Characterization of Scale-Free Properties of Human Electrocorticography in Awake and Slow Wave Sleep States - Zempel, John M.; Politte, David G.; Kelsey, Matthew; Verner, Ryan; Nolan, Tracy S.; Babajani-Feremi, Abbas; Prior, Fred; Larson-Prior, Linda J. (Frontiers in Neurology, 2012)
Like many complex dynamic systems, the brain exhibits scale-free dynamics that follow power law scaling. Broadband power spectral density (PSD) of brain electrical activity exhibits state-dependent power law scaling with a log frequency exponent that varies across frequency ranges. Widely divergent naturally occurring neural states, awake and slow wave sleep (SWS) periods, were used evaluate the nature of changes in scale-free indices. We demonstrate two analytic approaches to characterizing electrocorticographic (ECoG) data obtained during Awake and SWS states. A data driven approach was used, characterizing all available frequency ranges. Using an Equal Error State Discriminator (EESD), a single frequency range did not best characterize state across data from all six subjects, though the ability to distinguish awake and SWS states in individual subjects was excellent. Multisegment piecewise linear fits were used to characterize scale-free slopes across the entire frequency range (0.2-200 Hz). These scale-free slopes differed between Awake and SWS states across subjects, particularly at frequencies below 10 Hz and showed little difference at frequencies above 70 Hz. A Multivariate Maximum Likelihood Analysis (MMLA) method using the multisegment slope indices successfully categorized ECoG data in most subjects, though individual variation was seen. The ECoG spectrum is not well characterized by a single linear fit across a defined set of frequencies, but is best described by a set of discrete linear fits across the full range of available frequencies. With increasing computational tractability, the use of scale-free slope values to characterize EEG data will have practical value in clinical and research EEG studies.