Adaptive segmentation of electroencephalographic data using a nonlinear energy operator

  • Agarwal R
  • Gotman J
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Analysis of long-term EEG requires that it be segmented into piece-wise stationary sections. This is accomplished by drawing boundaries at time instants of change in the amplitude or frequency content of the EEG. In this paper we describe a method of signal characterization that can be used to segment EEGs. This method is based on a nonlinear energy operator that inherently combines the amplitude and frequency content of the EEG. We show how the resulting frequency-weighted energy measure can be used for segmentation. By using synthetic and real data, the proposed method is compared to a popular segmentation method from the EEG literature. Enhanced sensitivity of the proposed method (particularly to the changes in frequency) are highlighted.

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  • ISSN: 02714310
  • PUI: 29852077
  • SGR: 0032655922
  • SCOPUS: 2-s2.0-0032655922


  • Rajeev Agarwal

  • Jean Gotman

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