DocumentCode
3308017
Title
Non-Gaussian modeling of EEG data
Author
Charles, Prophete J. ; Sclabassi, Robert J. ; Sun, Mingui
Author_Institution
Lab. for Comput. Neurosci., Pittsburgh Univ., PA, USA
Volume
2
fYear
1999
fDate
36434
Abstract
It is common when modeling EEG data to employ an assumption of normality. While this assumption usually provides a modest approximation for random variables, its use for EEG data is limited. In this paper we examine why general modeling of EEG data as normal is inadequate and provide an example of approximating various stages of a seizure with non-normal distributions
Keywords
Gaussian distribution; brain models; electroencephalography; probability; EEG data; nonGaussian modeling; nonnormal distributions; normality; seizure; Brain modeling; Electroencephalography; Frequency; Gaussian distribution; Histograms; Maximum likelihood estimation; Random variables; Robustness; Surges; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint
Conference_Location
Atlanta, GA
ISSN
1094-687X
Print_ISBN
0-7803-5674-8
Type
conf
DOI
10.1109/IEMBS.1999.804176
Filename
804176
Link To Document