The Physics of Mind-Matter Interaction at a Distance

The aim of this work is identification and localisation of the interaction between mind and matter, specifically with respect to random number generators, and identification of the type of energy that can alter the degree of randomness of bit-string outputs of these electronic devices. Regarding localisation of the mind/random-number- generator interaction, we believe it occurs through the production of electron+gap pairs in the inversely polarised P-N junction of the Zener diode that is used as a white noise generator, with resulting peaks of non-random current. Conversely, regarding the type of energy acting on the analogue signal, we believe it is made of photons of wavelength ranging from 0.2 to 1.1 \u3bcm, each therefore carrying an energy of between 6.2 and 1.14 eV. The most controversial part concerns the means by which the human mind can produce this type of energy from a distance to act directly on a chosen target, in that it is not possible for it to have been emitted by either the body or brain as biophotons

A new method to detect event-related potentials based on Pearson\u2019s correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience. Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise. The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP\u2019s waveform, these waveforms being time- and phase-locked. In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson\u2019s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase\u2014in consonance with the stimuli\u2014in EEG signal correlation over all channels. This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs. These hidden components seem to be caused by variations (between each successive stimulus) of the ERP\u2019s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology. The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language

EEG correlates of social interaction at distance

This study investigated EEG correlates of social interaction at distance between twenty-five pairs of participants who were not connected by any traditional channels of communication. Each session involved the application of 128 stimulations separated by intervals of random duration ranging from 4 to 6 seconds. One of the pair received a one-second stimulation from a light signal produced by an arrangement of red LEDs, and a simultaneous 500 Hz sinusoidal audio signal of the same length. The other member of the pair sat in an isolated sound-proof room, such that any sensory interaction between the pair was impossible. An analysis of the Event-Related Potentials associated with sensory stimulation using traditional averaging methods showed a distinct peak at approximately 300 ms, but only in the EEG activity of subjects who were directly stimulated. However, when a new algorithm was applied to the EEG activity based on the correlation between signals from all active electrodes, a weak but robust response was also detected in the EEG activity of the passive member of the pair, particularly within 9 – 10 Hz in the Alpha range. Using the Bootstrap method and the Monte Carlo emulation, this signal was found to be statistically significant

Exploring the Brain-to-Brain interaction at a distance: a global or differential relationship?

Backgound: The main objective of this exploratory study was a confirmation of the results obtained by Giroldini et al, 2016, relative to the possibility of identifying a long-distance connection between the EEG activities of two totally isolated subjects, one of whom was stimulated with light and sounds. In this new study we have used the method of the steady-state stimulus (visual and auditory) given at the frequencies of 10, 12 and 14 Hz in order to answer the following questions: - What is the relationship between the power of the EEG response in the stimulated partner and that of the other isolated partner? - Is the relationship between the EEG activities of the stimulated and the isolated partner global (i.e., an undifferentiated response), or is it differentiated and thus displays variations depending on the characteristics of the stimulation applied to the stimulated partner? Methods: Five adults chosen for their experience in mind control techniques and their mutual friendships took part in this study. Each participant took turns in being both the stimulated partner and the isolated non-stimulated partner with each of the others, making a total of 20 pair combinations. The stimulated partner received three blocks of 32 visual-auditory stimulations lasting 1 second modulated at 10 Hz, 12 Hz, and 14 Hz respectively, with a constant inter-stimulus interval of 4 seconds. The EEG activity of each pair was recorded at 128 samples/sec over 14 channels and analyzed by measuring traditional steady-state potentials and the Pearson’s linear correlation between all possible signal pairs with an innovative algorithm. Results: From the results of twenty pairs of subjects, we found an overall increase in the correlation among the EEG channels of the isolated partners, therefore confirming the previous research. Furthermore, we did not find any correlation between the correlation strength among the EEG channels of the stimulated partner and that observed in the non-stimulated partner, suggesting that this physical characteristic cannot be transferred between isolated partners; but we did find that the correlation among the EEG channels of the isolated distant partners changed not only globally, but also when the frequencies perceived by the stimulated partners were outside the Alpha band, suggesting that this neurophysiological mental connection at a distance may be differentiated

EEG correlation at a distance: A re-analysis of two studies using a machine learning approach

Background: In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms. The first dataset comprises the data of 25 pairs of participants where one member of each pair was stimulated with a visual and an auditory 500 Hz signals of 1 second duration. The second dataset consisted of the data of 20 pairs of participants where one member of each pair received visual and auditory stimulation lasting 1 second duration with on-off modulation at 10, 12, and 14 Hz. Methods and Results: Applying a \u2018linear discriminant classifier\u2019 to the first dataset, it was possible to correctly classify 50.74% of the EEG activity of non-stimulated participants, correlated to the remote sensorial stimulation of the distant partner. In the second dataset, the percentage of correctly classified EEG activity in the non-stimulated partners was 51.17%, 50.45% and 51.91%, respectively, for the 10, 12, and 14 Hz stimulations, with respect the condition of no stimulation in the distant partner. Conclusions: The analysis of EEG activity using machine-learning algorithms has produced advances in the study of the connection between the EEG activities of the stimulated partner and the isolated distant partner, opening new insight into the possibility to devise practical application for non-conventional \u201cmental telecommunications\u201d between physically and sensorially separated participants

Brain-to-Brain interaction at a distance: a global or differential relationship?

<div>EEG, GW6 max correlation individual data related to the paper "<b>Brain-to-Brain interaction at a distance: a global or differential relationship?"</b></div><div><b><br></b></div><div><b>MatLab syntax (MachineLearningSyntax) and Extended data related to the paper "EEG correlation at a distance: a re-analysis of two studies using a machine learning approach"<br></b></div