Abstract
Scalp recording of electrical events allows 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 auxiliary physiological knowledge about the spatio-temporal characteristics of the generators’ activity. The widely-used model to describe the evoked potentials’ sources is a set of current dipoles. It does not include a temporal model of source activity and does not propose a solution of the number of sources that are active simultaneously nor how to differentiate their contributions.
In our multichannel wavelet-type decomposition, scalp recorded signals are decomposed into a combination of physiologically-based wavelets. The coherent activity of a population of neurons may be derived by convolving a single cell’s electrical contribution with the population’s Gaussian temporal distribution of activity. Thus, we chose the Hermite Functions (derived from the Gaussian function to form mono-, bi- and tri-phasic waveforms) as the mathematical model to describe the temporal pattern of mass neural activity.
For each wavelet we solve the inverse problem for two symmerically positioned and oriented dipoles, one of which attains zero magnitude when a single source is more suitable. We use the wavelet to model the temporal activity pattern of the symmetrical dipoles. By this we reduce the dimension of inverse problem and find a plausible solution. Once the number and the initial parameters of the sources are given, we can apply multiple source estimation to correct the solution for generators with overlapping activity.
Application of the procedure to subcortical and cortical components of short-latency visual evoked potentials (SVEP) in response to high-intensity, strobe flashes, demonstrates its feasibility.
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Geva, A.B., Pratt, H., Zeevi, Y.Y. (1996). Spatio-Temporal Source Estimation of Evoked Potentials by Wavelet-Type Decomposition. In: Gath, I., Inbar, G.F. (eds) Advances in Processing and Pattern Analysis of Biological Signals. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9098-6_8
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DOI: https://doi.org/10.1007/978-1-4757-9098-6_8
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