lower spatial accuracy than those of the NOSER images. However, the conductivity values computed by the D-bar method were more accurate in value and dynamic range. In \[[@Murphy2009]\] the effects of errors in input currents, output voltages, electrode placement, and domain shape modeling in the D-bar method for real-valued conductivities were studied on simulated data and the method was demonstrated to be quite robust. [[Additionally, the D-bar reconstruction methods for anisotropic conductivities, which uniquely recover $\sqrt{\det\sigma}$ up to a change of coordinates, are identical to their isotropic counterparts \[[@Henkin2010], [@Hamilton2014a]\]. As incorrect domain modeling is a known source of anisotropy, leading to EIT data that would only arise from an anisotropic conductivity even if the true conductivity is isotropic, these results help explain the robustness observed for D-bar based reconstruction methods for EIT.]{}]{} This paper contains the first study of the effects of errors in electrode placement and domain shape modeling when using experimental data with D-bar methods and handles permittivity, as well as conductivity, imaging.

The paper is organized as follows. Section \[sec:methods\] provides overviews of the equations in the D-bar methods studied here with computational details of the implementation of the D-bar method for complex conductivities in \[[@Herrera2015]\] 