Title :
Spatial filtering and neocortical dynamics: estimates of EEG coherence
Author :
Srinivasan, Ramesh ; Nunez, Paul L. ; Silberstein, Richard B.
Author_Institution :
Electr. Geodesics Inc., Eugene, OR, USA
fDate :
7/1/1998 12:00:00 AM
Abstract :
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.
Keywords :
electroencephalography; medical signal processing; spatial filters; stochastic processes; surface potential; EEG coherence; artificial correlation; coherence estimates; concentric spheres; cortical surface potentials; head; individual frequency bands; neocortical dynamic function; neocortical sources; poorly conducting skull; raw scalp potentials; scalp electroencephalogram; spatial bandwidths; spatial filtering; spatial statistics; stochastic field theory; surface Laplacian estimate; tissues; uncorrelated source activity; volume conduction; Coherence; Electrodes; Electroencephalography; Filtering; Frequency; Laplace equations; Low pass filters; Scalp; Skull; Statistics; Algorithms; Cerebral Cortex; Electrodes; Electroencephalography; Humans; Models, Neurological; Signal Processing, Computer-Assisted; Stochastic Processes;
Journal_Title :
Biomedical Engineering, IEEE Transactions on