Statistical reduction of EEG data

Author

Charles, Prophete J. ; Sun, Mingui ; Sclabassi, Robert J.

Author_Institution

Dept. of Bioeng., Pittsburgh Univ., PA, USA

Volume

2

fYear

2000

fDate

2000

Firstpage

1394

Abstract

Analyzing multi-channel EEG can be a daunting chore due to the volume of information that must be examined. For this reason, it is often more productive to reduce the data set by limiting the analysis to certain features or by selecting a subset of the data according to a certain criteria. One traditional method is to use frequency domain techniques to focus upon certain aspects of the data. However, when the frequency domain is used to analyze EEG data, the temporal domain features are lost. In this paper we examine an alternative method of data reduction and channel grouping using a statistical approach which retains time information

Keywords

electroencephalography; normal distribution; statistical analysis; EEG data statistical reduction; channel grouping; data set; data subset; frequency domain techniques; multi-channel EEG; statistical approach; temporal domain features; time information; Brain modeling; Concrete; Data analysis; Data mining; Electrodes; Electroencephalography; Frequency domain analysis; Laboratories; Surges; Time domain analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE

Conference_Location

Chicago, IL

ISSN

1094-687X

Print_ISBN

0-7803-6465-1

Type

conf

DOI

10.1109/IEMBS.2000.898000

Filename

898000