title: Cortical potential imaging of brain electrical activity by means of parametric projection filter.
abstract: OBJECTIVE
  The objective of this study was to explore suitable spatial filters for inverse estimation of cortical potentials from the scalp electroencephalogram. The effect of incorporating noise covariance into inverse procedures was examined by computer simulations and tested in human experiment.
  
  
  METHODS
  The parametric projection filter, which allows inverse estimation with the presence of information on the noise, was applied to an inhomogeneous three-concentric-sphere model under various noise conditions in order to estimate the cortical potentials from the scalp potentials. The method for determining the optimum regularization parameter, which can be applied for parametric inverse techniques, is also discussed.
  
  
  RESULTS
  Human visual evoked potential experiment was carried out to examine the performance of the proposed restoration method. The parametric projection filter gave more localized inverse solution of cortical potential distribution than the truncated SVD and Tikhonov regularization.
  
  
  CONCLUSIONS
  The present simulation results suggest that incorporation of information on the noise covariance allows better estimation of cortical potentials, than inverse solutions without knowledge about the noise covariance, when the correlation between the signal and noise is low.
authors.affiliation: Department of Biocybernetics, Niigata University, 8050 Ikarashi-2, Niigata 950-2181, Japan. hori@bc.niigata-u.ac.jp
authors.forename: J
authors.initials: J
authors.lastname: Hori
authors.forename: J
authors.initials: J
authors.lastname: Lian
authors.forename: B
authors.initials: B
authors.lastname: He
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D004594:Electrophysiology; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D005240:Feasibility Studies; D006801:Humans; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted