Title :
Testing EEG data for statistical normality
Author_Institution :
US Naval Biodynamics Lab., New Orleans, LA, USA
Abstract :
A modification of the Kolmogorov-Smirnov (KS) statistical test for normally distributed time series data when these data are highly correlated and EEG-like is described. Analysis of EEG data using the KS statistics or any other statistic that needs independent data requires reduction of the effective sampling rate by discarding data. Here an estimate of the correlation properties of the data is used to correct the test statistic, allowing all the data to be used as originally sampled. This increases the power of the test. Results obtained when the KS procedure was applied to several samples of EEG data obtained under various experimental conditions are reported. They demonstrate the usefulness of the modified method as a test for normality of correlated EEG data with strong peaks in the lower part of the spectrum
Keywords :
electroencephalography; statistical analysis; EEG data testing; Kolmogorov-Smirnov statistical test; experimental conditions; normally distributed time series data; Analytical models; Brain modeling; Computational modeling; Computer simulation; Data analysis; Electroencephalography; Statistical analysis; Statistics; Testing; Time series analysis;
Conference_Titel :
Engineering in Medicine and Biology Society, 1989. Images of the Twenty-First Century., Proceedings of the Annual International Conference of the IEEE Engineering in
Conference_Location :
Seattle, WA
DOI :
10.1109/IEMBS.1989.95942
