Measures of linear and nonlinear interdependence of electrocortigram time series from evoked-response potential experiments

In this brief discussion, we consider various coupling measures applied to electrocortigram (ECoG) data. The analysis consists of both linear and nonlinear measures of coupling - or interdependence - between two ensembles of measurements collected at two electrodes in an evoked-response potential (ERP) experiment. The interdependence measures are applied to simulated time series data and experimental ECoG data. The algorithms discussed here are implemented in the interactive data language (IDL) and available for download from the authors.

[1]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[2]  C. Stam,et al.  Nonlinear synchronization in EEG and whole‐head MEG recordings of healthy subjects , 2003, Human brain mapping.

[3]  P. Grassberger,et al.  A robust method for detecting interdependences: application to intracranially recorded EEG , 1999, chao-dyn/9907013.

[4]  L. Tsimring,et al.  Generalized synchronization of chaos in directionally coupled chaotic systems. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[5]  Kurths,et al.  Phase synchronization of chaotic oscillators. , 1996, Physical review letters.

[6]  Carroll,et al.  Synchronization in chaotic systems. , 1990, Physical review letters.

[7]  M. Breakspear.  Nonlinear phase desynchronization in human electroencephalographic data , 2002, Human brain mapping.

[8]  R Quian Quiroga,et al.  Performance of different synchronization measures in real data: a case study on electroencephalographic signals. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  P. König,et al.  Top-down processing mediated by interareal synchronization. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Joydeep Bhattacharya,et al.  Effective detection of coupling in short and noisy bivariate data , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[12]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[13]  D. Tucker,et al.  EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. , 1997, Electroencephalography and clinical neurophysiology.