title: Source modeling of ElectroCorticoGraphy (ECoG) data: Stability analysis and spatial filtering.
abstract: BACKGROUND
  Electrocorticography (ECoG) measures the distribution of the electrical potentials on the cortex produced by the neural currents. A full interpretation of ECoG data requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of the neural currents. This study addresses the ECoG source modeling developing a beamformer method.
  
  
  METHODS
  We computed the lead-field matrix by using a novel routine provided by the OpenMEEG software. We performed an analysis of the numerical stability of the ECoG inverse problem by computing the condition number of the lead-field matrix for different configurations of the electrodes grid. We applied a Linear Constraint Minimum Variance (LCMV) beamformer to both synthetic data and a set of real measurements recorded during a rapid visual categorization task.
  
  
  RESULTS
  For all considered grids the condition number indicates that the ECoG inverse problem is mildly ill-conditioned. For realistic SNR we found a good performance of the LCMV algorithm for both localization and waveforms reconstruction.
  
  
  CONCLUSIONS
  The flow of information reconstructed by analyzing real data seems consistent with both invasive monkey electrophysiology studies and non-invasive (MEG and fMRI) human studies.
  
  
  CONCLUSIONS
  Despite a growing interest from the neuroscientific community, solving the ECoG inverse problem has not quite yet reached the level of systematicity found for EEG and MEG. Starting from an analysis of the numerical stability of the problem we considered the most widely utilized method for modeling neurophysiological data based on the beamformer method in the hope to establish benchmarks for future studies.
authors.affiliation: CNR - IAC, Roma, Italy. Electronic address: a.pascarella@iac.cnr.it.
authors.forename: A
authors.initials: A
authors.lastname: Pascarella
authors.affiliation: Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University "G. d'Annunzio" of Chieti-Pescara, Italy.
authors.forename: C
authors.initials: C
authors.lastname: Todaro
authors.affiliation: INRIA Sophia Antipolis - Mediterranee, France.
authors.forename: M
authors.initials: M
authors.lastname: Clerc
authors.affiliation: Brown University, USA.
authors.forename: T
authors.initials: T
authors.lastname: Serre
authors.affiliation: Department of Mathematics, University of Genova, Italy; CNR - SPIN, Genova, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Piana
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000502:physiology*; D003198:Computer Simulation; D000069280:Electrocorticography; D006801:Humans; D008959:Models, Neurological; D010775:Photic Stimulation; D021641:Recognition, Psychology; D013997:Time Factors
keywords: Beamforming; Electrocorticography (ECoG); Inverse problems; Source modeling