our biofeedback training:
- EEG Complexity Training – Modern neurofeedback now leverages EEG complexity metrics like Lempel–Ziv Complexity (LZC) to capture how dynamically the brain organizes information. By providing real-time feedback on neural entropy, clinicians can guide clients toward states that increase signal diversity without losing coherence. Over time, users learn to access brain states that balance stability and flexibility, strengthening resilience, cognitive agility, and emotional regulation.
- HRV Flexibility Training – Traditional HRV biofeedback focuses on achieving a steady, coherent rhythm, but true adaptability lies in modulation, not constancy. With HRV flexibility training, clients learn to shift smoothly between sympathetic activation and parasympathetic recovery through guided breathwork and feedback. This approach builds autonomic agility, teaching the body how to recover faster from stress while maintaining physiological balance and readiness.
- Coupled Brain-Heart Feedback – The most advanced model integrates both EEG and HRV signals into a unified Flexibility Index. This coupled brain-heart feedback rewards moments when neural complexity and cardiac variability rise together, representing synchronized adaptability across systems. By engaging cognition, emotion, and physiology in one continuous feedback loop, clinicians can train the entire self-regulation network rather than isolated subsystems.
Why Adaptive Resilience Matters for Therapy and Peak Performance
In clinical settings, such as Ketamine-Assisted Psychotherapy (KAP), EEG and HRV metrics provide real-time insight into how the nervous system reorganizes during profound shifts in consciousness and emotional processing.
We can now identify “windows of neural flexibility” – periods where the brain shows increased complexity and the body shows coherent flexibility – signaling an ideal moment for therapeutic integration.
In performance or wellness training, this same model applies: flexibility, not control, predicts resilience. The ability to shift quickly between focus and rest, action and reflection, engagement and recovery, is the true hallmark of a healthy nervous system.
Looking Ahead: Toward Precision Bioadaptive Training
The emerging picture is clear:
- EEG, LZC and HRV entropy are two sides of the same coin; one cortical, one autonomic.
- The Central Autonomic Network orchestrates this dance.
- With wearable EEG and HRV sensors, we can now train this synchrony directly.
In the near future, your biofeedback device won’t just show “calm” or “stress.” It will measure how flexibly you can transition and how dynamically your brain and heart adapt together.
This isn’t just regulation. It’s adaptive intelligence, built into the body itself.
Science, technology, and human potential are converging.
We’re learning that mental health, resilience, and performance all emerge from one principle: The resilience of the human nervous system lies in its ability to adapt.
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References
Mediano, P. A. M., Rosas, F. E., Luppi, A. I., Faskowitz, J., Shanahan, M., & Bor, D. (2023). Spectrally and temporally resolved estimation of neural signal diversity. eLife (Reviewed Preprint 88683v1). https://elifesciences.org/reviewed-preprints/88683v1
Orłowski, P., & Bola, M. (2023). Sensory modality defines the relation between EEG Lempel–Ziv diversity and meaningfulness of a stimulus. Scientific Reports, 13, 3453. https://doi.org/10.1038/s41598-023-30639-3
Scientific Reports. (2025). Complexity and 1/f slope jointly reflect brain states. Scientific Reports. https://www.nature.com/articles/s41598-025-18615-5
Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. Computer Methods and Programs in Biomedicine, 78(3), 321–336. https://www.sciencedirect.com/science/article/pii/S001048251100223X
McCraty, R., Shaffer, F., & HeartMath Institute Research Team. (2015). Chapter 3: Heart rate variability. In Science of the Heart (Vol. 2). HeartMath Institute. https://www.heartmath.org/research/science-of-the-heart/heart-rate-variability
Lanza, G., Lanuzza, B., Aricò, D., Cantone, M., Cosentino, F. I. I., Bella, R., & Ferri, R. (2021). 24-Hour heart rate is a trait but not state marker. Frontiers in Neuroscience, 15, 738646. https://pubmed.ncbi.nlm.nih.gov/34461325
Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 258. https://pmc.ncbi.nlm.nih.gov/articles/PMC5062102/[7]
Zorick, T., & Mandelkern, M. A. (2020). Multiscale entropy in physiology and medicine. Entropy, 22(3), 317. https://www.mdpi.com/1099-4300/22/3/317
Elsevier (Journal of Affective Disorders). (2025). Effects of electrocardiographic noise on ultra‑short‑term heart rate variability indices. Journal of Affective Disorders. https://www.sciencedirect.com/science/article/pii/S016503272500919X
Nature Mental Health. (2025). Electrocardiography‑derived autonomic profiles in depression and anxiety. Nature Mental Health. https://www.nature.com/articles/s44184-025-00130-0
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