An Arduino Giga-Based Multi-Frequency Electrical Impedance Tomography System for Electrolyte Media: Hardware Design, Statistical Voltage Analysis, and System Performance Evaluation

Abstract

Multi-frequency electrical impedance tomography (EIT) often requires specialized hardware. This challenge is especially pronounced in biomedical applications. We developed a 16-electrode EIT system using an Arduino GIGA R1 WiFi for generation and acquisition, with a discrete current source, three analog multiplexers, and basic signal conditioning. Dual cores separate waveform timing from switching and acquisition. A 3D-printed tank with silver-wire electrodes was tested at 11 frequencies (1-180 kHz). Across the 10-180 kHz analysis band, median repeat-frame signal-to-noise ratio was 48 dB, acquisition rate was ~0.18 frames/s per frequency (~60 s per sweep), and cross-session baseline drift was ~6%. KCl phantoms contained an insulating ABS cylinder or KCl-filled dialysis bags (0.001-1.0 M) spanning positive and negative conductivity contrasts. Boundary voltages were analyzed without reconstruction to estimate inclusion position, contrast polarity, and relative magnitude. Leave-one-session-out matching located ABS inclusions in 29/30 runs at 160 kHz (87-97% across the band; chance 33%). For dialysis bags, polarity was correctly identified in all 36 nonzero-contrast runs. Signed voltage projections correlated with the cylindrical contrast factor (R^2=0.68 across runs; R^2=0.89 for the aggregate fit). Concentration was ordered (Spearman rho=0.85), despite saturation above ~10-fold contrast. Bag position accuracy ranged from 6/6 at the highest contrasts to 2/6 at matched conductivity. The matched-conductivity bag produced a ~1.7% RMS perturbation against 0.4-0.6% repeat-frame variability. These results demonstrate that microcontroller-based multi-frequency EIT can support selected detection and identification tasks through direct boundary-voltage analysis, supporting future studies of muscle electrochemistry.