Intan Technologies integrated circuits can produce analog-to-digital conversion artifacts that affect neural signal acquisition
Year: 2024
Type: article
Abstract: Objective. Intan Technologies’ integrated circuits (ICs) are valuable tools for neurophysiological data acquisition, providing signal amplification, filtering, and digitization from many channels (up to 64 channels/chip) at high sampling rates (up to 30 kSPS) within a compact package (⩽9× 7 mm). However, we found that the analog-to-digital converters (ADCs) in the Intan RHD2000 series ICs can produce artifacts in recorded signals. Here, we examine the effects of these ADC artifacts on neural signal quality and describe a method to detect them in recorded data. Approach. We identified two types of ADC artifacts produced by Intan ICs: 1) jumps, resulting from missing output codes, and 2) flatlines, resulting from overrepresented output codes. We identified ADC artifacts in neural recordings acquired with Intan RHD2000 ICs and tested the repeated performance of 17 ICs in vitro. With the on-chip digital-signal-processing disabled, we detected the ADC artifacts in each test recording by examining the distribution of unfiltered ADC output codes. Main Results. We found larger ADC artifacts in recordings using the Intan RHX data acquisition software versions 3.0–3.2, which did not run the necessary ADC calibration command when the inputs to the Intan recording controller were rescanned. This has been corrected in the Intan RHX software version 3.3. We found that the ADC calibration routine significantly reduced, but did not fully eliminate, the occurrence and size of ADC artifacts as compared with recordings acquired when the calibration routine was not run (p < 0.0001). When the ADC calibration routine was run, we found that the artifacts produced by each ADC were consistent over time, enabling us to sort ICs by performance. Significance. Our findings call attention to the importance of evaluating signal quality when acquiring electrophysiological data using Intan Technologies ICs and offer a method for detecting ADC artifacts in recorded data.
Source: Journal of Neural Engineering
Authors: Katrina Barth, Cecilia Schmitz, Thomas Jochum, Jonathan Viventi
Institutions: Duke University, Duke Medical Center
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Defense, National Institutes of Health, National Institute on Deafness and Other Communication Disorders, National Institute of Neurological Disorders and Stroke, Division of Chemical, Bioengineering, Environmental, and Transport Systems, National Center for Advancing Translational Sciences
Awards: Optimizing flexible, active electrode arrays for chronic, large-scale recording and stimulation on the scale of 100,000 electrodes, Duke CTSA, G2050033097, G2699415559, CAREER: Resolving action potentials and high-density neural signals from the surface of the brain, Neuro-CROWN:Optimized Ultra-Flexible CMOS Electrode Arrays for 3D, Low-Noise Neural Interfaces, Advancing Epilepsy Diagnosis with Flexible, High-Resolution Thin-Film Electrodes, A Wireless micro-ECoG Prosthesis for Speech
OpenAlex ID: W4399564270