HRV Archives - BIOPAC Blog

Mind-Body Connection: Biofeedback in Research

The connection between the mind and body has long intrigued scientists, driving research into how our thoughts, emotions, and mental states can influence physical health and well-being. Biofeedback has become a recognized approach for helping people manage stress, reduce pain, and even enhance cognitive and physical performance. Biofeedback involves using sensors and real-time data to monitor physiological functions that are typically automatic, such as heart rate, breathing, muscle tension, and skin conductance. With this information, individuals can learn to gain greater awareness and, in some cases, voluntarily control these bodily processes.

Common physiological signals used in biofeedback include electrodermal activity (EDA), which reflects sweat gland activity and emotional arousal; electroencephalogram (EEG), which measures electrical activity in the brain; electrocardiogram (ECG), which measures heart electrical activity; heart rate variability (HRV), an index of autonomic nervous system flexibility; and respiration rate, among others. These signals offer researchers a window into the body’s stress responses, regulatory mechanisms, and even aspects of cognition and emotional control.

To complement physiological monitoring, researchers are increasingly integrating tools like stimulus presentation systems, virtual reality (VR) environments, and eye tracking technology into biofeedback studies. These tools allow scientists to study how people react to controlled stimuli, immerse participants in realistic scenarios, and track where and how long someone focuses visually, all while collecting detailed data on their physiological responses. This combination of immersive experiences and real-time monitoring helps researchers investigate how the body responds to stress or cognitive challenges and whether individuals can be trained to influence those responses.

Swiss researchers at ETH Zurich investigated whether individuals could voluntarily control their arousal states using real-time feedback on their pupil size. Pupil size is a physiological marker closely linked to the brain’s arousal systems, particularly the locus coeruleus. Study participants employed mental strategies to adjust their pupil diameter, receiving visual feedback through a dynamically changing circle that reflected their pupil size. The participants’ cardiovascular data, including ECG and respiration, were recorded to provide insights into the physiological effects of pupil self-regulation. The research team employed a BIOPAC data acquisition and analysis system running AcqKnowledge software to record ECG data continuously at a sampling rate of 1,000 Hz. Additionally, a BIOPAC respiratory effort transducer was used to measure respiration, although these respiratory data were not reported in the publication. The study’s findings demonstrated that pupil-based biofeedback enabled participants to modulate not only their pupil size but also associated neural activity and cardiovascular responses, such as heart rate. The results suggest a non-invasive method for individuals to control their autonomic functions, with potential applications in managing stress and anxiety disorders.

Vagally mediated heart rate variability (vmHRV) refers to the natural changes in the time between heartbeats that are controlled by the vagus nerve, which is closely tied to how well a person can adapt to stress and regulate their body. Researchers from Université Paris Cité explored whether a brief session of vmHRV biofeedback could help people better control their memory, specifically whether it could help them tell the difference between real and false memories. In the study, 71 healthy young adults completed a VR memory task after receiving either vmHRV biofeedback or a simple breathing exercise. To track their physiological responses, the team used a BIOPAC data acquisition and analysis system and recorded heart activity via a three-lead ECG configured according to Einthoven’s triangle using Lead II. The results showed that this short biofeedback session had no immediate effect on memory accuracy or on vmHRV levels during the task. This suggests that a single session of HRV biofeedback may not be enough to influence memory-related brain functions, underlining the complex relationship between the body’s stress responses and cognitive control.

A study published in the Journal of Human Kinetics looked at whether just one 10-minute session of HRV biofeedback, using a special breathing technique called resonance frequency breathing, could affect the level of activity of certain nerve cells. Researchers measured this by looking at a reflex in the lower leg muscle (the soleus) called the Hoffman reflex (H-reflex). Twelve healthy men took part in both the HRV biofeedback session and a regular breathing session in a random order. To measure muscle activity, researchers used a BIOPAC surface electrode on the leg and a reference electrode on the hand. The signals were recorded with a BIOPAC data acquisition system and analyzed with AcqKnowledge software, which helped line up the timing of muscle activity with electrical stimulation. The results showed that HRV biofeedback lowered the strength of the reflex and a related ratio (Hmax/Mmax), suggesting the spinal nerve cells were less excitable. These findings point to the potential of short HRV biofeedback sessions to affect the nervous system in ways that could be useful for physical therapy or improving movement control.

To learn more about how to integrate biofeedback techniques into your next research project, check out BIOPAC’s webinar, Biofeedback: Theory, Techniques, and Tools for Research.

Are you planning a research project that will utilize biofeedback data? Our expert customer service representatives can help you find the right tools for all your biofeedback needs.

Balancing Act: HRV Provides Critical Marker for Psychophysiological Health

Two college wrestlers square off on the mat awaiting the signal that will begin their match. To the casual observer, the outcome seems impossible to predict. Their coach, however, knows one competitor has a clear advantage—a heart rate variability level (HRV) that is higher than his opponent’s. Examining HRV data provides more than insight into athletic performance, it offers a window into a broad spectrum of functions that influence psychophysiological health.

In simple terms, HRV is the natural variation in the intervals between each heartbeat over time. Unlike a ticking clock, our hearts do not beat at a constant rate. As such, variations in heart rate can provide a wide range of valuable information on our bodily systems. This is because HRV represents a balancing act between our sympathetic and parasympathetic nervous systems, which together make up the autonomic nervous system. As we have discussed in previous posts, the autonomic nervous system controls nearly all the body’s involuntary functions.

The sympathetic nervous system is activated in times of stress or danger (i.e., the fight-or-flight response), during which our bodies sweat, our heart rates increase, and the interval between beats decreases. Conversely, the parasympathetic nervous system acts as a sort of switch, shutting down the sympathetic nervous system and activating a relaxed state (i.e., rest and digest). To do this, it releases acetylcholine, a neurotransmitter that makes the heart beat slower and at greater intervals.

When these two systems are in balance, they produce higher HRV through a more variable heart rate. Greater variability has been associated with the body’s ability to switch between these systems. Thus, HRV is used as a health marker, such that high HRV is associated with better cardiovascular health, mental focus, emotional health, stress management, and ability to recover from exercise, while low HRV is associated with inflammation, chronic fatigue, increased stress, depression, and chronic pain.

HRV has also been connected to willpower and is a predictor of persistence and self-control. HRV increases during periods of self-regulation sometimes called the “pause and plan response.” As such, HRV has been particularly useful in research into the mechanism associated with addiction. A 2019 study used HRV to explore how chronic pain patients treated with opioids could regulate drug cravings and depression through dispositional mindfulness. Electrocardiogram (ECG) data were sampled with a BIOPAC data acquisition unit while HRV was calculated with AcqKnowledge software.

Because HRV plays such a critical role in physical and psychological health, several studies have investigated how to self-regulate HRV, including examining how it is affected by posture. A 2020 study used a 2-channel BSL data acquisition unit to gather ECG data to calculate HRV for a variety of bodily positions to determine their impact.

A similar study examined the link between HRV and resilience, i.e., the individual’s ability to return to stable physical and psychological equilibrium after experiencing stress. HRV calculations were made from data gathered via a BIOPAC ECG100C ECG amplifier, RSP100C respiratory amplifier, and EDA/GSR amplifier. The study identified possible trends connecting resilience factors and HRV levels during specific stress-inducing simulations.

Calculating HRV with BIOPAC hardware and software solutions provides researchers with a myriad of psychophysiological insights for their lab work. Learn more about HRV from our Knowledge Base entry, as well as application notes on data preparation for analysis and statistical and geometric measures.

For additional information on how BIOPAC can assist you with a wide range of research and educational applications, contact your local sales representative.

Circumplex Model of Affect and Motivational State

By Alan Macy, BIOPAC Systems, Inc.

The Circumplex Model of Affect was first described in 1980 by James Russell. Affective states arise from the behavior of two independent neurophysiological systems, the arousal and valence systems. Affective states are a function of these two systems. The circumplex model is two-dimensional, with arousal and valence defined as orthogonal (perpendicular) axes. The arousal axis, plotted vertically, ranges from zero arousal to high arousal.The valence axis, plotted horizontally, ranges from negative to positive. Objective physiological indexes of affect or emotion are available. As valence examples, positive affect is indicated by increasing zygomaticus activity and negative affect is indicated by increasing corrugator activity. Physiological example indices for arousal include heart rate and electrodermal activity.

Perhaps even more fundamental to emotional state is the concept of motivational state. Motivational state is indexed by specific, bodily expressed, physiological states that can easily be measured. Motivational state is based on the concept of core relational themes, called “challenge” and “threat”. During the course of living, humans relate to difficult environmental circumstances as a combination of challenge and threat. A challenge response is similar to the aerobic physiological response and involves increased stroke volume and cardiac output, unchanged or increased heart rate, decreased vascular resistance and relatively unchanged blood pressure. This response is indicative of efficient mobilization of available energy for coping with circumstances. A threat response is characterized by increased heart rate and blood pressure, increased or little changed vascular resistance, decreased or unchanged stroke volume and relatively unchanged cardiac output. A challenge response occurs when the subject experiences sufficient resources to meet circumstantial demands in a performance situation where goals are important. A threat response occurs when the subject experiences insufficient resources to meet those same circumstantial demands. If a circumstance is determined to be challenging, then subject performance is usually adequate. If a circumstance is determined to be threatening, then subject performance tends to worsen. Motivational state information can be used to better reflect objective differences between similar circumplex model defined emotions, such as anger and fear.

Electrodermal activity (EDA) is a physiological signal that indicates increased SNS activity. EDA is representative of changes in the electrical conductance of the skin due to eccrine (sweat) gland activity. SNS activity increases sweat gland secretions. Eccrine glands only receive activation signals from the SNS, so increased EDA is an indicator of increased arousal.

Skin temperature (SKT) changes are primarily driven by variations in blood flow. These local variations are mainly caused by changes in vascular resistance or arterial blood pressure. Local vascular resistance is modulated by smooth muscle activity, which is mediated by the SNS. SKT variation reflects SNS activity, and is an indicator of emotional state. In particular, fingertip temperature is a marker for sympathetic induced changes in microcirculation.

Blood Volume Pulse (BVP), also referred to as the Pulse Plethysmogram (PPG), is reflective of variations in microcirculation. As vasculature resistance to flow increases, subject to increased SNS, microcirculation decreases. Accordingly a reduction in PPG (BVP) amplitude reflects increased SNS activity.

The heart and brain are connected bidirectionally via the vagus nerve. Vagal PsNS stimulation, from the brain, influences the heart via the sino-atrial (SA) node. Baroreceptor signals, from the heart, travel back along the vagus nerve to affect the brain. The SA node is the pacemaker of the heart. The SA node receives inputs from both the SNS and PsNS. The SA node can be considered to be a spike-train generator whose inter-spike (firing) interval is modulated by both SNS and PsNS activity levels. Because both SNS and PsNS activity influence SA node spike firing, heart rate behavior can be considered to be dependent on a range of emotional states. SNS activity increases heart rate and PsNS activity decreases heart rate.

Heart rate variability (HRV) is a measure of heart rate changes over time. HRV has a frequency range of 0.003 to 0.4 Hz and is considered to have four sub-bands: HF, LF, VLF and ULF. High frequency HRV (HF-HRV) band power is in the range of 0.18 to 0.4 Hz and reflects primarily PsNS influences. HF-HRV band power reflects modulation of vagus nerve activity, as driven by respiration. This modulation is called respiratory sinus arrhythmia (RSA). Low frequency HRV (LF-HRV) band power is in the range of 0.05-0.15 Hz and appears to reflect both SNS and PsNS influences. Very low frequency HRV (VLF-HRV) band power is in the range of 0.003-0.05 Hz and may reflect cardiovascular disease, thermo-regulatory cycles and blood plasma renin activity. Ultra low frequency HRV (ULF-HRV) band power is in the range of DC-0.003 Hz and may reflect circadian rhythm activity.

PsNS influences occur over the LF and HF frequency ranges of HRV. SNS influences drop off at about 0.15 Hz and higher. PsNS influences can affect heart rate in a fraction of a second, but SNS influences can only affect the heart rate after a few seconds. Accordingly, PsNS influences are uniquely capable of producing high speed changes in the heart rate. PsNS innervation of the heart is controlled by the right vagus nerve via the SA node. PsNS induced changes in heart rate are associated with vagal nerve activity that is modulated by respiration. Expiration causes an increase in vagal nerve activity, so heart rate decreases. Inspiration causes suppression of vagal nerve activity, so heart rate climbs back up. This combined action is known as respiratory sinus arrhythmia (RSA). RSA is considered, in part, to be a marker for vagal control of heart rate and also for emotional regulation. Total HRV appears to be positively correlated with valence.

When attention increases, there is a short term deceleration of heart rate. Arousal is also correlated to a long term acceleration in heart rate. Heart rate also provides an indication of valence. Compared to neutral stimuli, both positive and negative stimuli first exhibit a short term decrease in heart rate. Over the long term, positive stimuli are correlated with an increase in heart rate while negative stimuli usually are correlated to a decrease in heart rate.

Respiratory activity occurs via periodic contraction and relaxation of respiratory muscles, including the diaphragm, intercostal and abdominal muscles. The motor outputs for controlling respiration are generated by efferent neurons in the spinal cord. There are autonomic and voluntary breathing pathways to these respiratory-related efferent neurons. A variety of afferent (sensory) inputs influence respiratory rate and tidal volume in support of the body’s metabolic demands. SNS activity increases respiratory rate and PsNS activity reduces respiratory rate.

Several EEG studies suggest that emotional valence is linked to frontal lobe activation, typically indexed by alpha power. Positive valence is correlated to increased activation of the left frontal lobe and negative valence is correlated to increased activation of the right frontal lobe. Other studies have suggested that frontal lobe EEG activation asymmetry reflects the relative balance of motivational state, more so than emotional valence. In this work, enhanced left frontal lobe activation predicted dispositional states towards challenge and enhanced right frontal lobe activation predicted dispositional states towards threat.

Artwork adapted from from Russell, J. A. (1980). A circumplex model of affect.Journal of Personality and Social Psychology, 39, 1161–1178.

For more information on BIOPAC’s wide range of tools for recording, displaying, classifying, and analyzing psychophysiological measurements—including EEG, ECG, EDA, PPG, and emotional states—visit BIOPAC’s applications pages and view BIOPAC’s full line of physiology data acquisition and analysis systems, electrodes, amplifiers, and wearable, wireless transmitters and loggers.

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