Signal averaging can reveal patterns in noisy data from repeated-measures experimental designs. A widely known example is brain activity in response to either endogenous or exogenous stimuli such as decisions, visual patterns, or auditory bursts of sound. A commonly used technology is EEG or other monitoring of brain electrical potentials. Evoked potentials (EP) are measured in time-locked synchronization with repetitions of a 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 measurement 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. A typical pattern may show a large excursion preceded and followed by smaller deviations with a typical time-course relative to the stimulus.

The Global Consciousness Project (GCP) network generates trials once per second at about 60 locations around the world, sending synchronized sequences of data to a central archive. Our standard analysis computes a network variance measure for each second across the parallel data streams. From this a Z-score figure of merit is calculated 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 typically displayed as a cumulative deviation trace, but for the present work, we treat the raw data in the same way measured electrical potentials from the brain are processed. Signal averaging is used to reveal temporal patterns or 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 exploratory 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 research designed to observe responses of a global consciousness to events in the world.

Bio: Roger Nelson, PhD, is an experimental psychologist and conceptual artist. He joined Princeton University’s PEAR lab in 1980, and in 1997 he founded the Global Consciousness Project (GCP) to study the active presence of consciousness in the world. An SSE member since its inception, he lives in Princeton, NJ, USA.

Recorded at the Society for Scientific Exploration Conference in Broomfield, Colorado 2019.

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Published on November 29, 2019