Performances of surface Laplacian estimators: A study of simulated and real scalp potential distributions

F. Babiloni, C. Babiloni, L. Fattorini, F. Carducci, P. Onorati, A. Urbano

Research output: Contribution to journalArticle

Abstract

This paper presents a study of the performance of various local and spherical spline methods currently in use for the surface Laplacian (SL) estimate of scalp potential distributions. The SL was estimated from simulated instantaneous event-related scalp potentials generated over a three-shell spherical head model. Laplacian estimators used planar and spherical scalp models. Noise of increasing magnitude and spatial frequency was added to the potential distributions in order to simulate noise presumed to contaminate scalp-recorded event-related potentials. A comparison of noise effects on various Laplacian estimates was made for increasing number of "electrode" positions in variants of the 10-20 system. Furthermore, to evaluate the error due to the use of unrealistic scalp models, the matching between SL estimates of human scalp-recorded movement-related potentials computed on spherical and realistically-shaped MRI-constructed models of the scalp was examined. With all methods the error of the SL estimate increased proportionally with the magnitude and spatial frequency of noise. Increased number of "electrodes" up to 256 significantly reduced the error (p

Original languageEnglish
Pages (from-to)35-45
Number of pages11
JournalBrain Topography
Volume8
Issue number1
DOIs
Publication statusPublished - Sep 1995

Keywords

  • Event-related potentials
  • Noise effects
  • Simulated scalp potential distributions
  • Spatial sampling
  • Surface Laplacian estimators
  • Unrealistic and realistic MRI-based scalp models

ASJC Scopus subject areas

  • Neuroscience(all)
  • Clinical Neurology

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    Babiloni, F., Babiloni, C., Fattorini, L., Carducci, F., Onorati, P., & Urbano, A. (1995). Performances of surface Laplacian estimators: A study of simulated and real scalp potential distributions. Brain Topography, 8(1), 35-45. https://doi.org/10.1007/BF01187668