Sensitive Measures of Condition Change in EEG Data

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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.

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4 p.

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Hively, L.M.; Gailey, P.C. & Protopopescu, V. March 10, 1999.

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Description

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.

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4 p.

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OSTI as DE00006563

Medium: P; Size: 4 pages

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  • International Conference on Chaos in Brain, Bonn (DE), 03/10/1999--03/12/1999

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  • Report No.: ORNL/CP-102445
  • Report No.: EB 50 03 00 0
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 6563
  • Archival Resource Key: ark:/67531/metadc710530

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Office of Scientific & Technical Information Technical Reports

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  • March 10, 1999

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • April 10, 2017, 8:09 p.m.

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Hively, L.M.; Gailey, P.C. & Protopopescu, V. Sensitive Measures of Condition Change in EEG Data, article, March 10, 1999; Tennessee. (digital.library.unt.edu/ark:/67531/metadc710530/: accessed September 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.