Hurst parameter as a correlation measure for brain signal

Correlation in brain is generally considered an evidence of physical connectivity of the neuron network. However, the way neuron clusters cooperate varies from mental function to function. In this work, we used Hurst parameter as a measure of brain signal correlation and analyzed the EEG data made fungible available by USC. We found striking results showing significant self-similarity in the analyzed EEG signal in the form of high values for the Hurst's parameter. This raises the question whether neurons communicate using common control signals very similar to the control protocols in Internet devices, also resulting in high self-similarity.