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An heuristic signal processing scheme recently introduced shows how brainsignals can be efficiently represented by a sparse spatiotemporal pointprocess. The approach has been validated already for different relevantconditions demonstrating that preserves and compress a surprisingly largefraction of the signal information. In this paper the conditions for suchcompression to succeed are investigated as well as the underlying reasons forsuch good performance. The results show that the key lies in the correlationproperties of the time series under consideration. It is found that signalswith long range correlations are particularly suitable for this type ofcompression, where inflection points contain most of the information. Sincethis type of correlation is ubiquitous in signals trough out nature includingmusic, weather patterns, biological signals, etc., we expect that this type ofapproach to be an useful tool for their analysis.

On why a few points suffice to describe spatiotemporal large-scale brain dynamics