Optimal Design for Parameter Estimation in EEG Problems in a 3D Multilayered Domain

Abstract

The fundamental problem of collecting data in the "best way" in order to assure statistically efficient estimation of parameters is known as Optimal Experimental Design. Many inverse problems consist in selecting best parameter values of a given mathematical model based on fits to measured data. These are usually formulated as optimization problems and the accuracy of their solutions depends not only on the chosen optimization scheme but also on the given data. We consider an electromagnetic interrogation problem, specifically one arising in an electroencephalography (EEG) problem, of finding optimal number and locations for sensors for source identification in a 3D unit sphere from data on its boundary. In this effort we compare the use of the classical D-optimal criterion for observation points as opposed to that for a uniform observation mesh. We consider location and best number of sensors and report results based on statistical uncertainty analysis of the resulting estimated parameters.

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Document Details

Document Type
Technical Report
Publication Date
Mar 30, 2014
Accession Number
ADA599689

Entities

People

  • D. Rubio
  • H. Thomas Banks
  • M. I. Troparevsky
  • N. Saintier

Organizations

  • North Carolina State University

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Boundaries
  • Cartesian Coordinates
  • Computational Science
  • Data Science
  • Design Criteria
  • Differential Equations
  • Enzyme Kinetics
  • Equations
  • Experimental Design
  • Grids
  • Information Science
  • Inverse Problems
  • Mathematical Models
  • Models
  • Observation
  • Voltage

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Regression Analysis.
  • Structural Dynamics.