title: Simultaneous head tissue conductivity and EEG source location estimation.
abstract: Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15cm(2)-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32.6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm(2)-scale accurate 3-D functional cortical imaging modality.
authors: Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA 92093-0559, USA. Electronic address: zeynep@sccn.ucsd.edu.
authors: Zeynep
authors: Z
authors: Akalin Acar
authors: Qualcomm Technologies, Inc., 5775 Morehouse Drive, San Diego, CA 92121, USA. Electronic address: canacar@canacar.net.
authors: Can E
authors: CE
authors: Acar
authors: Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA 92093-0559, USA. Electronic address: smakeig@ucsd.edu.
authors: Scott
authors: S
authors: Makeig
mesh_terms: D000328:Adult; D000465:Algorithms; D002540:Cerebral Cortex / Q000502:physiology*; D003627:Data Interpretation, Statistical; D004553:Electric Conductivity; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008959:Models, Neurological; D012535:Scalp / Q000502:physiology*; D012886:Skull / Q000502:physiology*; D055815:Young Adult
keywords: EEG; FEM; Finite Element Method; Four-layer realistic head modeling; Sensitivity of EEG to skull conductivity; Skull conductivity estimation; Source localization