EDA Archives - Page 2 of 2

Holiday Happiness: Gratitude or Gifts?

Studies investigated the anxiety of gift-giving, the effects of social support in stressful situations, and what impact meditation has on heart rate, ECG, and blood pressure.

December has arrived, and for many the holidays bring a rush to check items off of a long shopping list for parents, siblings, friends, in-laws, and many more. Often we struggle to find that “perfect” gift, but are the physical goods we exchange the true source of the warmth and happiness that characterizes the season?

An article by Nomura investigated anxiety induced by gift-giving. To test this, EDA data was recorded with a BIOPAC data acquisition system and STP100 while participants responded to stimuli. The participants visualized a specific relative while being presented with potential gift options, and were asked how likely they were to purchase the potential gift for that relative. The data found that people who self-reported thinking of in-laws were the most stressed, while those who reported thinking of children or same-gender friends were least stressed.

A psychosomatic study by Inagaki and Eisenberger found that mental and physical well-being benefit from forms of social support. They tested this by administering mild shocks with the BIOPAC STMISOLA to male patients who held the hand of a female partner lying in an fMRI. The results suggest that this support (hand-holding) provided possible stress-reduction for both the male and female participants.

Another article on nonverbal communication and well being by Shaltout, et al. tested the effects of meditation and the subsequent ECG, blood pressure, and heart rate effects. The BIOPAC ambulatory BioHarness and NIBP Blood Pressure System gathered data while participants underwent several mediation episodes, during which they were instructed to focus on loving kindness towards a subject. The results found that participants had decreased heart rate.

Research suggests direct, loving interactions with those who are close to us have many stress-reducing benefits. So when you are shopping for gifts, remember that spending time is more important than spending money. To read more studies about love and stress reduction, visit the BIOPAC publication page.

BIOPAC offers a variety of equipment that can be used in studies similar to the ones presented. To find more information on solutions for recording and analyzing signals such as EEG, EMG, respiration, and EDA using the MP160/MP150, MP36, and other platforms, you can visit the individual application pages on the BIOPAC website.

BIOPAC Systems, Inc. provides life science researchers and educators with data acquisition and analysis systems that inspire people and enable greater discovery about life. Visit us at www.biopac.com.

The Physiology of Consumer Buying Habits

On “Black Friday” in the US, shoppers will come out in search of great deals, and researchers can measure the many physiological factors that motivate shoppers other than deals and price cuts.

Black Friday is one of the most exciting days for consumers in the United States. With the holidays right around the corner, significant deals and price cuts incentivize people to do bulk gift shopping. But is price the only factor influencing shoppers to dedicate hours waiting in line for a product?

In a neurophysiology study by Alexander et al., researchers found that participants who received a coupon while shopping online had a significant increase in oxytocin and a decrease in cortisol. A BIOPAC research system was used to gather ECG, respiration, and skin conductance data while the participants browsed a commerce website.

Another article by Ha-Brookshire and Bhaduri measured ECG response to stimuli that tested consumers’ perceived trust of businesses. The BIOPAC MP36R was used to record participants’ heart rates while they read negative reviews of an apparel business. The results found lower heart rate and less trust were correlated with the less favorable comments.

Conversely, a consumer research study by Langner et al. found that consumers’ love of brands is similar to their love of friends. Signals measured included EDA, EMG, ECG, and BPV, and were recorded by BIOPAC MP160/MP150 hardware to measure arousal levels while participants viewed different brand and interpersonal stimuli.

BIOPAC offers a variety of equipment that can be used in studies similar to the ones presented. To find more information on solutions for recording and analyzing signals such as EEG, EMG, respiration, skin conductance using the MP160/MP150, MP36, and other platforms, you can visit the individual application pages on the BIOPAC website. If you are interested in reading other related studies, visit the publications page on the BIOPAC website.

The Physiology of Election Decision-Making

The psychophysiological factors of stress, the behaviors of others, and of introception can play major roles in decision-making, and BIOPAC systems provide measurements of how physiological factors such as these influence decisions.

People experienced varying stress levels because of the 2016 US presidential election. There are a variety of factors that can impact voting behaviors and decision-making. Several recent studies using BIOPAC systems investigated the psychophysiological effects of stress, the behaviors of others, and introception on decision-making.

Stress has been shown to be very influential in decision-making. In their study, Yamakawa, Ohira, and Mastunga Isowa (2016) investigated effects of acute stress and risk taking in decision-making. Participants were asked to perform a decision-making choice where they either kept or bet the money they were given. Cardiodynamic activity was measured with ECG using a BIOPAC MP system. Results suggest that with an increase in cortisol, adrenaline, heart rate, and subjective stress, participants were likely to make fewer risky choices. So, high levels of stress may be key in helping make better decisions when selecting a future president.

Perhaps it’s not stress that will influence the choice of the next POTUS (President of the United States), but rather the behaviors of others. Work by Bault, Joffily, Rustichini, and Coricelli (2011) sought to compare private and social decision-making to discover the effect of social comparison on risky choices. The objective of the study was to understand more about how individuals evaluated the outcomes of their decisions in private as opposed to in a social context. Combining data from functional MRI (FMRI) and skin conductance recordings, subjects’ reactions were measured using a BIOPAC MP150 System. Researchers found that participants took significantly longer to make a decision in a social setting than in a private one.

In the end, many feel that it is important to listen to your heart when deciding who you find qualified to be the next president. A study by Dunn et al. (2010) examined whether introception moderates the coupling of bodily responses to emotional experience and intuitive decision-making. Bodily response and heart rate were recorded using a BIOPAC Research System and electrodes to measure introception through changes in heart rate. Researchers found that introception was not related to arousal, valence ratings, or heart rate response to images.

For more information on BIOPAC’s wide range of tools for recording, displaying, and analyzing physiological measurements from human and animal subjects including respiration, heart rate variability, ECG and EDA, visit BIOPAC’s psychophysiology page and view BIOPAC’s full line of electrodes, amplifiers, and wearable, wireless transmitters and loggers.

Start the First Class with EDA and Emotional Responses!

By Howard Iver Thorsheim, Ph.D., St. Olaf College. I wanted to start the first day of my class with an activity that would engage students’ curiosity and enthusiasm, to set the stage for introducing the course and going over the syllabus!

By Howard Iver Thorsheim, Ph.D., St. Olaf College

Howard Iver Thorsheim is Professor of Psychology and Neuroscience at St. Olaf College, Northfield, Minnesota, USA. In these blogs he offers his first-person insights into effective teaching techniques gleaned from a combination of scholarly research and over 40 years in the classroom.

I find Electrodermal Activity (EDA) to be a great way to demonstrate psychophysiology measurement at the beginning of a course: students easily understand it. It takes little time to set up and record because it only involves two sensors on the finger tip pads. It works every time as a “window” to emotions with clear responses to a variety of stimuli (e.g., visual, auditory, gustatory, olfactory).

I show students a picture of a rock climber climbing a cliff, then ask them to jot down some notes of what they might feel when climbing like the woman pictured, and then buzz with a person sitting next to them about what they wrote. I ask for a show of hands of who has mentioned the bag hanging from the climber’s belt. Depending on the amount of time I choose to devote to the activity, I lead the entire class through a number of probing questions; for example:

“Who knows what the bag is for?” (Chalk for fingers.)

“Why does the climber use chalk?” (Reduce slipperiness from sweat.)

“Why do fingers get slippery?” (Emotion.)

“What other people encounter emotion daily?”

I then set the stage for EDA: changes in skin conductivity are due to the amount of sweat in sweat glands of the skin, which are produced by the sympathetic branch of the autonomic nervous system responding to emotion.

I explain to the class that I will be the first volunteer, and guarantee that no shock is involved using BIOPAC equipment. I connect electrodes on the volar pads of my index and middle fingers, and take a deep breath, which will generate EDA. Then I tell the class I will think of emotion-producing situations, and ask them if they can see any EDA detected by the equipment. There will be a few good chuckles because in anticipation of doing so, my response will already appear. Two of several easy classroom measurements of EDA include latency (about 1 to 3 seconds between a stimulus and beginning of the electrodermal response), and peak amplitude (the maximum size of the response). My eBook Doing Psychophysiology–Getting Started on the Apple iBooks Store has a graphic narrative and a one-minute video explanations about EDA, as well as other psychophysiology measures you can show your students. The Biopac Student Lab system also includes two lessons in which students can engage in interactive learning activities while measuring EDA; a lesson on the principles of biofeedback and one on the standard physiological measures of a polygraph.

An excellent summary of Electrodermal Activity is provided in the following chapter:

Dawson, M. E., Schell, A. M. & Fillion, D. L. (2007). The electrodermal system. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.). Handbook of psychophysiology (3^rd ed. pp. 159-181). New York: Cambridge University Press.

Learn more about BIOPAC’s education solutions, including complete teaching systems for psychophysiology and the life sciences. BIOPAC also provides a wide range of research systems for recording, displaying, classifying, and analyzing psychophysiological measurements.

Professor Thorsheim is a licensed Psychologist, educator, and author. He has published or presented over 125 papers, journal articles, books, book chapters or monographs, and received a patent in 1985 as a co-inventor for an educational book design with interactive elements. His work has been funded by marquee foundations across the globe. His most recent work is Doing Psychophysiology: Getting Started, available from Knowledge Growers, Inc., available as an iBook download in 51 countries.

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.

Electrodermal Activity (EDA)

Typically, EDA is measured as skin conductance and a small direct constant voltage (0.5 vdc) is used as excitation source across the skin electrodes.

By Alan Macy, BIOPAC Systems, Inc.

Electrodermal activity measurement is subject to electrically-related characteristics of the skin. There are two phenomena associated with this type of skin activity. One is skin potential, which is a self-generated potential intrinsic to the skin. This potential is a consequence associated with dissimilar skin layers placed in proximity coupled with the presence and movement of interstitial fluids. An evoked skin potential response is called the sympathetic skin response. This change in skin potential can be evoked by electrical, auditory or magnetic stimulation. The other phenomenon associated with skin electrical activity can be measured when a stimulation (excitation) current is injected through, or a voltage is applied across, attached skin electrodes. The current or voltage stimulation is typically at constant (DC) level, or at low frequency, because the skin impedance is high. The application of excitation current or voltage produces a corresponding voltage or current, respectively, that changes over time subject to physiological state. In these cases, the skin’s resistance, impedance, conductance or admittance is being measured, corresponding to the type of electrical stimulation used.

EDA Measurements – subject to electrical excitation type:

Skin Resistance – Direct Constant Current (not commonly used)

Skin Conductance – Direct Constant Voltage (most commonly used)

Skin Impedance – Alternating Constant Current (not commonly used)

Skin Admittance – Alternating Constant Voltage (sometimes used)

Typically, EDA is measured as skin conductance and a small direct constant voltage (0.5 vdc) is used as excitation source across the skin electrodes. The skin admittance method (alternating constant voltage excitation) may provide some advantages in measurement in that this method does not polarize the skin electrodes during EDA recording. Skin conductance is usually measured in units of “Siemens” although sometimes “mho” is used. The unit “mho” comes from the reverse spelling of “ohm”, the unit of resistance. One mho equals one Siemen. The conductivity of the skin is small, so values are given in microsiemens (uS) or micromhos (umho).

Changes in the electrically-related characteristics of the skin is correlated to eccrine sweat gland activity. Eccrine glands are in nearly all skin locations and are found, in highest concentrations, on the palms of hands, fingertips and soles of the feet. EDA, measured as skin conductance, is a physiological signal that indicates increased sympathetic nervous system (SNS) activity. This aspect of 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.

There are two aspects to skin conductance measurements, namely phasic and tonic. Tonic value is the average skin conductance level (SCL), that is relatively consistent over time, and is related to arousal. The phasic component of skin conductance is the change of the skin conductance, over a short time period, in reaction to a stimulus. For a skin conductance response (SCR), the skin conductance rises in a short time period and then returns to tonic levels. SCRs are characterized by four different parameters: the response peak amplitude, latency of response, rise time to the response peak, and recovery time after peak.

For more information on BIOPAC’s EDA research solutions, including real-time live raw EDA data as well as real-time filtering or calculation channels including High-pass filtered/AC Coupled EDA, visit BIOPAC’s EDA Analysis page or view BIOPAC’s full line of electrodes, amplifiers, and wearable, wireless transmitters and loggers.

Additionally, a link to a recently updated EDA SCR Analysis Guide for Psychological Experiments came be found here.