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Research Study
Effects of Mental Intention on a Device Sensitive to Entrainment
Paradigm Shift in Detection Methodology of Mental Intention

Introduction


There is substantial statistical researched evidence and patent art [US 2013/0036078 A9; US 8,423,297 B2; US RE44,097 E; US 6,324,558 B1; US 6,763,364 B1; US 6,762,605 B2] that demonstrates the ability to detect the influence of mental intention on a physical device, using a randomly-generated signal with the processing of a random digital number output by various methods. The basic concept is that mental intention increases the orderliness/coherence of a randomly-produced signal.  The field of mental intention that influences a physical device is ever moving forward to create greater fidelity of, and validity for, the thought field effect.  To fulfill the goal of greater fidelity, a paradigm shift in the detection and measurement of mental influence should be considered.  The first change in methodology is to generate a random signal that is, by its nature, highly responsive to active human entrainment (ordering influence).  This human ability to actively create order from randomness is a pivotal concept to this coherence concept. The second change in methodology is a data processing technique that detects the amount mental intention changes the random signal source’s natural coherence. The third change in methodology that leads to practical application is a processing technique that provides coherence information in streaming real time. A streaming data processing technique lends itself to threshold and pattern controls for the potential purpose of mentally controlling switching, communication, feedback and mechanical movement. These signal source and processing methods have been developed and are operational as a human non-contact approach.  

Method:
Thirty-four (34) adult subjects participated in a research project.  Prior to participant participation, a trial was performed in an empty room.  A 5-minute delay in data capture was set, and then 5 minutes of unprocessed frequency data was digitally saved.  Each participant performed three 5-minute trials where he/she was requested to change the characteristics of a moving tracing on a computer screen.  The moving tracing represented the amount of coherence associated with the device’s output.

Data analysis: The unprocessed frequency data was processed from frequency to the time of the frequency.  This transformation was used to obtain the number of frequency values required to obtain a period from 10 milliseconds to 200 milliseconds in 10 millisecond increments. 300 seconds where parsed using each time frame resulting in an N values between N=30,000 to and N=5.  These periods where used to parse the frequency data to calculate the following:

The 2nd derivative of each period from 10 milliseconds to 200 milliseconds in 10 millisecond-increments. Histogram sorting separated derivative values into 10 discrete bins.  
The bias of the 2nd derivative separated into three histogram bins.  The range of the derivative bias was calculated to determine the percent of values allocated to each of three bins.  The bins contain 36% of the lowest and highest bias values while the central bin contain 28% of the values.  This provides the greatest mean discrimination between the three bins.
The running statistical mode
s frequency is within a 7000 Hz bandwidth.   

The mean of each processed value for each time frame was calculated.  Processing of the 2nd derivative, the derivative bias and the statistical mode’s frequency produced 2700 values each; from 34 participants with 4 trials each (one no intend and 3 intention trials), and 20 discrete analysis time frames from 10 to 200 milliseconds in 10 millisecond-increments.  A statistical ANOVA (analysis of variance) was performed on the three processed types comparing Trial 0, the non-intention (empty room) trial with the three intend participant trials, (trials 1, 2 and 3).  

Results:  
There was a statistically significant difference in the 2nd derivative processing at a p = 0.000 between the non-intend trial 0 and each of intend trials 1, 2 and 3.  There was no statistically significant difference at a p>0.05 between the intention trials 1 to 2, 1 to 3 and 2 to 3.

There was a statistically significant difference in the 2nd derivative bias processing at a p = 0.013 between the non-intend trial 0 and trial 1, and a p=0.000 between the non-intend trial 0 and intend trials 2 and 3.  

There was a statistically significant difference in the statistical mode’s frequency processing at a p = 0.036 between the non-intend trial 0 and trial 1, p=0.015 between the non-intend trial 0 and intend trial 2 and a p=0.030 between the non-intend trial 0 and intend trial 3.  

Discussion:
There is a statistical difference between intention and no intention trials, both in the derivative, derivative bias and frequency shift of the statistical mode of the raw frequency data.  The statistical results support the premise that humans can actively influence a randomly generated signal.  This study supports the foundational theory that humans actively entrain a devise that is already sensitive to entrainment influence. It is apparent from the derivative statistical evidence that humans organize a random signal by increasing its coherence; creating greater consistency in the signal’s rate of change.  It is further apparent that humans create a frequency shift when influencing a random signal.  

Conclusion:  
There is strong statistical evidence that human intention affects the present device using its entrained signal and rate of change and frequency shift processing.



 
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