Evoked potentials (EP) are measured in time-locked synchronization with repetitions of the same stimulus. The electrical measure in raw form is extremely noisy, reflecting not only responses to the imposed stimulus but also a large amount of normal, but unrelated activity. In the raw data no structure related to the stimulus is apparent, so the process is repeated many times, yielding multiple epochs that can be averaged. Such “signal averaging” reduces or washes out random fluctuations while structured variation linked to the stimulus builds up over multiple samples. The resulting pattern usually shows a large excursion preceded and followed by smaller deviations with a typical time-course relative to the stimulus.
The Global Consciousness Project (GCP) maintains a network of random number generators (RNG) running constantly at about 60 locations around the world, sending streams of 200-bit trials generated each second to be archived as parallel random sequences. Standard processing for most analyses computes a network variance measure for each second across the parallel data streams. This is the raw data used to calculate a figure of merit for each formal test of the GCP hypothesis: we predict non-random structure in data taken during “global events” that engage the attention of large numbers of people. The data are combined across all seconds of the event to give a representative Z-score, and typically displayed graphically as a cumulative deviation from expectation showing the history of the data sequence. For the present work, we treat the raw data in the same way measured electrical potentials from the brain are processed to reveal temporal patterns. In both cases the signal to noise ratio is very small, requiring signal averaging to reveal structure in what otherwise appears to be random data.
Applying this model to analyze GCP data from events that show significant departures from expectation, we find patterns that look like those found in EP work. While this assessment is limited to visual comparisons, the degree of similarity is striking. It suggests that human brain activity in response to stimuli may be a useful model to guide further research addressing th
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