@Article{RSM:Lew2009a,
  author =       "S. Lew and C.H. Wolters and A. Anwander and S. Makeig and
                 R.S. Macleod",
  title =        "Improved {EEG} source analysis using low-resolution
                 conductivity estimation in a four-compartment finite
                 element head model.",
  journal =      "Hum Brain Mapp",
  volume =       "30",
  number =       "9",
  pages =        "2862-2878",
  year =         "2009",
  month =        "Dec",
  robnote =      "We propose a low-resolution conductivity
                 estimation (LRCE) method using simulated annealing
                 optimization on high-resolution finite element models that
                 individually optimizes a realistically shaped four-layer
                 volume conductor with regard to the brain and skull
                 compartment conductivities. As input data, the method
                 needs T1- and PD-weighted magnetic resonance images for an
                 improved modeling of the skull and the cerebrospinal fluid
                 compartment and evoked potential data with high
                 signal-to-noise ratio (SNR). Our simulation studies showed
                 that for EEG data with realistic SNR, the LRCE method was
                 able to simultaneously reconstruct both the brain and the
                 skull conductivity together with the underlying dipole
                 source and provided an improved source analysis result. We
                 have also demonstrated the feasibility and applicability
                 of the new method to simultaneously estimate brain and
                 skull conductivity and a somatosensory source from
                 measured tactile somatosensory-evoked potentials of a
                 human subject. Our results show the viability of an
                 approach that computes its own conductivity values and
                 thus reduces the dependence on assigning values from the
                 literature and likely produces a more robust estimate of
                 current sources. Using the LRCE method, the individually
                 optimized four-compartment volume conductor model can, in
                 a second step, be used for the analysis of clinical or
                 cognitive data acquired from the same subject. Hum Brain
                 Mapp, 2009. (c) 2008 Wiley-Liss, Inc.",
  note   =       "http://dx.doi.org/10.1002/hbm.20714",
  bibdate =      "Sun Apr 26 13:45:54 2009",
}

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As input data, the method needs T1- and PD-weighted magnetic resonance images for an improved modeling of the skull and the cerebrospinal fluid compartment and evoked potential data with high signal-to-noise ratio (SNR). Our simulation studies showed that for EEG data with realistic SNR, the LRCE method was able to simultaneously reconstruct both the brain and the skull conductivity together with the underlying dipole source and provided an improved source analysis result. We have also demonstrated the feasibility and applicability of the new method to simultaneously estimate brain and skull conductivity and a somatosensory source from measured tactile somatosensory-evoked potentials of a human subject. Our results show the viability of an approach that computes its own conductivity values and thus reduces the dependence on assigning values from the literature and likely produces a more robust estimate of current sources. Using the LRCE method, the individually optimized four-compartment volume conductor model can, in a second step, be used for the analysis of clinical or cognitive data acquired from the same subject. Hum Brain Mapp, 2009. (c) 2008 Wiley-Liss, Inc.","note":"http://dx.doi.org/10.1002/hbm.20714","bibdate":"Sun Apr 26 13:45:54 2009","bibtex":"@Article{RSM:Lew2009a,\n author = \"S. Lew and C.H. Wolters and A. Anwander and S. Makeig and\n R.S. Macleod\",\n title = \"Improved {EEG} source analysis using low-resolution\n conductivity estimation in a four-compartment finite\n element head model.\",\n journal = \"Hum Brain Mapp\",\n volume = \"30\",\n number = \"9\",\n pages = \"2862-2878\",\n year = \"2009\",\n month = \"Dec\",\n robnote = \"We propose a low-resolution conductivity\n estimation (LRCE) method using simulated annealing\n optimization on high-resolution finite element models that\n individually optimizes a realistically shaped four-layer\n volume conductor with regard to the brain and skull\n compartment conductivities. As input data, the method\n needs T1- and PD-weighted magnetic resonance images for an\n improved modeling of the skull and the cerebrospinal fluid\n compartment and evoked potential data with high\n signal-to-noise ratio (SNR). Our simulation studies showed\n that for EEG data with realistic SNR, the LRCE method was\n able to simultaneously reconstruct both the brain and the\n skull conductivity together with the underlying dipole\n source and provided an improved source analysis result. We\n have also demonstrated the feasibility and applicability\n of the new method to simultaneously estimate brain and\n skull conductivity and a somatosensory source from\n measured tactile somatosensory-evoked potentials of a\n human subject. Our results show the viability of an\n approach that computes its own conductivity values and\n thus reduces the dependence on assigning values from the\n literature and likely produces a more robust estimate of\n current sources. Using the LRCE method, the individually\n optimized four-compartment volume conductor model can, in\n a second step, be used for the analysis of clinical or\n cognitive data acquired from the same subject. Hum Brain\n Mapp, 2009. (c) 2008 Wiley-Liss, Inc.\",\n note = \"http://dx.doi.org/10.1002/hbm.20714\",\n bibdate = \"Sun Apr 26 13:45:54 2009\",\n}\n\n","author_short":["Lew, S.","Wolters, C.","Anwander, A.","Makeig, S.","Macleod, R."],"key":"RSM:Lew2009a","id":"RSM:Lew2009a","bibbaseid":"lew-wolters-anwander-makeig-macleod-improvedeegsourceanalysisusinglowresolutionconductivityestimationinafourcompartmentfiniteelementheadmodel-2009","role":"author","urls":{},"metadata":{"authorlinks":{}},"downloads":0,"html":""},"search_terms":["improved","eeg","source","analysis","using","low","resolution","conductivity","estimation","four","compartment","finite","element","head","model","lew","wolters","anwander","makeig","macleod"],"keywords":[],"authorIDs":[],"dataSources":["5HG3Kp8zRwDd7FotB","5G2skx26SJtreWr4m"]}