A modular soft core-based system for affordable acquisition and processing of electrophysiological recordings

• Modular platform for electrophysiological signal acquisition. • FPGA-based design with a soft core processor. • Experimental validation using biological signals. • Cost-effective design supports wider adoption in research laboratories. Flexible low-cost platforms for high-fidelity recording of biological signals are essential to advance health monitoring applications, yet they often rely on proprietary hardware that is resource-intensive and expensive. This study introduces a cost-effective FPGA platform that embeds an ARM Cortex M1 soft core processor with custom logic for signal acquisition, signal conditioning, artifact suppression, and data management, which is built around an Intan RHD2000 headstage. On-chip routines perform automatic offset calibration and gain calibration to ensure measurement fidelity. A graphical user interface, co-developed with biomedical end users, ensures compliance with data rate and timing constraints, enables live visualization, and streamlines parameter configuration. Bench validation using a multichannel test generator reproducing cardiac field potentials up to ± 2 mV and bandwidths up to 5 kHz demonstrated stable timing, low crosstalk, and accurate amplitude reconstruction. Platform performance matches that of commercial multielectrode array systems, with minor deviations attributed to interconnection effects. Together, these results provide an accessible and extensible platform that preserves measurement fidelity while lowering the barrier to adoption in resource constrained laboratories.

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Publication Details

Journal
Results in Engineering
Published
2026-02-27
DOI
https://doi.org/10.1016/j.rineng.2026.109764
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
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article

A modular soft core-based system for affordable acquisition and processing of electrophysiological recordings

Antoni Castel, Aida Oliván‐Viguera, Antonio Velarte, Esther Pueyo et al.
Results in Engineering
Neuroscience and Neural Engineering
article

A modular soft core-based system for affordable acquisition and processing of electrophysiological recordings

Antoni Castel, Aida Oliván‐Viguera, Antonio Velarte, Esther Pueyo, Aranzazu Otin
article en

Abstract

• Modular platform for electrophysiological signal acquisition. • FPGA-based design with a soft core processor. • Experimental validation using biological signals. • Cost-effective design supports wider adoption in research laboratories. Flexible low-cost platforms for high-fidelity recording of biological signals are essential to advance health monitoring applications, yet they often rely on proprietary hardware that is resource-intensive and expensive. This study introduces a cost-effective FPGA platform that embeds an ARM Cortex M1 soft core processor with custom logic for signal acquisition, signal conditioning, artifact suppression, and data management, which is built around an Intan RHD2000 headstage. On-chip routines perform automatic offset calibration and gain calibration to ensure measurement fidelity. A graphical user interface, co-developed with biomedical end users, ensures compliance with data rate and timing constraints, enables live visualization, and streamlines parameter configuration. Bench validation using a multichannel test generator reproducing cardiac field potentials up to ± 2 mV and bandwidths up to 5 kHz demonstrated stable timing, low crosstalk, and accurate amplitude reconstruction. Platform performance matches that of commercial multielectrode array systems, with minor deviations attributed to interconnection effects. Together, these results provide an accessible and extensible platform that preserves measurement fidelity while lowering the barrier to adoption in resource constrained laboratories.

Results in EngineeringVol. 29
Universidad de Zaragoza (ES), Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (ES)
Openalex Percentile: Top 41%
Neuroscience and Neural Engineering
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