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A novel analog interface using 3D printed electrodes for mixed voltage-current mode EEG
Author(s) -
A. Rotondo,
G. Barile,
G. Ferri,
V. Stornelli
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3614505
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
This work presents a comprehensive study on a novel analog interface for single- and dual-channel electroencephalographic (EEG) analysis, employing current-mode active building blocks (ABBs), specifically the second-generation current conveyor (CCII) and voltage conveyor (VCII). The proposed mixed voltage-current mode approach enables voltage signal acquisition at the input stage, followed by current-domain processing in subsequent stages. A discrete PCB prototype was developed using AD844 operational amplifiers to emulate both CCII and VCII behavior. Signal acquisition was performed using a range of custom-designed 3D-printed passive and active electrodes fabricated with copper-based conductive filament, whose contact impedance variability was also characterized. The performance of the proposed interface was experimentally evaluated and compared against commercial EEG analog front-end chips such as the ADS1298, ADS1299, AD630, and other microelectronic circuits. The current-mode implementation demonstrates significant advantages, including high input impedance (ranging from 300 MΩ to 6.49 TΩ), excellent common-mode rejection ratio (CMRR > 100 dB), and a tunable bandwidth (0.1–200 Hz). The system successfully acquired high-quality EEG signals and limb movement data, highlighting its potential for low-noise, high-impedance biosignal interfaces.

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