skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Sensitive Measures of Condition Change in EEG Data

Abstract

We present a new, robust, model-independent technique for measuring condition change in nonlinear data. We define indicators of condition change by comparing distribution functions (DF) defined on the attractor for time windowed data sets via L{sub 1}-distance and {chi}{sup 2} statistics. The new measures are applied to EEG data with the objective of detecting the transition between non-seizure and epileptic brain activity in an accurate and timely manner. We find a clear superiority of the new metrics in comparison to traditional nonlinear measures as discriminators of condition change.

Authors:
; ;
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Research (ER) (US)
OSTI Identifier:
6563
Report Number(s):
ORNL/CP-102445; EB 50 03 00 0
EB 50 03 00 0; TRN: AH200116%%243
DOE Contract Number:  
AC05-96OR22464
Resource Type:
Conference
Resource Relation:
Conference: International Conference on Chaos in Brain, Bonn (DE), 03/10/1999--03/12/1999; Other Information: PBD: 10 Mar 1999
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; ELECTROENCEPHALOGRAPHY; BRAIN; DISTRIBUTION FUNCTIONS; STATISTICS; DATA ANALYSIS; EPILEPSY; BIOLOGICAL INDICATORS

Citation Formats

Hively, L M, Gailey, P C, and Protopopescu, V. Sensitive Measures of Condition Change in EEG Data. United States: N. p., 1999. Web.
Hively, L M, Gailey, P C, & Protopopescu, V. Sensitive Measures of Condition Change in EEG Data. United States.
Hively, L M, Gailey, P C, and Protopopescu, V. 1999. "Sensitive Measures of Condition Change in EEG Data". United States. https://www.osti.gov/servlets/purl/6563.
@article{osti_6563,
title = {Sensitive Measures of Condition Change in EEG Data},
author = {Hively, L M and Gailey, P C and Protopopescu, V},
abstractNote = {We present a new, robust, model-independent technique for measuring condition change in nonlinear data. We define indicators of condition change by comparing distribution functions (DF) defined on the attractor for time windowed data sets via L{sub 1}-distance and {chi}{sup 2} statistics. The new measures are applied to EEG data with the objective of detecting the transition between non-seizure and epileptic brain activity in an accurate and timely manner. We find a clear superiority of the new metrics in comparison to traditional nonlinear measures as discriminators of condition change.},
doi = {},
url = {https://www.osti.gov/biblio/6563}, journal = {},
number = ,
volume = ,
place = {United States},
year = {1999},
month = {3}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: