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

Emergent behaviors in RBCs flows in micro-channels using digital particle image velocimetry.

Microvascular research·2017
Same author

Red blood cells flows in rectilinear microfluidic chip.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016
Same author

Bio-Microfluidics Real-Time Monitoring Using CNN Technology.

IEEE transactions on biomedical circuits and systems·2013
Same author

A cellular nonlinear network: real-time technology for the analysis of microfluidic phenomena in blood vessels.

Nanotechnology·2011
Same author

Real time blood flow velocity monitoring in the microcirculation.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference·2007
Same author

Real-time estimation of oxygen concentration in micro-hemo-vessels.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference·2007

Related Experiment Video

Updated: Mar 27, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.8K

Automatic preprocessing of EEG signals in long time scale.

C Corradino, M Bucolo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary

    This study introduces an automated method for cleaning electroencephalography (EEG) data, crucial for accurate long-term recordings. The approach uses Independent Component Analysis (ICA) to effectively remove physiological artifacts without manual intervention.

    More Related Videos

    A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
    12:03

    A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

    Published on: May 25, 2019

    9.0K
    PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
    06:51

    PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing

    Published on: June 6, 2025

    1.2K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
    06:40

    Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

    Published on: June 15, 2018

    10.8K
    A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
    12:03

    A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

    Published on: May 25, 2019

    9.0K
    PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
    06:51

    PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing

    Published on: June 6, 2025

    1.2K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Physiological artifacts significantly contaminate electroencephalography (EEG) signals.
    • Robust and repeatable EEG preprocessing is essential for reliable data analysis, especially in long-term studies.
    • Current methods often require manual intervention, limiting scalability.

    Purpose of the Study:

    • To develop an automated control feedback scheme for cleaning EEG independent components.
    • To eliminate the need for manual intervention in EEG data preprocessing.
    • To enhance the usability of EEG data in long-term experiments and multi-subject studies.

    Main Methods:

    • Independent Component Analysis (ICA) was applied to EEG data.
    • A control feedback scheme was implemented for automatic artifact management.
    • The method combines residual artifact checks, identification, and cleaning, with and without co-registrations.

    Main Results:

    • The automated procedure effectively managed the cleaning of independent component signals.
    • Demonstrated successful artifact removal on a test dataset.
    • The tool was integrated into a platform for automated EEG recording clearing.

    Conclusions:

    • The developed automated method provides a robust and repeatable solution for EEG preprocessing.
    • This approach facilitates the utilization of EEG data in large-scale, long-duration studies.
    • The embedded analysis tool supports automatic clearing of multi-subject EEG recordings.