Source localization and bioelectric imaging as it relates to continuous waveform analysis.
One of the goals of the mathematical analysis of scalp-recorded continuous EEG waveforms is to elucidate non-invasively the neural generators of these voltages. One way of accomplishing this is to simulate these generators by equivalent current sources and follow the apparent motion of these theoretical generators during the temporal evolution of the EEG. Another way of accomplishing this is to follow the changes in scalp or simulated cortical surface potential or Laplacian maps during the temporal evolution of the EEG. We first discuss the possible theoretical pitfalls of using linear techniques on an essentially nonlinear problem (the localization of the sources of the EEG), as well as possible computational pitfalls associated with realistic, but complex, conductive medium models simulating the head. Various mathematical source localization methods and bioelectric imaging techniques are then outlined. Later in this paper a collaborative project involving mathematicians, computer scientists, and clinical neuroscientists is described. In this project EEG waveform data will be analyzed, millisecond-to-millisecond, using the various mathematical techniques for localizing equivalent current sources and simulating cortical surface potential and Laplacian topographical maps mentioned above. One of the clinical aims of this research project is to localize epileptic foci without employing invasive recording procedures. Since the EEG datasets that will be analyzed are large (22 Mb, in some cases), data compression and parallel processing strategies will be important parts of this research project. Such strategies, as they may apply to continuous waveform analysis, are discussed in the two appendices at the end of this paper. Many of the presentations at the Symposium and the corresponding papers in this Supplement are related to the ideas and goals of this research project and the implementation of the mathematical/computational techniques for realizing these goals.
Sidman
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Department of Mathematics, University of Southwestern Louisiana, Lafayette 70504-1010, USA. rds7637@usl.edu
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D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D004569:Electroencephalography; D006801:Humans; D008959:Models, Neurological