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Year : 2017  |  Volume : 5  |  Issue : 2  |  Page : 48-52

Quantification of electrodermal activity variation across human fingers: Toward a scientific basis of mudras

Biomedical Engineering Group, Department of Applied Mechanics, IIT Madras, Chennai, Tamil Nadu, India

Date of Web Publication15-Feb-2018

Correspondence Address:
Dr. Manivannan Muniyandi
MSB 207, Department of Applied Mechanics, IIT Madras, Chennai - 600 036, Tamil Nadu
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijny.ijoyppp_4_17

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Context: Fingertips when mechanically stimulated can elicit varied responses in the human body as in Mudras. Each fingertip is considered as a terminal of one of the ten meridian (or energy) channels. Can meridians be quantified? Aims: Electrodermal activity (EDA) of skin is an easy and inexpensive biosignal to acquire and is considered to be a good indicator of psychophysiological state of human health. Although EDA has been studied before, EDA across fingers has never been studied before, and it could be used to prove or disprove the theory of meridian terminals in the fingers. Settings and Design: This study was a randomized, event-based trials. Materials and Methods: A device to measure EDA of ten fingers simultaneously has been developed. Event-based experiments, involving external stimuli given to the participant due to which there may be an onset of skin conductance response (SCR), were conducted on seven voluntary participants. Continuous decomposition analysis is used to decompose data into continuous tonic and phasic activity. Statistical Analysis Used: Several time domain and frequency domain parameters have been extracted from the EDA and compared against different fingers. Results: The number of SCRs and the latency values of SCRs occurring are varying from finger to finger from 1.029 to 3.5 s. Values of SCR amplitudes and average phasic driver and maximum value of phasic activity are also varying which implies different levels of activity in each finger. Conclusions: It was observed that there is a marked difference across fingers in various metrics used to characterize EDA, and it is likely that fingertips indeed represent the terminal of meridian channels.

Keywords: Electrodermal activity, meridian, skin conductance level, skin conductance response, skin potential, skin resistance

How to cite this article:
Amba RR, Muniyandi M. Quantification of electrodermal activity variation across human fingers: Toward a scientific basis of mudras. Int J Yoga - Philosop Psychol Parapsychol 2017;5:48-52

How to cite this URL:
Amba RR, Muniyandi M. Quantification of electrodermal activity variation across human fingers: Toward a scientific basis of mudras. Int J Yoga - Philosop Psychol Parapsychol [serial online] 2017 [cited 2024 Feb 21];5:48-52. Available from: https://www.ijoyppp.org/text.asp?2017/5/2/48/225627

  Introduction Top

Sweat glands in the human body average around 2.6 million (1.6–4 million).[1],[2],[3],[4] Sweat glands are of two kinds: eccrine and apocrine. Eccrine gland secretions do not contain cytoplasm from granular cells while apocrine glands do. The average eccrine glands' density depends on the anatomic area. It is reported 108 glands/cm 2 on the forearm, 64 glands/cm 2 on the back, 181 glands/cm 2 on the forehead, and 600–700 glands/cm 2 on the palms of hands and feet.[1],[2],[3],[4]

Innervation of sweat glands comes from a dense net of nerve terminals, both cholinergic and adrenergic. In particular, the secretion of the apocrine glands is stimulated by circulating adrenaline, whereas innervation of secretory part of the eccrine sweat glands is solely stimulated through the sympathetic branch of the autonomic nervous system (ANS).[5],[6]

Main function of the phenomenon of sweating is to regulate the temperature of body. However, it is also a known fact that palmar sweating is independent of the temperature of the environment, under normal conditions and occurs due to psychological phenomena such as stress, fear, pleasure, agitation, and anxiety or physiological conditions such as inspiratory gasp, tactile stimulation, and pain.[2],[6]

All findings concerning the central innervations of sweat glands' activity point to several centers, located at different levels of the CNS, and partly independent of one another.[7] The sympathetic nervous system (SNS) activity is responsible for the regulation of secretory segment of sweat glands. Electrical properties of the skin change due to sweat produced due to the electrolytes present in sweat filling up the ducts. The extent of SNS activation can be gauged by the measurement of output of sweat glands. Electrodermal activity (EDA) is a good indicator of the sweat gland output.

Sweat gland output, particularly in palms, is heavily regulated by the SNS activity of an individual. Palmar sweating is influenced by psychological phenomena such as fear, agitation, and anxiety or due to physiological phenomena such as tactile stimulation and inspiratory gasps. Yoga propounds that each fingertip is a terminal of one meridian channel and each fingertip is mapped to a different function. Stimulating the fingertips in the form of mudras has different physiological and psychological effects because each finger on stimulation activates a different neural pathway and thereby activates a different center in the brain.

To verify this hypothesis, we built a ten-channel skin conductance measurement sensor. Various metrics were used to characterize the skin conductance response (SCR) obtained from the ten fingers and compared against each other to test whether there is a difference in the response obtained from different fingers under different stimulus conditions.

  Materials and Methods Top

A device to measure EDA of ten fingers simultaneously has been developed. Skin conductance is of two types: tonic and phasic. Tonic conductance is the baseline level of skin conductance, and phasic skin conductance is the instantaneous variation observed due to environmental stimuli.[7] While the participant is hooked to the sensor, various event-based stimuli such as deep breathing, meditating, curling toes or crunching stomach muscle, startling, and working out a math sum are applied to the participant. These stimuli are expected to elicit a variation in the SCR. Data were collected from seven participant s, with an average age of 22, under the above-mentioned seven stimuli conditions. Tonic and phasic skin conductance is analyzed in a postprocessing step using MATLAB. Continuous decomposition analysis (CDA) is used to decompose skin conductance data into continuous tonic and phasic activity, which is based on standard deconvolution.

Statistical analysis used

Several time domain parameters that characterize the SCR such as the latency time between stimulus and observed response, number of responses observed within the expected window after impulse, and amplitudes of the responses observed have been extracted from the EDA signal. Characteristics of each finger are compared against other fingers.

Settings and design

We developed a sensor board in our laboratory to acquire the skin conductance signal. Arduino Mega 2560 was used as the communication channel, to send the data obtained from the sensor board to a personal computer for signal processing.

Criteria to be considered while designing the modules of the circuit for measuring skin conductance are listed below:[3],[8]

  1. Linearity of skin conductance: Skin conductance is linear when the voltage supplied to skin between two points is <0.5 volts
  2. Resolution of skin conductance: Skin conductance resolution needs to be high. The more linear the relationship between the output voltage and skin conductance, the better the resolution
  3. Range of skin conductance: Skin conductance range is typically between 4 μS and 250 μS. Circuit needs to be designed so that this range of values can be recorded
  4. Frequency range of skin conductance: Phasic responses of skin conductance which we are interested in have a frequency range of <0.5 Hz. Hence, a low-pass filter of cutoff frequency 0.5 Hz or more would be needed to remove unwanted frequencies.

Sensor module design

A standard EDA circuit has been designed and developed. This circuit consists of the following four modules:

  1. Voltage divider: It converts the 5 V supply to 0.5 V supply to Wheatstone bridge. SCR is nonlinear at voltages above 0.5 V. The voltage divider is fashioned out of a 10 K ohm in series with forward biased diode. A voltage follower is connected between the voltage divider and the Wheatstone bridge for the below reasons:

    1. To isolate the Wheatstone bridges of ten channels from the output of voltage divider
    2. To avoid overloading the voltage divider, voltage follower supplies higher current required for ten channels drawing current from the same source

  2. Wheatstone bridge: The purpose of Wheatstone bridge module is twofold:

    1. To maintain a linear relationship between the output voltage of instrumentation amplifier (V0) and skin conductance throughout its range of possible values so as to achieve higher resolution of the SCR
    2. >
    3. To ensure the output of instrumentation amplifier is positive for compatibility with Arduino's analog inputs.

    The values of R1, R2, and R3 are picked so as to satisfy the above two conditions. Relationship between the output voltage V0 and resistance R1, R2, and R3 and skin resistance Rs is given below. The skin conductance range is considered to be 1–100 μS.

    And G = gain of instrumentation amplifier

    From the above equation, we can tell that higher value C3 would ensure linearity between V0 and skin conductance but C3 needs to be low enough to keep V0 positive. Through trial and error, optimal values of R1, R2, and R3 were decided upon as 2, 220, and 5 kΩ, respectively.

  3. Low-pass filter: A first-order active low-pass filter has been designed to eliminate frequencies above 0.5 Hz. Output of instrumentation amplifier is connected to the active low-pass filter of cutoff frequency 0.603 Hz. The voltage follower that forms a part of active low-pass filter also serves the purpose of isolating sensor circuit from Arduino mega circuit. ADC of Arduino tends to draw high currents. Voltage follower draws very low current from the input circuit and provides high enough current for ADC to draw, thereby preventing ADC from overloading the sensor circuit and disrupting its functioning.

Electrodes and electrode placement

Recommended electrodes for the collection of skin conductance are silver/silver chloride electrodes. Copper or silver electrodes work as well. Copper electrodes have been used for their cost-effectiveness. Reference electrode has to be placed at a neutral location like ulna bone near the elbow where there is minimal skin conductance signal. Medial and distal phalanges give good skin conductance signals and the positive electrode terminal, i.e., the nonreference terminal should be placed at one of these locations.[3],[8]

Subjects and Methods

Various experiments were performed using the sensor developed to verify the proposed hypothesis.

Event-based experiments, where there is a clearly defined discrete stimulus given to the participant due to which there may be an onset of SCR, were conducted on the participants who volunteered. Experiments were conducted on seven participants, four males and three females, with an average age of 22, to find the resulting changes occurring in skin conductance.

The following protocol was followed for conducting experiments:

  1. Preparing the participant: Once the subject reaches the laboratory, he/she is asked to thoroughly wash their hands. Then, they are told to try to relax for about 10 min before the experiments begin
  2. Experiment 1: The participant is not given any instructions for the first 1 min of the recording. The EDA of participant in their normal state is recorded
  3. Experiment 2: After that, the participant is asked to draw a deep breath and exhale
  4. Experiment 3: Participant would then be asked to meditate or to do something that makes them feel calm and peaceful
  5. Experiment 4: Participant was startled by clapping hands close to their face
  6. Experiment 5: Math solving: Participants were asked to add up three and four digit numbers in a determined amount of time. This was to get the participants to exert some mental effort.

Humans have a typical tonic level ranging from 10 to 50 μs.[9] Tonic SCL varies based on a person's psychological state and ANS regulation. Phasic skin conductance changes when discrete environmental stimuli occur such as sights, sounds, and smells. This phasic skin conductance changes are called SCR and they usually occur 1.5–6 s after the stimulus occurs. Frequency of spontaneous SCRs is typically in the range of 1–3/min. Some individuals are highly reactive with considerable spontaneous generation of galvanic skin responses (GSRs), and others have a relatively steady tonic level of skin conductance without too many spontaneous GSRs.[10],[11]

The EDA data resulting due to the experiments performed were split into tonic conductance or skin conductance level (SCL) and phasic skin conductance or SCR using CDA and different metrics that characterize the EDA were analyzed. Ledalab, an additive toolbox developed for MATLAB, has been used to analyze the EDA data. CDA is performed using Ledalab. CDA decomposes SC data into continuous tonic and phasic activity. This method is based on standard deconvolution and is comparatively fast and quite robust (artifacts).[12]

The following metrics have been extracted using Ledalab within a response window defined after the occurrence of an event:

  1. nSCR: Number of significant SCRs within response window
  2. Latency: Response latency of first-significant SCR within response window
  3. AmpSum: Sum of SCR amplitudes of significant SCRs within the response window
  4. APD: Average phasic driver in the response window. This represents phasic activity in the response window most accurately
  5. ISCR: Time integral of phasic driver in the response window
  6. Phasic max: Maximum value of phasic activity in the response window
  7. Tonic mean: Tonic activity in the response window.

  Results Top

In a deep breathing event, we observed a variation in the nSCRs. The latency values of SCRs occurring varied from finger to finger from 1.029 to 3.5 s. This variation in latency indicates that, for the same stimulus, different pathways respond at different speeds or not at all. Variations were observed in values of SCR amplitudes and average phasic driver and the maximum value of phasic activity from finger to finger implying different levels of activity in each finger when the participant takes a deep breath. Certain channels respond faster and at a higher intensity than the others. [Figure 1], [Figure 2], [Figure 3] illustrate these variations.
Figure 1: Number of skin conductance responses observed within the response window in the right hand's little finger (Rlittle), ring (Rring), middle (Rmiddle), index (Rindex), and thumb (Rthumb) and left hand's little finger (Llittle), ring (Lring), middle (Lmiddle), index (Lindex), and thumb (Lthumb)

Click here to view
Figure 2: Latency of the first skin conductance response observed within the response window in the right hand's little finger (Rlittle), ring (Rring), middle (Rmiddle), index (Rindex), and thumb (Rthumb) and left hand's little finger (Llittle), ring (Lring), middle (Lmiddle), index (Lindex), and thumb (Lthumb)

Click here to view
Figure 3: AmpSum, skin conductance response, phasic max values observed within the response window in the right hand's little finger (Rlittle), ring (Rring), middle (Rmiddle), index (Rindex), and thumb (Rthumb) and left hand's little finger (Llittle), ring (Lring), middle (Lmiddle), index (Lindex), and thumb (Lthumb)

Click here to view

In the meditating event, we observed that SCRs did not occur in fingers other than in thumb finger of the left hand. This could imply that energy corresponding to all the other fingers is suppressed while the left thumb remains activated.

The maximum value of phasic activity was lower compared to other events.

In the event of curling of toes or clenching of stomach muscles, we observe that right-hand thumb finger and left-hand middle finger show no SCRs while the other fingers do. Latency of first significant SCR in the time window, ISCR values, amplitude-related metrics of SCR, and tonic values of skin conductance varied from finger to finger. In the experiment where the participant was startled, SCRs were not observed in little finger of the right hand, middle finger, and index finger of the left hand.

  Discussion Top

Yoga is a physical, mental, and spiritual discipline dating back to pre-Vedic Indian traditions. According to this ancient science when fingertips are stimulated by holding them in certain configurations, corresponding responses are elicited in the body. Different configurations will lead to different responses in the body. The different hand and finger positions are called mudras. Mudras are thought to make important connections in the nervous system and stimulate specific energy pathways (nadis). It is also said that mudras increase blood circulation to different parts of the brain, to important nerve junctures and the glands.

A step toward verifying if in fact, mudras stimulate different energy pathways is to give different kinds of stimulus to the individual and record the response seen in every finger. The results observed from our experiments indicate that indeed there is a difference in observations made in different fingers for different stimuli.

  Conclusions Top

Based on this analysis, it was observed that there is a marked difference from finger to finger in various metrics used to characterize EDA. Our analysis shows that our hypothesis is most probably true: fingertips do indeed represent the terminal of meridian channels.

The future direction of this work would be to analyze the impact mudras have on EDA of each finger. EEG could be combined with the ten-channel EDA measurement circuit to better map brain activity with the observed EDA in fingers.

EDA measurements have been used as a prognostic index in epilepsy [13],[14] and after brain trauma injury,[15] as an efficiency index of therapy in schizophrenia,[16] as a diagnostic tool for subclinical epileptic seizure,[16] in sleep research,[17] and in the early diagnosis of skin malignancies.[18] It is worth exploring the effect of finger to finger variations in EDA, on each of the above applications.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

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Neumann E, Blanton R. The early history of electrodermal research. Psychophysiology 1970;6:453-75.  Back to cited text no. 2
Lykken DT, Venables PH. Direct measurement of skin conductance: A proposal for standardization. Psychophysiology 1971;8:656-72.  Back to cited text no. 3
Montagna W, Parakkal PF. The Structure and Function of the Skin. New York: Academic Press; 1974. p. 376-96.  Back to cited text no. 4
Sato K. The physiology, pharmacology, and biochemistry of the Eccrine Sweat Gland. Rev Physiol Biochem Pharmacol 1977;79:51-131.  Back to cited text no. 5
Harker M. Psychological sweating: A systematic review focused on aetiology and cutaneous response. Skin Pharmacol Physiol 2013;26:92-100.  Back to cited text no. 6
Boucsein W, Fowles DC, Grimnes S, Ben-Shakhar G, roth WT, Dawson ME, et al. Publication recommendations for electrodermal measurements. Psychophysiology 2012;49:1017-34.  Back to cited text no. 7
Schell AM, Dawson ME, Rissling A, Ventura J, Subotnik KL, Gitlin MJ, et al. Electrodermal predictors of functional outcome and negative symptoms in schizophrenia. Psychophysiology 2005;42:483-92.  Back to cited text no. 8
Axisa F, Gehin C, Delhomme G, Collet C, Robin O, Dittmar A. Wrist Ambulatory Monitoring System and Smart Glove for Real Time Emotional, Sensorial and Physiological Analysis. Proceedings of the 26th Annual International Conference of the IEEE EMBS. San Francisco, CA, USA; 2004.  Back to cited text no. 9
Stork M, Benesova D. The Electronic Device for Electrodermal Responses Measurement, Applied Electronics 2006. Plzen: University of West Bohemia; 2008.  Back to cited text no. 10
Benesova D, Stork M. The Measurement of Electrodermal Activity in Process of the Sensomotor Task, Analysis of Biomedical Signal and Images. Brno University of Technology VUTIUM Press; 2008. p. 1-4.  Back to cited text no. 11
Benedek M, Kaernbach C. A continuous measure of phasic electrodermal activity. J Neurosci Methods 2010;190:80-91.  Back to cited text no. 12
Turkstra LS. Electrodermal response and outcome from severe brain injury. Brain Inj 1995;9:61-80.  Back to cited text no. 13
Poh MZ, Loddenkemper T, Reinsberger C, Swenson NC, Goyal S, Sabtala MC, et al. Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. Epilepsia 2012;53:e93-7.  Back to cited text no. 14
Colbert AP, Spaulding K, Larsen A, Ahn AC, Cutro JA. Electrodermal activity at acupoints: Literature review and recommendations for reporting clinical trials. J Acupunct Meridian Stud 2011;4:5-13.  Back to cited text no. 15
Poh MZ, Loddenkemper T, Reinsberger C, Swenson NC, Goyal S, Madsen JR, et al. Autonomic changes with seizures correlate with postictal EEG suppression. Neurology 2012;78:1868-76.  Back to cited text no. 16
Sano A, Picard RW. Toward a taxonomy of autonomic sleep patterns with electrodermal activity. Conf Proc IEEE Eng Med Biol Soc 2011;2011:777-80.  Back to cited text no. 17
Mohr P, Birgersson U, Berking C, Henderson C, Trefzer U, Kemeny L, et al. Electrical impedance spectroscopy as a potential adjunct diagnostic tool for cutaneous melanoma. Skin Res Technol 2013;19:75-83.  Back to cited text no. 18


  [Figure 1], [Figure 2], [Figure 3]

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