EMF Detection from the Human Body


Measuring Voltages from the Human Body

Updated 29 March 2011

Added paragraph: New Techniques in Weak Signal Detection

Updated 8 November 2011

Added details of gamma ray detection in Schwartz study

Updated December 26 2011 EEG in blue


Abstract: Current standard allopathic measurements of EEG, ECG and EMG are usually pathology based. Nanoscience evidence suggests that radio and microwave electromagnetic energy is radiated from human tissue, may be detectable, and may provide important information on internal states. Hunt, Mendanha, and Rubik have suggested that including higher EMF frequencies might increase our knowledge base, especially for the study of higher level functioning. Research has already found that the brain emits higher frequencies (EEG) than were ever considered before, and these frequencies relate to very high level function. Are we missing other meaningful higher frequency signals?


Fourier Transforms and the Frequency Spectrum

The Fourier Transform (FFT) is used to deconstruct  a complex signal into simpler components of various frequencies. [1] For this reason it is useful for extracting information on pattern or regularity where none is apparent.  For example, the complex looking plot in black below (fourth plot) results from a combination of  three simple cosine waves with three distinct frequencies, shown in blue, green, and red above the black plot.








The Fourier transform of the plot in black is simply three lines in the frequency spectrum domain:








Because of this ability to find order in apparent randomness, the Fourier transform (FFT) is often used to process electrical signals in real world phenomena, including electrical voltages from the human body.



A few words from the Mavericks.

Joseph Chilton Pearce has strongly connected biological cells with the earth and solar system by way of their similar electromagnetic fields. In an interview, [2] Pearce stated: “…the heart produces two and a half watts of electrical energy at each pulsation, creating an electromagnetic field identical to the electromagnetic field around the earth. The electromagnetic field of the heart surrounds the body from a distance of twelve to twenty-five feet outward and encompasses power waves such as radio and light waves which comprise the principle source of information upon which the body and brain build our neural conception and perception of the world itself. “


Pearce’s claims prove hard to support. Most FFT and Power spectrums of the heart found on the internet look at cardiac frequencies of about 1 Hz using a sample rate of only about 100 Hz, and investigate heart rate variability, an important indicator of human health. [3]


Rollin McCraty, research director for Heart Math notes Pearce’s statement is inaccurate.[4] Inaccurate, but perhaps not entirely false?


Beverly Rubik notes that recent research has shown that the brain emits higher frequencies, detectable by EEG, than were ever considered before. [5]


As noted in Validation of the Human Energy Field,  Dr. Valerie Hunt’s book Infinite Mind, published in 1989 and 1996, reported on her 1970s research, which found that a specially wired Electro Myo Graph (EMG) machine detected very high frequency signals; much higher than normally found in muscular activity. These signals, recorded ostensibly from  traditional chakra locations, were found to exhibit chaos, and have been interpreted as having a very high information content. [6] The EMG readings ostensibly correlated with the color descriptions of the chakras given by energy healer Rosalyn Bruyere. [7] The Hunt studies recorded ‘Chakra’ EMG frequencies of up to 16 kHz, but also measured EEG frequencies up to 200 kHz in meditators, as high as their equipment would go. [8] This is interesting because the EEG is generally believed to be a low frequency data source. Hunt’s work has been ignored by the scientific community. Hunt’s  book shows connections for bipolar and ground as well as primary signal were used. (See below section on EMG)



In 2008, the Indian Journal of Physiology and Pharmacology [9] published a short paper by RE Mendanha in the vein of Hunt’s work, noting that the standard procedure of filtering out higher frequencies may be filtering out coherent information of the human biofield. He has reported a stable signal at 40 kHz (40000 Hz) in the frequency domain which responds to the test subject’s meditation state.


Were Hunt’s 16 and 200 kHz signals, and Mendanha’s 40kHz signals only noise? Do relatively high frequency non-noise  signals exist? Do they have structure? Do they exhibit chaos? Are they “intelligent”? Do they correlate with manifestations of the HEF? Do they correspond to chakra locations as Hunt found? More generally, are higher frequency EM fields, measurable by conventional voltage,  emitted from the human body which thus far remain undetected?



Microwave, gamma and X-ray radiation from the human body?

Sources of measurable electromagnetic energy inside the body may be external as well as internal. External sources include static electricity from the non electrical environment as well as fields induced by external electro-mechanical devises, such as the 60 Hz power grid.


According to a recent article on nanomedicine,[10] the most important internal electrical sources are electrochemical gradients caused by gated channel and transporter molecular pump operations at the intracellular level; muscular, membrane, digestive, and neural activity at the intercellular and organ level and piezoelectric fields generated by movement of collagenous tissues, for example, tendons and bones. [11] Typically these sources generate potentials of 10-100 millivolts over distances of 0.01-10 microns.


Observing that millivolt electrical potentials maintained across cell membranes ~10 nm thick gave rise to huge fields ~107 volts/m,  Herbert Frohlich theorized that membrane molecules must be highly electrically polarized and thus could interact to produce coherent surface acoustic vibrational modes in the 10-100 GHz (microwave) frequency range. [12]


Audio and Radio Frequency:





He showed that once energy reaches a certain level, molecules begin to vibrate in unison, until they reach a high level of coherence, when they may take on certain properties of quantum mechanics. [13] One might expect such a macroscopic quantum system to be detectable macroscopically.

The nanomedicine article notes that direct detection of 10-100 GHz millimeter radiation by non-nanotechnological means is difficult, because water in body tissue strongly absorbs microwaves. [14]

Further, Debye-Huckel shielding due to counterion flow in salty fluids reduces electric voltage fields very rapidly with distance. [15] Magnetic fields, in contrast, are not significantly attenuated  Attenuated
Alive but weakened; an attenuated microorganism can no longer produce disease.

Mentioned in: Tuberculin Skin Test---

having undergone a process of attenuation.
..... Click the link for more information.
as they pass through body tissues. [16]

The study concludes that radio frequency and microwave electromagnetic signals may be detectable by nanotechnology and could provide information on numerous conventional internal states. [17]

Dr. Gary Schwartz, director of the Human Energy Systems Laboratory at the University of Arizona, reports detection of 12 giga-Hz (Ku band) microwaves from the hands and body using relatively inexpensive radio frequency satellite communications equipment. His system consists of an18 inch satellite dish with LNB detector,  a signal strength meter with visual and auditory feedback, and an analog output to a digital voltmeter whose values are recorded on a computer. He notes that the LNB detector can also be used without the dish to detect microwave signals coming from the hand. [18]


The LNB, or low-noise block downconverter, processes the giga-Hz signals collected by satellite antennas;  amplifies them, and converts them to a lower frequency, typically in the mega-Hz range, where they may travel through cables with much less attenuation. Three microwave frequency bands from satellite are readily commercially available; the C band, in the 3-4 giga-Hz range, the Ku band, in the 10-12  giga-Hz range, and the Ka band, in the 19-21 giga-Hz range. [19]


Using Princeton Gammatech gamma ray spectrometers with sodium iodine detectors, Schwartz, Jones et al 2002 have replicated in four experiments that the human body absorbs and/or scatters cosmic gamma radiation and emits high frequency X-rays. The closer the subject to the detector, the greater the decrease in recorded gamma radiation and the greater the increase in recorded X-ray emission.  These observations have been independently replicated.


Preliminary findings suggest that states of relaxation are associated with dynamic increase in gamma radiation absorption and/or scatter, as well as an increase in emission of high frequency X-rays. Schwartz speculates that X-ray emission and/or gamma ray absorption may play a role in energy healing mechanisms. [20]


New Techniques in Weak Signal Detection

Chaotic oscillators are now being studied as a new method for obtaining weak signal data in a noisy environment. This new technique avoids the issues of working in the time domain and small signal to noise ratio. The paper The application of chaotic oscillators to weak signal detection explains that chaotic systems are sensitive to certain signals and immune to noise at the same time. [21] The paper Weak Signal Detection Principle Based on Chaotic Duffing Oscillator and its Simulation Method provides another approach to a similar problem. [22] Such techniques would seem to offer a lot of potential in the study of weak signals from the human body.


A Quick Look at Traditional Voltages From the Human Body

Spontaneous voltages from the brain, heart,  and muscle tissue  in the body are traditionally and routinely recorded by use of the  EEG, EKG, and EMG. [23]


EEG (Brain)


Typical EEG sample rate is 256 hz. As many as 16 different channels, connected to sensors on various parts of the head, constitute a complete EEG. There is general agreement that the brain functions in the ELF frequency range. Traditionally, four frequency bands have been of interest [24]:  Delta waves, 0-4Hz,  associated with deep sleep; [25]  Theta waves, 4-7Hz, associated with sleep; [26] Alpha waves, 7.5-13Hz, associated with relaxation, (10 to 50 mV ); and Beta waves, 13-30Hz, associated with alertness, arousal, problem solving, and concentration, (low amplitude.)[27]


More recently interest has increased in higher frequency brain  waves. The gamma band, described variously from 20 to 70 Hz, is now popular in research circles. [28]  An excellent article; A Brief History of the 40 Hertz Rhythm, pulls together a number of threads in the study of the Gamma band, including the association of the gamma band with event binding, loving kindness and compassion meditation, and consciousness. During deep meditation the gamma band activity is found at several locations, including the forehead region, on the scalp. [29] Rubic also notes the 40-Hz emission from the forehead region appears to involve highly positive emotional states with reduced stress.[30] Feedback training is now available which allows a person to see the 40Hz they are producing, and learn how to produce more, resulting in more positive emotions. [31]


The gamma band has also been associated with expanded human capabilities. Inego  Swann is considered one of most proficient remote viewers in the world. Dr. Michael Persinger monitored his EEG and found that the remote viewing process used high beta/gamma brain  waves. [32]

Much has been written about the importance of the alpha wave in relaxation,  and the synchronization of the low alpha frequency with the fundamental 7.8Hz Schumann resonance of the earth’s geomagnetic field. However, the nominal average Schumann frequencies observed are not only the fundamental at 7.8, but also harmonics at 14, 20, 26, 33, 39, and 45 Hz. These frequencies span the Beta and Gamma bands. 

Experiments have shown the effect of screening out of natural electromagnetic fields on human beings. Traditionally it is reported that subjects suffer a number of effects, which are attributed to screening of the Schumann 7.8Hz fundamental frequency. [33] Considering the importance of the 40Hz band in binding sensory inputs into the single, unitary object we perceive, it seems likely that the shielding of this band would also cause significant distress.



Meanwhile, 40Hz research continues. [34]


Low frequency EEG FFT components may only be artifacts generated by transient shifts in the DC potential.


Two sources of transients in EEGs; episodic neurological events such as the SCP, and the time relaxation of an amplifier circuit due to an abrupt change in the signal.  generate multiple ‘phantom” FFT harmonics. [35]



The following image, from


shows the relative amplitude of the different EEG bands.










ECG (Heart)


According to the Hewlett Packard Journal, [36] the amplitude of the ECG signal as measured on the skin ranges from 0.1 mV to 5 mV with an average of 1mV. The frequency extends from 0.05Hz to 130Hz.


The most widely used measurement configuration are three differential voltages: From right arm (RA) to left arm (LA), from LA to left leg (LL), and from LL to RA. These voltages are known as ECG leads I, II, and III. The right leg electrode (RL) acts as the neutral pole in this system. This configuration is known as the Eindhoven triangle.


Eindhoven triangle from:




Although cardiac automaticity is intrinsic to various pacemaker tissues, heart rate and rhythm are largely under the control of the autonomic nervous system, and thus intimately tied to the bodily expression of emotions. 

Heart Rate Variability (HRV) and Heart Rate Variability Coherence are considered important measures of health.

Heart rate vari­abil­ity (HRV) is a mea­sure of the con­tin­u­ous inter­play between sym­pa­thetic and parasym­pa­thetic influ­ences on heart rate that  rep­re­sents the capac­ity for healthy emo­tional response. [37]

In states of stress, including anxiety anger and sadness, variability tends to be disordered and chaotic. In positive emotional states such as love and gratitude, the variation tends to be ordered and rhythmic. This state of rhythmic variation is known as Heart (Rate Variability) Coherence, and is a highly efficient and healthy mode of operating.

Images from:


The left side of the above images show two types of Heart Rate Variability. The right side shows a biofeedback computer screen of chest and abdominal breathing (blue tracings) and moment-to-moment change in heart rate (red tracing). When the breathing and heart rate tracings become perfectly synchronized, maximal Heart Rate Variability Coherence is in effect. In this case the FFT in the frequency domain shows that the resulting signal has a very narrow frequency spectrum [38]




The EMG signal shows electrical activity in muscle tissue during contraction (Thus the ECG is a special kind of EEG).

According to an excellent document written by Dr. Scott Day, [39] Typical band pass frequencies in EMG recording are between 10-20 hz (High pass filtering) to between 500-1000 hz. (low pass filtering) High pass filtering is necessary to remove artifacts due to inadvertent movement. Low pass filtering is desirable to remove high frequency components to avoid signal aliasing [40]. Typically EMG signals have a large contribution of frequencies from 50-60 hz. Notch filters have been used to remove power line effects, but these also remove important EMG signal information. High quality amplifiers have adjustable gains of between 100 and 10000. This range is sufficient for recording  surface EMG signals which can range from 0 to 6mv peak to peak. According to a 1985 study by Basmajian & DeLuca, the skin surface EMG signal may vary from uV (microvolts) to low mV (millivolts).  [41]


According to a study by Gerdle et al, the signal properties depend on a number of factors, including timing and intensity of contraction, distance of the electrode to the active area,  properties of the overlying tissue,  electrode and amplifier properties,  quality of contact between electrode and skin, and noise.


Ambient noise is generated by EM devises such as computers and power lines at a dominant frequency of 60 Hz. Transducer noise is generated by the electrode- skin junction.


Electrodes convert ionic currents generated in muscle tissue into electro magnetic current, with its corresponding voltage. Two types of noise result from the transduction from ionic to electronic form: DC voltage caused by differences in the impedances of  electrode and skin and electrode, as well as from chemical reactions taking place between the electrode and conductive gel. AC voltage from fluctuations in impedence between the electrode and skin. Ag-AgCl electrodes are used to decrease impedence effects.


Bipolar recording technique is used with a differential amplifier which subtracts the signal amplitude at one electrode from the other and then amplifies the difference. The concept is that correlated signals, common to both electrodes, such as ambient EM noise and far ranging signals from other internal sources, as well as DC components at the electrode-skin junction will be filtered out.


An imbalance between electrode-skin junction characteristics at the different electrode sites, commonly called electrode-skin impedance  can also effect the differential amplification in bipolar measurements.  The more balanced the skin impedance between electrode sites, the more accurate the resulting signal. Further, studies suggest that given the impedances are balanced, the level of this impedance will effect the energy of the EMG signal as a function of frequency. Low impedance (<10k ohms) resulted in a high energy EMG signal for frequency components under 100 hz, but a low energy EMG signal for   frequencies between 100 and 150 hz.


For small EMG target sources, surrounding sources may effect readings in what is called “cross talk”, which is reduced by selecting the appropriate electrode surface area and inter-electrode spacing.


Mainly because impedance between the target source and electrode varies, the amplitude of the signal will vary among individuals and within one individual over time. Normalization by referring measurements to a given muscle stress level is used to get around this.  [42]





The Problem of Finding Valid High Frequency Voltage Signals from the Human Body


In his article on EMG recording above, Dr. Day notes the issue of signal aliasing. Signal aliasing just means that multiple signals (aliases) may be represented by an inadequate sample rate. For a given signal frequency,  aliasing can always be reduced by increasing the sample rate. The first figure below shows adequate sampling; the second figure shows that the sample rate is too low for the sampled frequencies, which results in an ambiguity as to which signal is actually present. 




The use of higher sample rates will allow greater detail of any signal analyzed. The real problem in any signal sampling is distinguishing a true signal from noise.


A number of pitfalls are associated with trying to obtain meaningful FFT data, especially at higher frequencies.


It is common for non-linear systems to couple at different frequencies, so low frequency components may be linked to higher frequency components. Also, frequencies may appear in the FFT which are just artifacts of  lower frequency phenomena. For example, the following shows the frequency spectrum for successive buildup of a sine wave to a square wave. Note that the higher frequencies in the frequency spectrum are  merely the result of the buildup of the low frequency signal.







Even transients may cause phantom harmonics, or artifacts in the FFT signal.


One safeguard against random noise is repeatability of the signal.


Could noise explain Hunt’s signal? Apparently not, since her result was confirmed by independent aura readers.




[3] For example:

 http://www.neurotraces.com/scilab/scilab2/node59.html shows 3 successive cardiac pulses of one second duration, or “epochs”; sampled at 100 Hz.

then shows FFT to 100 Hz of these. The effect of increasing sample rate is shown. In all cases the lower and higher frequencies are largest. The three heart pulses show time and frequency variability.



http://www.librow.com/cases/case-2 shows matlab program which removes low frequencies (due to subject breathing etc) and then does inverse FFT to get back the time history with low frequencies removed


[4] [email response]


[6] More on the chaos of human physiology at:



EMG signals do have strange attractors:

Multifractal characterization of electromyogram signals





[7] http://www.somatics.de/HuntStudy.html

Pg  24-27: Science of the Chakras
In her book Infinite mind, Valerie Hunt reports using an electromyogram to record signals from a subject’s upper and lower arm, back, and head.  As the subject began to dance, the signals from the upper and lower arm and back stopped, but the signal from the head became intense. Dr. Hunt concluded she had recorded electromagnetic radiation from the subject’s crown chakra. 40. Dr Hunt later had a NASA engineer, who had developed highly sensitive telemetry systems, build a system to record very weak signals. Surface sensors picked up the body’s electrical signals by an FM radio frequency carrier via a miniature battery operated radio transmitter and amplifier attached to the subject by a belt. The transmitted signal was then picked up by a radio receiver and recorded. This system allowed Dr. Hunt “to record regular high frequency oscillations coming from the chakras that had never been previously recorded or reported in the scientific literature. … conventional recordings are taken by inserting needle sensors or probes into a nerve or muscle, which gives a reading only for a very local area. Dr Hunt attached the electrodes to the surface of the skin where there was a larger signal. She amplified and filtered the baseline data to remove the brain and muscle signals, including the heart. She discovered a void of electrical activity   between about 250 and 500 cycles per second (hertz), and then discovered continuous activity from 500 to 20,000 cps (the highest capacity of the instrument at the time. 41. An energy healer treated a patient. The EMG signals at the chakra locations up  the patients spine and out of the top of his head during healing agreed with the flow of energy reported by a medical intuitive. That intuitive was  the quite well known Rosalyn Bruyere. 42
Dr. Hiroshi Motoyama of Japan had no way to measure the alleged energy of the chakras, he hoped to measure secondary energy, such as electrostatic fields. He found that the level of energy found at chakra locations of advanced meditators  was significantly greater than that found at the corresponding location of control subjects. (Those with no meditative or psychic experience) He also monitored the energy advanced meditators claimed to be able to project through their chakras. He documented significant electrical field disturbances emanating from the activated chakras. These results were repeated by Itzhak Bentov, author of Stalking the Wild Pendulum. 43
Gordon p. 26 on.
               40 Hunt, Infinite Mind 11, 19, 21
               41 ibid 18, 19, 21, 27
               42 ibid 14-16





[8] Hunt, Infinite Mind, data exhibit


[9] A method for computerized recording and analysis of high frequency biopotentials.

Mendanha RE

Indian Journal of Physiology and Pharmacology 2008 52(4): 398-402



also posted at NIH:





[11] The most significant source of biomagnetic fields, generated by electric charges moving as currents through the body, are neural impulses.


[13] http://en.wikipedia.org/wiki/Herbert_Fr%C3%B6hlich  also see The Intention Experiment Lynne Mc Taggart p. 49 f


[16] So for the EEG, ECG and EMG, there are the MEG, MCG, and MMG, magnetic sensors which can pick up much more information than their electric counterparts.


[17] Such states may include cytoskeletal dynamics, metabolic rates, plasmon-type excitations due to the collective motion of ions freed in chemical reactions, positional, rotational or conformational changes in biological macromolecules and membranes, internal movements of organelles and nerve traffic conduction, cellular pinocytosis, cellul….


[18] Measuring Energy Fields: State of the Science edited by Dr. Konstantin Korotkov Backbone Publishers 2004: Review of contemporary biofield measures GE Schwartz PhD. P. 175-176


[19] See http://www.satcritics.com/tech_lnb1.php, http://en.wikipedia.org/wiki/Low_noise_block-downconverter,

and http://www.satsig.net/lnb/explanation-description-lnb.htm

A good description of the conversion process:

“When the signal is reflected from the dish, it enters what is called a feedhorn. The feedhorn, or feed for short, focuses the received signal down a tuned tube, which is called a waveguide. We sometimes refer to the waveguide as the THROAT of the feedhorn. At the end of the waveguide is the entrance to the amplifier. The amp usually has 2 probes that is each oriented to correspond to the horizontal and vertical polarities, and is switched by the amplifier according to which polarity is needed. A circular feedhorn has a small device that transforms the circular signals to either horizontal or vertical signals for processing by the amp. The amplifier is generally called an LNA (low noise amplifier).

”The amp does it's job and amplifies the very weak microwave signal, and then it's passed on to a device called a downconverter. A microwave signal has extremely high loss when trying to send it down a regular coax. After just a few feet there would not be much signal left because of this loss. Lower loss coax could be used, but it would be extremely expensive. The downconverter "converts" the signals to a much lower frequency so that relatively inexpensive coax can be used to get the signal from the dish to the receiver. Todays technology has allowed the combining of the LNA and downconverter into the same box. This is called an LNB, or Low Noise Block converter. And feeds are now being integrated with LNB's into a single assembly known as an LNBF.” From:





[20] Measuring Energy Fields: State of the Science edited by Dr. Konstantin Korotkov Backbone Publishers 2004: Review of contemporary biofield measures GE Schwartz PhD. P. 176

[22] http://www.scientific.net/AMR.108-111.834 Cost associated with download.

[23] An evoked potential  is a voltage recorded from the nervous system following an external stimulus, as distinct from spontaneous voltages as detected by electroencephalography (EEG) or electromyography (EMG). Evoked potential amplitudes tend to be low, “ranging from less than a microvolt to several microvolts, compared to tens of microvolts for EEG, millivolts for EMG, and often close to a volt for ECG.”http://en.wikipedia.org/wiki/Evoked_potential


[24] Slow cortical potentials (SCPs) persist for longer than six seconds, show a sharp cusp, and are not periodic.



[25] They are a high-amplitude, low-frequency wave, and are generated by the lack of processing by neurons.


[26] Also associated with anxiety


[28] One of the more conventional studies shows that not only is gamma-band activity increased in associative learning, but also that gamma coherence increases between regions of the brain that receive stimuli involved in an associative-learning task. This supports Donald O. Hebb’s concept of strengthening cell assembly binding as the basis for associative learning.




[32] Lynn McTaggart, The Intension Experiment p. 75f


[34] The authors describe the extraction of 40 Hz burst EEG activity in the face of electromyogram (EMG) noise. Extraction is done using time- and frequency-domain analysis, with a neural network to automate the task. The data are then analyzed using chaos theory to show how the 40 Hz activity can be used to recognize differences in cognitive states.






Assume EEG data collected from an electrode. I would like to discriminate the power of 30 hz, 35 hz, and 40 hz. Typical FFT
amplitude is 3 microvolts across much of the spectrum, and it's typical to see power of 40 hz increase to 8 microvolts for periods of 100
milliseconds or so (though often longer, but the short bursts are the ones of interest). Data is band-pass filtered .5-100hz.




[37] HRV from medical perspective; good discussion of methods of measurement. Notes many interpretations  of HRV.



[39] Important Factors in Surface EMG Measurement



[40] see http://www.answers.com/topic/aliasing for a discussion of aliasing.


[41] These values are consistent with 1998 data provided by  D. Thompson, PT,  who notes: EMG signals exist in a frequency range between 20 and 200 Hz. Because movement artifacts have frequencies less than 10 Hz, that is the cutoff frequency frequently employed in "high-pass" filters.  Single muaps (muscle fibers) have an amplitude of 100 microvolts, and signals detected by surface electrodes are in range of 5 mV