A Cost-Effective 3D-Printed Conductive Phantom for EEG Sensing System Validation: Development, Performance Evaluation, and Comparison with State-of-the-Art Technologies

Abstract

This paper presents the development and validation of a cost-effective 3D-printed conductive phantom for EEG sensing system validation that achieves 85% cost reduction (£48.10 vs. £300-£500) and 48-hour fabrication time while providing consistent electrical properties suitable for standardized electrode testing. The phantom was fabricated using conductive PLA filament in a two-component design with a conductive upper section and a non-conductive base for structural support. Comprehensive validation employed three complementary approaches: DC resistance measurements (821-1502 Ω), complex impedance spectroscopy at 100 Hz across anatomical regions (3.01-6.4 kΩ with capacitive behavior), and 8-channel EEG system testing (5-11 kΩ impedance range). The electrical characterization revealed spatial heterogeneity and consistent electrical properties suitable for comparative electrode evaluation and EEG sensing system validation applications. To establish context, we analyzed six existing phantom technologies including commercial injection-molded phantoms, saline solutions, hydrogels, silicone models, textile-based alternatives, and multi-material implementations. This analysis identifies critical accessibility barriers in current technologies, particularly cost constraints (£5000-20,000 tooling) and extended production timelines that limit widespread adoption. The validated 3D-printed phantom addresses these limitations while providing appropriate electrical properties for standardized EEG electrode testing. The demonstrated compatibility with clinical EEG acquisition systems establishes the phantom's suitability for electrode performance evaluation and multi-channel system validation as a standardized testing platform, ultimately contributing to democratized access to EEG sensing system validation capabilities for broader research communities.

Keywords: 3D printing; additive manufacturing; conductive materials; electrode testing; electroencephalography; neuromonitoring sensors; neurophysiological signal acquisition; phantom head; sensing system validation; standardized testing.

Figure 1

3D model of the conductive head phantom showing (a) side view and (b) front profile. (c) Component separation showing conductive upper section and non-conductive base.

Figure 2

Fabricated phantom head showing (a) conductive head on top of non-conductive base and (b) components separated.

Figure 3

Resistance measurement setup using a digital multimeter at various anatomical locations on the conductive phantom head.

Figure 4

Distribution of resistance measurements across anatomical locations. Box plots represent the three measurements per location, showing median, mean, and individual measurements. The shaded region indicates the expected resistance range (900–1700 $\mathsf{\Omega }$) for injection-molded phantoms.

Figure 5

Signal transmission testing: (left) Experimental setup with signal generator and oscilloscope; (right) Output waveform showing preserved 200 Hz sine wave characteristics.

Figure 6

Complete OpenBCI experimental setup for phantom validation: 3D-printed conductive phantom with 8 EEG electrodes connected via multi-colored electrode wires to OpenBCI Cyton board (bottom right), with laptop computer displaying real-time EEG signal acquisition and impedance monitoring through OpenBCI GUI software (v6.0.0-beta.1).

Figure 7

Multi-channel EEG system validation using OpenBCI Cyton: (left) Time-domain signal acquisition showing active signal transmission across all 8 channels; (right) Electrode impedance measurements demonstrating consistent values of 5–11 k$\mathsf{\Omega }$ across connected channels, with head diagram showing electrode positions.

Figure 8

Spatial signal localization visualization using OpenBCI head plot. Active signal on Channel 3 is correctly localized (red region) with minimal crosstalk to adjacent channels, demonstrating the phantom’s capability for spatially-resolved EEG validation studies.

Figure 9

LCR meter impedance measurement setup showing probe placement on phantom frontal region. The measurement configuration demonstrates the systematic approach used for regional impedance characterization across all anatomical locations.

Figure 10

Complex impedance analysis of 3D-printed phantom across anatomical regions measured at 100 Hz using LCR meter with conductive paste. The Nyquist plot demonstrates impedance magnitude ranging from 3.01–6.4 k$\mathsf{\Omega }$ with consistent capacitive behavior (phase angles −53° to −67°) across all regions. The spatial impedance variation provides controlled heterogeneity suitable for electrode testing while the negative reactance values confirm frequency-dependent electrical properties appropriate for EEG sensing system validation.