Linear and nonlinear current density reconstructions.
Minimum norm algorithms for EEG source reconstruction are studied in view of their spatial resolution, regularization, and lead-field normalization properties, and their computational efforts. Two classes of minimum norm solutions are examined: linear least squares methods and nonlinear L1-norm appr...
| Published in: | Journal of Clinical Neurophysiology Vol. 16; no. 3; pp. 267 - 296 |
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| Main Authors: | , , , , , , , |
| Format: | review Journal Article |
| Published: |
Lippincott Williams & Wilkins
May1999
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=112252772&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 112252772 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07360258 8CL jtl: Journal of Clinical Neurophysiology issn: 07360258 maglogo: N pubinfo: dt: May1999 vid: 16 iid: 3 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 112252772 112252772 NLM10426408 112252772 10.1097/00004691-199905000-00006 NLM10426408 112252772 ppf: 267 ppct: 29 formats: tig: atl: Linear and nonlinear current density reconstructions. aug: au: Fuchs, Manfred Wagner, Michael Köhler, Thomas Wischmann, Hans-Aloys Fuchs, M Wagner, M Köhler, T Wischmann, H A affil: Philips Research Laboratories Hamburg, Germany sug: subj: Epilepsy Diagnosis Magnetic Resonance Imaging Methods Electroencephalography Methods Linear Regression Algorithms Epilepsy Pathology Epilepsy Physiopathology Signal Processing, Computer Assisted Male Female Image Interpretation, Computer Assisted Chaos Theory Male Female ab: Minimum norm algorithms for EEG source reconstruction are studied in view of their spatial resolution, regularization, and lead-field normalization properties, and their computational efforts. Two classes of minimum norm solutions are examined: linear least squares methods and nonlinear L1-norm approaches. Two special cases of linear algorithms, the well known Minimum Norm Least Squares and an implementation with Laplacian smoothness constraints, are compared to two nonlinear algorithms comprising sparse and standard L1-norm methods. In a signal-to-noise-ratio framework, two of the methods allow automatic determination of the optimum regularization parameter. Compensation methods for the different depth dependencies of all approaches by lead-field normalization are discussed. Simulations with tangentially and radially oriented test dipoles at two different noise levels are performed to reveal and compare the properties of all approaches. Finally, cortically constrained versions of the algorithms are applied to two epileptic spike data sets and compared to results of single equivalent dipole fits and spatiotemporal source models. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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