title: Detecting millisecond-range coupling delays between brainwaves in terms of power correlations by magnetoencephalography.
abstract: BACKGROUND
  The spatiotemporal coupling of brainwaves is commonly quantified using the amplitude or phase of signals measured by electro- or magnetoencephalography (EEG/MEG). To enhance the temporal resolution for coupling delays down to millisecond level, a new power correlation (PC) method is proposed and tested.
  
  
  METHODS
  The cross-correlations of any two brainwave powers at two locations are calculated sequentially through a measurement using the convolution theorem. For noise suppression, the cross-correlation series is moving-average filtered, preserving the millisecond resolution in the cross-correlations, but with reduced noise. The coupling delays are determined from the delays of the cross-correlation peaks.
  
  
  RESULTS
  Simulations showed that the new method detects reliably power cross-correlations with millisecond accuracy. Moreover, in MEG measurements on three healthy volunteers, the method showed average alpha-alpha coupling delays of around 0-20 ms between the occipital areas of two hemispheres. Lower-frequency brainwaves vs. alpha waves tended to have a larger lag; higher-frequency waves vs. alpha waves showed delays with large deviations.
  
  
  METHODS
  The use of signal power instead of its square root (amplitude) in the cross-correlations improves noise cancellation. Compared to signal phase, the signal power analysis time delays do not have periodic ambiguity. In addition, the novel method allows fast calculation of cross-correlations.
  
  
  CONCLUSIONS
  The PC method conveys novel information about brainwave dynamics. The method may be extended from sensor-space to source-space analysis, and can be applied also for electroencephalography (EEG) and local field potentials (LFP).
authors: Department of Biomedical Engineering and Computational Science (BECS), Aalto University School of Science, P.O. Box 12200, FI-00076 AALTO, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Central Hospital, P.O. Box 340, FI-00029 HUS, Finland. Electronic address: juhani.dabek@aalto.fi.
authors: Juhani
authors: J
authors: Dabek
authors: Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité - University Medicine Berlin, Germany; Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia.
authors: Vadim V
authors: VV
authors: Nikulin
authors: Department of Biomedical Engineering and Computational Science (BECS), Aalto University School of Science, P.O. Box 12200, FI-00076 AALTO, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Central Hospital, P.O. Box 340, FI-00029 HUS, Finland.
authors: Risto J
authors: RJ
authors: Ilmoniemi
mesh_terms: D000465:Algorithms; D000513:Alpha Rhythm / Q000502:physiology; D001921:Brain / Q000502:physiology*; D058256:Brain Waves / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008959:Models, Neurological; D012146:Rest; D012815:Signal Processing, Computer-Assisted
keywords: Brainwave; Coupling; MEG; Millisecond resolution; Power correlation