title: Multichannel wavelet-type decomposition of evoked potentials: model-based recognition of generator activity.
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.
authors.affiliation: Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
authors.forename: A B
authors.initials: AB
authors.lastname: Geva
authors.forename: H
authors.initials: H
authors.lastname: Pratt
authors.forename: Y Y
authors.initials: YY
authors.lastname: Zeevi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D016057:Evoked Potentials, Auditory, Brain Stem; D006801:Humans; D008959:Models, Neurological; D010363:Pattern Recognition, Automated