EMF Detection from the Human Body
Formerly
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.
http://dagsaw.sdsu.edu/3.4.html
The Fourier transform of the plot in
black is simply three lines in the frequency spectrum domain:
http://dagsaw.sdsu.edu/3.4.html
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:
http://www.sfu.ca/sonic-studio/handbook/Radio_Spectrum.html
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
attenuated
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
http://arstechnica.com/civis/viewtopic.php?f=26&t=178288,
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:
http://www.cvphysiology.com/Arrhythmias/A013a.htm
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:
http://www.drmueller-healthpsychology.com/Disorders_HeartCoherence.html
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]
EMG
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.
http://www.medscape.com/viewarticle/560221_3
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
[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
[11] The most
significant source of biomagnetic fields, generated by electric charges moving
as currents through the body, are neural impulses.
[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:
http://www.abadss.com/forum/102-faq-how/5880-understand-lnbs-standard-linear-circular-universal-lnb-lnbf-stacked.html
[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
[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
[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.
http://www.nature.com/nature/journal/v397/n6718/abs/397434a0.html
[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.
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=603645
http://web-us.com/40hz/default.htm
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.
http://www.dsprelated.com/showmessage/41546/1.php
[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
http://moon.ouhsc.edu/dthompso/pk/emg/emg.htm