Important Note: The information presented in this article has been updated and corrected by new information. Please read the new article here.
Well, we have a very interesting article today. We're going to take a look at the scientific papers, some strange signals hiding behind mains hum and a video of a CNN reporter having images inserted into his brain.
Its interesting that the media was all over this technology in the mid-80's pushing for its development, but surprisingly quiet when the reports of abuse and torture started to roll in.
So much for corporate responsibility.
The Scientific Papers
One of the interesting things about discussing radio-based brain computer interfaces with people, is the complete lack of public knowledge that exists. The first reaction is always either a smirk or reference to a tin foil hat. I often wonder why this is given the amount of scientific material that exists. I suppose that it has very little application outside of the military and what civilian application it could be applied to could not justify the development, infrastructure and on-going maintenance costs.
To help remedy this situation, I have collected five of the best papers of the subject. The first three I have posted links to the previous article. These cover establishing synchronization between the neurons and the ELF E-field and then controlling the firing pattern. Basically, that all there is to it. Scale it up, control the firing patterns of a wide range of neuron clusters and you can exercise control or induce hallucinations in a person.
It doesn't matter if you are 2000Km away.
Chaos control and synchronization of two neurons exposed to ELF external electric field
School of Electrical and Automation Engineering, Tianjin University, Tianjin 300072, China
Accepted 23 March 2006. Available online 26 May 2006.
Chaos control and synchronization of two unidirectional coupled neurons exposed to ELF electrical field via nonlinear control technique is investigated. Based on results of space–time characteristics of trans-membrane voltage, the variation of cell trans-membrane voltage exposed to extremely low frequency (ELF) electric field is analyzed. The dynamical behaviors of the modified Hodgkin–Huxley (HH) model are identified under the periodic ELF electric field using both analytical and numerical analysis. Then, using the results of the analysis, a nonlinear feedback linearization control scheme and a modified adaptive control strategy are designed to synchronize the two unidirectional coupled neurons and stabilize the chaotic trajectory of the slave system to desired periodic orbit of the master system. The simulation results demonstrated the efficiency of the proposed algorithms.
Unidirectional synchronization of Hodgkin–Huxley neurons exposed to ELF electric field
School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, PR China
Accepted 29 May 2007. Available online 26 July 2007.
In this paper, a hybrid control strategy, H∞ variable universe adaptive fuzzy control, is derived and applied to synchronize two Hodgkin–Huxley (HH) neurons exposed to external electric field. Firstly, the modified model of HH neuron exposed to extremely low frequency (ELF) external electric field is established and its periodic and chaotic dynamics in response to sinusoidal electric field stimulation are described. And then the statement of the problem for unidirectional synchronization of two HH neurons is given. Finally H∞ variable universe adaptive fuzzy control is designed to synchronize the HH systems and the simulation results demonstrate the effectiveness of the proposed control method.
Fire patterns of modified HH neuron under external sinusoidal ELF stimulus
School of Electrical and Automation Engineering, Tianjin University, Tianjin 300072, China
Accepted 5 August 2008. Available online 17 September 2008.
Neuron as the main information carrier in neural systems is able to generate diverse fire trains in response to different stimuli. In this paper, the stimulus frequency is taken as the bifurcation parameter, and ISI is considered to be one of the state variables. Via numerical simulation, we mainly concentrate on the kinds of fire patterns that the modified HH neuron model displays such as period-n, bursting, and modulation fire patterns, etc. under the effect of external sinusoidal ELF electric field, and the relation between the ISI sequences and the external stimulus just like synchronization and transition in the manner of pitchfork bifurcation. In addition, an explanation is put forwards from the electrophysiology point of view to try to interpret why neurons generate so many different kinds of ISI sequences.
This next paper I included to make a point. If you study it carefully you will see that it closely resembles the paper directly above. This is a good example of reinventing the wheel. In this case, this paper predates the previous paper by 9 years. This raises the question of just how long before this paper was the initial government funded research done? Was it a product of need and capability? Keep this in mind when you watch the video later, as this is a practical demonstration of this next paper produced in 2010.
Modeling the effect of an external electric field on the velocity of spike propagation in a nerve fiber
John M. Myers
Gordon McKay Laboratory, Harvard University, Cambridge, Massachusetts 02138
Received 21 April 1999; published in the issue dated November 1999
The effect of an externally generated electric field on the propagation of action potentials is modeled, assuming the Hodgkin-Huxley equation for the voltage-dependent conductance of the membrane of a nerve fiber. With some simplifying assumptions, this conductance together with Maxwell’s equations leads to the Hodgkin-Huxley differential equations for propagation, modified by a term proportional to the gradient of the externally generated electric field component along the nerve fiber. Computer solution of these equations shows the influence of an electric field gradient on propagation velocity. When the electric field oscillates, voltage spikes starting later along a given axon advance or lag relative to earlier spikes, so the time between spikes at the receiving end differs from the time between spike originations. The amount that a low-frequency electric field modulates pulse timing at the end of a fiber relative to that at the beginning is estimated under several conditions.
Now this paper gets very close to the mechanism that has been described in this series of articles. Note that this is the first paper to show that quite apart from transmitter design, the properties of the tissue are important to the reception of that signal. Not only this but the magnitude and frequency are important to prevent signal rejection.
Med Biol Eng Comput. 2011 Jan;49(1):107-19. Epub 2010 Nov 10.
Transmembrane potential generated by a magnetically induced transverse electric field in a cylindrical axonal model.
Ye H, Cotic M, Fehlings MG, Carlen PL.
Toronto Western Research Institute, University Health Network, Toronto, ON, Canada. email@example.com
During the electrical stimulation of a uniform, long, and straight nerve axon, the electric field oriented parallel to the axon has been widely accepted as the major field component that activates the axon. Recent experimental evidence has shown that the electric field oriented transverse to the axon is also sufficient to activate the axon, by inducing a transmembrane potential within the axon. The transverse field can be generated by a time-varying magnetic field via electromagnetic induction. The aim of this study was to investigate the factors that influence the transmembrane potential induced by a transverse field during magnetic stimulation. Using an unmyelinated axon model, we have provided an analytic expression for the transmembrane potential under spatially uniform, time-varying magnetic stimulation. Polarization of the axon was dependent on the properties of the magnetic field (i.e., orientation to the axon, magnitude, and frequency). Polarization of the axon was also dependent on its own geometrical (i.e., radius of the axon and thickness of the membrane) and electrical properties (i.e., conductivities and dielectric permittivities). Therefore, this article provides evidence that aside from optimal coil design, tissue properties may also play an important role in determining the efficacy of axonal activation under magnetic stimulation. The mathematical basis of this conclusion was discussed. The analytic solution can potentially be used to modify the activation function in current cable equations describing magnetic stimulation.
What none of these scientists has yet grasped is the secret to speeding up their research. The secret lies in recording the signals produced by the brain, storing the patterns and associating those patterns to activity. Its just a matter of replaying the signals, with the same physical characteristics, to induce remote control over the neuron clusters.
The upshot is that we are about 12-24 months away from someone reproducing the basics of the NSA's neural interface.
Tests in Europe (Germany) between 2000-2003 revealed the existence of a transmitter broadcasting in the ELF frequency range. The transmissions were hidden by the hum of electrical power grid and the fact that they appeared to be using the Earth itself as an antenna. You can read more about the signals, the equipment used and circuit diagrams here:
As you will find out in the video to come at the end of this article, the strength of a signal required to create an image in the brain is a thousand times weaker than the Earth's magnetic field. So standard oscilloscopes, with millivolt divisions, would barely register voltage of the E-field without some major amplification. The setup used to detect these hidden signals had an amplification factor of about 240,000. Given how weak this signal was, the fact that it was hidden behind electrical noise and that it could only be detected in the ground indicates someone went to extremes to hide this transmitter.
To this day, no major government has acknowledged this transmitter, its function or who operates it. Without a more detailed analysis of the signal, it would be nearly impossible to tell if it relates to remote neural communication.
The one thing to take from this is that classified transmitters of this type have been proven to exist.
CNN Meets Mr Computer's Retarded Cousin
This video is perfect, it shows very weak magnetic fields about 1/1000 the strength of the Earth's magnetic field, pulsed at ELF frequencies inducing controlled hallucinations in a target. The video is from a CNN segment on Soviet mind control technology in 1985. The reporter gets a scientist and an engineer to use of the shelf components to put images into his mind. What's really interesting is that the engineer claims that it would take about 3 week to modify it to effect an entire town.
Obviously, it is a cut down version of the real thing. The first difference we notice is that this system does not match particular frequencies to either a particular target or neural network. This may indicate that the field strength used far exceeds what is required to achieve the effect. The second point to note is that this is using the magnetic field, rather than the E-field.
It also demonstrates something we refereed to earlier. This is a practical demonstration of a paper that was not written until 2010, even though this segment was shown 25 years earlier. Not only this, but the technology used is based on an old Soviet design, meaning that it could be 10-15 years older again. This means the research into this began at least in the 1960's with implementation in the 1970's. If the Soviet's considered this old technology in the mid-80's, it would seem to indicate that a far more complex version was in current service. Despite claims of the US being behind in the technology, it is more likely given the access to better processors and hardware in general, that the NSA had, in fact, a fully functional system at this time.
If you are just interested in the device that can put images into the brain, skip ahead to time index 06:31.
1985 CNN Special Assignment