Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dynamics of mesoscale brain network during visual discrimination learning revealed by chronic, large-scale single-unit recording.

eLife·2026
Same author

Revolutionizing brain-computer interfaces: Compact and high-speed wireless neural signal acquisition.

The Review of scientific instruments·2025
Same author

Genome characterization based on the Spike-614 and NS8-84 loci of SARS-CoV-2 reveals two major possible onsets of the COVID-19 pandemic.

PloS one·2023
Same author

[Health Risk Assessment of Heavy Metals in Soil and Wheat Grain in the Typical Sewage Irrigated Area of Shandong Province].

Huan jing ke xue= Huanjing kexue·2023
Same author

A Temperature-to-Frequency Converter-Based On-Chip Temperature Sensor with an Inaccuracy of +0.65 °C/-0.49 °C.

Sensors (Basel, Switzerland)·2023
Same author

Dual-asymmetrically selective interfaces-enhanced poly(lactic acid)-based nanofabric with sweat management and switchable radiative cooling and thermal insulation.

Journal of colloid and interface science·2023

Related Experiment Video

Updated: Jun 19, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.7K

Modeling and Analysis of Environmental Electromagnetic Interference in Multiple-Channel Neural Recording Systems for

Gang Wang1,2, Changhua You3, Chengcong Feng4

  • 1School of Microelectronics, Shanghai University, Shanghai 200444, China.

Biosensors
|July 26, 2024
PubMed
Summary

This study introduces models to reduce environmental electromagnetic interference (EMI) in neural recording systems. The proposed design guidelines significantly decrease EMI, preserving neural action potential (AP) signal quality.

Keywords:
electromagnetic interference (EMI)equivalent circuit modelmultiple-channelneural recording system

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Multi-unit Recording Methods to Characterize Neural Activity in the Locust Schistocerca Americana Olfactory Circuits
12:13

Multi-unit Recording Methods to Characterize Neural Activity in the Locust Schistocerca Americana Olfactory Circuits

Published on: January 25, 2013

27.1K

Related Experiment Videos

Last Updated: Jun 19, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.7K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Multi-unit Recording Methods to Characterize Neural Activity in the Locust Schistocerca Americana Olfactory Circuits
12:13

Multi-unit Recording Methods to Characterize Neural Activity in the Locust Schistocerca Americana Olfactory Circuits

Published on: January 25, 2013

27.1K

Area of Science:

  • Neuroscience
  • Electrical Engineering
  • Biomedical Engineering

Background:

  • Environmental electromagnetic interference (EMI) poses a significant challenge for multichannel neural recording systems.
  • Limited theoretical frameworks exist to address EMI in these sensitive systems.

Purpose of the Study:

  • To develop equivalent circuit models for EMI sources and neural signals.
  • To establish design guidelines for neural probes and recording circuits to enhance common-mode interference (CMI) rejection.
  • To maintain the quality of recorded neural action potential (AP) signals.

Main Methods:

  • Proposed equivalent circuit models for EMI and neural signals.
  • Performed analysis to derive design guidelines for neural probes and recording circuits.
  • Conducted in vivo animal experiments using a 32-channel neural recording system.

Main Results:

  • Demonstrated a three-order reduction in the power spectral density (PSD) of 50 Hz EMI.
  • Achieved a reduction from 4.43 × 10-3 V2/Hz to 4.04 × 10-6 V2/Hz.
  • Confirmed no adverse effect on recorded AP signal quality in an unshielded environment.

Conclusions:

  • The proposed models and design guidelines effectively mitigate environmental EMI in neural recording.
  • High common-mode interference rejection performance can be achieved without compromising neural signal integrity.
  • Validated through in vivo experiments, offering practical solutions for improved neural data acquisition.