Spatial filtering and neocortical dynamics: estimates of EEG coherence

The spatial statistics of scalp electroencephalogram (EEG) are usually presented as coherence in individual frequency bands. These coherences result both from correlations among neocortical sources and volume conduction through the tissues of the head. The scalp EEG is spatially low-pass filtered by the poorly conducting skull, introducing artificial correlation between the electrodes. A four concentric spheres (brain, CSF, skull, and scalp) model of the head and stochastic field theory are used here to derive an analytic estimate of the coherence at scalp electrodes due to volume conduction of uncorrelated source activity, predicting that electrodes within 10-12 cm can appear correlated. The surface Laplacian estimate of cortical surface potentials spatially bandpass filters the scalp potentials reducing this artificial coherence due to volume conduction. Examination of EEG data confirms that the coherence estimates from raw scalp potentials and Laplacians are sensitive to different spatial bandwidths and should be used in parallel in studies of neocortical dynamic function.

[1]  D. A. Driscoll,et al.  EEG electrode sensitivity--an application of reciprocity. , 1969, IEEE transactions on bio-medical engineering.

[2]  P. Nunez.  The brain wave equation: a model for the EEG , 1974 .

[3]  G Pfurtscheller,et al.  Frequency dependence of the transmission of the EEG from cortex to scalp. , 1975, Electroencephalography and clinical neurophysiology.

[4]  Yu-Kweng Michael Lin.  Probabilistic Theory of Structural Dynamics , 1976 .

[5]  Anthony Sances,et al.  The Contributions of Intracerebral Currents to the EEG and Evoked Potentials , 1978, IEEE Transactions on Biomedical Engineering.

[6]  D. Cohen,et al.  Comparison of the magnetoencephalogram and electroencephalogram. , 1979, Electroencephalography and clinical neurophysiology.

[7]  A. J. Hermans,et al.  A model of the spatial-temporal characteristics of the alpha rhythm. , 1982, Bulletin of mathematical biology.

[8]  J. Fermaglich.  Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[9]  O Bertrand,et al.  A theoretical justification of the average reference in topographic evoked potential studies. , 1985, Electroencephalography and clinical neurophysiology.

[10]  D. Tucker,et al.  Functional connections among cortical regions: topography of EEG coherence. , 1986, Electroencephalography and clinical neurophysiology.

[11]  R. Thatcher,et al.  Cortico-cortical associations and EEG coherence: a two-compartmental model. , 1986, Electroencephalography and clinical neurophysiology.

[12]  Cees J. Stok,et al.  The influence of model parameters on EEG/MEG single dipole source estimation , 1987, IEEE Transactions on Biomedical Engineering.

[13]  G Fein,et al.  Common reference coherence data are confounded by power and phase effects. , 1988, Electroencephalography and clinical neurophysiology.

[14]  F. Perrin,et al.  Spherical splines for scalp potential and current density mapping. , 1989, Electroencephalography and clinical neurophysiology.

[15]  Armin Fuchs,et al.  Spatio-Temporal EEG Patterns , 1991 .

[16]  G Fein,et al.  Artifactually high coherences result from using spherical spline computation of scalp current density. , 1991, Electroencephalography and clinical neurophysiology.

[17]  J.C. de Munck,et al.  A random dipole model for spontaneous brain activity , 1992, IEEE Transactions on Biomedical Engineering.

[18]  A. van Oosterom,et al.  Computation of the potential distribution in a four-layer anisotropic concentric spherical volume conductor , 1992, IEEE Transactions on Biomedical Engineering.

[19]  F. L. D. Silva,et al.  A Random Dipole Model for Spontaneous Brain , 1992 .

[20]  P. Nunez,et al.  High-resolution EEG using spline generated surface Laplacians on spherical and ellipsoidal surfaces , 1993, IEEE Transactions on Biomedical Engineering.

[21]  D. Tucker.  Spatial sampling of head electrical fields: the geodesic sensor net. , 1993, Electroencephalography and clinical neurophysiology.

[22]  P. Nunez,et al.  A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging. , 1994, Electroencephalography and clinical neurophysiology.

[23]  T. Bullock,et al.  EEG coherence has structure in the millimeter domain: subdural and hippocampal recordings from epileptic patients. , 1995, Electroencephalography and clinical neurophysiology.

[24]  L. Ingber.  Statistical mechanics of multiple scales of neocortical interactions , 1995 .

[25]  P. Nunez,et al.  Neocortical Dynamics and Human EEG Rhythms , 1995 .

[26]  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.

[27]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1987 .

[28]  R. B. Silberstein,et al.  Comparison of high resolution EEG methods having different theoretical bases , 2005, Brain Topography.

[29]  Richard B. Silberstein,et al.  Measurement processes and spatial principal components analysis , 2005, Brain Topography.