Multichannel wavelet-type decomposition of evoked potentials: Model-based recognition of generator activity

A. B. Geva, H. Pratt, Y. Y. Zeevi

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Scalp recording of electrical events allows the evaluation of human cerebral function, but contributions of the. specific brain structures generating the recorded activity are ambiguous. This problem is ill-posed and cannot be solved without physiological constraints based on the spatio-temporal characteristics of the generators' activity. In our model-based analysis of evoked potentials for the purpose of generator activity detection, multichannel scalp-recorded signals are decomposed into a combination of wavelets, each of which can describe the neural mass coherent activity of cell assemblies. Elimination of contributions of specific generators and/or distributed background activity can produce physiologically motivated time-frequency filtering. The decomposition and filtering procedures are demonstrated by three examples: simulation of the surface manifestation of known intracranial generators; decomposition and reconstruction of auditory brainstem evoked potentials which reflect the differences among generators of these potentials; and cognitive components of evoked potentials which are diminished in the averaged recording but are clearly detected in single-trial signals.

Original languageEnglish
Pages (from-to)40-46
Number of pages7
JournalMedical and Biological Engineering and Computing
Volume35
Issue number1
DOIs
StatePublished - 1 Jan 1997

Keywords

  • Bioelectric inverse problem
  • Evoked potential source estimation
  • Matching pursuit
  • Model-based pattern recognition
  • Wavelet-type decomposition

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