DocumentCode
1396438
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
Volume
45
Issue
7
fYear
1998
fDate
7/1/1998 12:00:00 AM
Firstpage
814
Lastpage
826
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;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/10.686789
Filename
686789
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