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

Networks of respiratory-muscular coupling in exercise and fatigue in young adults.

Physiological reports·2026
Same author

Cascade Skip-Connection BiLSTM Autoencoder for CPR Artifact Removal Prior to AED Shock Advisory.

IEEE open journal of the Computer Society·2026
Same author

ospEDA: Orthogonal Subspace Projection for Electrodermal Activity Decomposition.

IEEE transactions on bio-medical engineering·2026
Same author

Multimodal Detection of Pain and Anticipation Anxiety from Ultra-Short Duration Wearable Sensors Measurements.

Sensors (Basel, Switzerland)·2026
Same author

ERG-Graph: Graph Signal Processing of the Electroretinogram for Classification of Neurodevelopmental Disorders.

Bioengineering (Basel, Switzerland)·2026
Same author

Continuous Emotion Recognition Using EDA-Graphs: A Graph Signal Processing Approach for Affective Dimension Estimation.

Applied sciences (Basel, Switzerland)·2026

Related Experiment Video

Updated: Sep 23, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.5K

A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity.

Md-Billal Hossain, Hugo F Posada-Quintero, Ki H Chon

    IEEE Transactions on Bio-Medical Engineering
    |May 11, 2022
    PubMed
    Summary
    This summary is machine-generated.

    A new deep convolutional autoencoder (DCAE) effectively removes motion artifacts from electrodermal activity (EDA) signals. This advanced technique recovers valuable EDA data previously lost to noise, outperforming existing methods.

    More Related Videos

    Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
    09:42

    Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

    Published on: January 24, 2025

    723
    Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
    08:31

    Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

    Published on: July 31, 2016

    13.7K

    Related Experiment Videos

    Last Updated: Sep 23, 2025

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    43.5K
    Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
    09:42

    Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

    Published on: January 24, 2025

    723
    Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
    08:31

    Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

    Published on: July 31, 2016

    13.7K

    Area of Science:

    • Physiological signal processing
    • Biomedical engineering
    • Machine learning applications

    Background:

    • Electrodermal activity (EDA) signals are crucial for understanding physiological and psychological states.
    • Motion artifacts (MA) are a significant challenge, often corrupting EDA data and leading to its exclusion.
    • Existing MA removal techniques have limitations in handling high-intensity artifacts.

    Purpose of the Study:

    • To develop a robust, data-driven, automatic method for removing motion artifacts from electrodermal activity signals.
    • To introduce a deep convolutional autoencoder (DCAE) approach for enhanced MA removal.
    • To improve the quality and usability of EDA data affected by motion artifacts.

    Main Methods:

    • Proposed a deep convolutional autoencoder (DCAE) model for automatic MA removal in EDA signals.
    • Trained and validated the DCAE using diverse public datasets and novel laboratory-collected MA data.
    • Compared DCAE performance against existing methods on independent, unseen datasets (CMAD II, CNS-OT).

    Main Results:

    • The DCAE model achieved significantly higher signal-to-noise-power-ratio improvement (SNRimp) and lower mean squared error (MSE) compared to previous methods.
    • Reconstructed EDAs from the CMAD II dataset showed a higher correlation (0.78) with clean data than raw corrupted data (0.68).
    • Demonstrated superior performance in removing high-intensity motion artifacts where other methods failed.

    Conclusions:

    • The developed DCAE model offers a robust and effective solution for automatic motion artifact removal in EDA signals.
    • This technique significantly enhances the quality of EDA data, enabling the recovery of previously unusable recordings.
    • The findings suggest a promising approach for improving the reliability and scope of EDA-based research.