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 3 cm. 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 15 cm2-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-cm2-scale accurate 3-D functional cortical imaging modality.

High-resolution EEG source imaging of one-year-old children

“…Our recently developed SCALE approach allows estimation of skull conductivity using an iterative approach while also estimating the locations of brain EEG sources whose projections to the scalp (scalp maps) are used in the estimation [7]. For each subject, SCALE uses a FEM head model constructed from their MR head image and 10–30 near-dipolar scalp maps found by ICA decomposition of their high-density EEG data.…”

“…The SCALE algorithm iteratively improves the source and conductivity estimates; it can be summarized as follows [7]: Generate a FEM mesh and select a starting conductivity distribution: σ 0 .For each iteration i = 0, 1, 2, …, Calculate the FEM model at conductivity σ i .For each IC j = 1, 2, …, P Estimate source distribution s j for IC j .Compute estimated potentials Φ j for s j at σ i .Calculate the sensitivity matrix, S j .Find the change in conductivity distribution Δ σ j that maximizes the goodness of fit for IC j .Compute Δ σ i +1 as a weighted sum of Δ σ j .Update conductivity: σ i +1 = σ i + Δ σ i .Stop if Δ σ ≤ ε .…”

“…BESA provides generic age-based conductivity estimates using an exponential spline fit for children older than 3. We have previously proposed a skull conductivity estimation method using an iterative gradient-based approach, “Simultaneous tissue Conductivity And source Location Estimation” (SCALE) [7]. SCALE estimates head tissue conductivities and electrical brain source locations using a realistically shaped FEM head model and 10–30 near-dipolar sources found by ICA decomposition of sufficiently high-density EEG data.…”

Auditory steady state responses and cochlear implants: Modeling the artifact-response mixture in the perspective of denoising

“…Although more recent studies report varying values of this ratio mostly dependent on the method of measurement (see [47] for a review), we assume that these values will not drastically affect the simulations of this computational framework as a tool for comparing denoising algorithms. This ratio remains user defined in our implementation.…”

Data-driven forward model inference for EEG brain imaging

“…However, it is known that the skull:brain conductivity ratio in particular varies greatly between people, and additionally that a correct specification of this ratio is important for accurate EEG imaging [23,24]. Akalin Acar et al [25] suggest to optimize the skull:brain conductivity ratio based on the compactness and focality of the reconstructed sources. The technique, however, is reliant on the subject's MRI data.…”