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

A Smartphone-Based Psychological Intervention for Nonsuicidal Self-Injury (Kalmer App): Protocol for a Multicenter Double-Blind Randomized Controlled Trial.

JMIR research protocols·2026
Same author

Multi-source self-guided domain adaptation framework for EEG-based emotion recognition.

Medical & biological engineering & computing·2025
Same author

Information Theory Quantifiers in Cryptocurrency Time Series Analysis.

Entropy (Basel, Switzerland)·2025
Same author

Combination Antitumor Activation of Anlotinib with Radiofrequency Ablation in Human Medullary Thyroid Carcinoma.

Current molecular medicine·2024
Same author

Photosensitizing metal-organic framework nanoparticles combined with tumor-sensitization strategies can enhance the phototherapeutic effect upon medullary thyroid carcinoma.

Biochimica et biophysica acta. General subjects·2024
Same author

Advancements in NADH Oxidase Nanozymes: Bridging Nanotechnology and Biomedical Applications.

Advanced healthcare materials·2024

Related Experiment Video

Updated: Oct 18, 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

43.6K

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode

Yangyang Dai1, Feng Duan1, Fan Feng1

  • 1College of Artificial Intelligence, Nankai University, Tianjin 300350, China.

Entropy (Basel, Switzerland)
|September 28, 2021
PubMed
Summary

A new method using signal serialization based EEMD (sEEMD) and canonical correlation analysis (CCA) effectively removes electromyography (EMG) artifacts from electroencephalogram (EEG) signals. This sEEMD-CCA approach significantly speeds up processing for real-time brain-computer interface applications.

Keywords:
CCAEEGEEMDEMG artifact rejectionsignal serialization

More Related Videos

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.3K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.3K

Related Experiment Videos

Last Updated: Oct 18, 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

43.6K
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.3K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.3K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electroencephalogram (EEG) signals are crucial for brain-computer interfaces (BCI) but are often contaminated by electromyography (EMG) artifacts.
  • Effective EEG denoising is essential for reliable BCI performance, especially for patients with movement disorders.
  • Traditional Ensemble Empirical Mode Decomposition (EEMD) combined with Canonical Correlation Analysis (CCA) can suppress EMG artifacts but is computationally intensive for real-time applications.

Purpose of the Study:

  • To introduce and evaluate a novel, faster EMG artifact removal method for EEG signals using signal serialization based EEMD (sEEMD) and CCA.
  • To compare the denoising performance and computational efficiency of the proposed sEEMD-CCA method against the conventional EEMD-CCA method.
  • To assess the suitability of sEEMD-CCA for real-time BCI systems requiring rapid artifact suppression.

Main Methods:

  • Developed an EMG artifact denoising technique by integrating signal serialization based EEMD (sEEMD) with Canonical Correlation Analysis (CCA).
  • Analyzed semi-simulated EEG data with varying levels of EMG contamination.
  • Compared the intrinsic mode functions (IMFs) decomposition, artifact separation accuracy, and processing speed of sEEMD-CCA against EEMD-CCA.

Main Results:

  • sEEMD decomposed IMFs showed consistency in frequency and amplitude with those from EEMD.
  • sEEMD-CCA demonstrated comparable accuracy in separating EMG artifacts from EEG signals to EEMD-CCA (p > 0.05), even under heavy contamination (SNR < 2 dB).
  • The sEEMD-CCA method achieved a significant improvement in running speed, reducing processing time by over 50% compared to EEMD-CCA (p < 0.05).

Conclusions:

  • sEEMD-CCA is a highly effective and significantly faster alternative for EMG artifact removal from EEG signals.
  • The method maintains high denoising performance, making it suitable for real-time BCI applications.
  • sEEMD-CCA offers a promising solution to overcome the limitations of traditional EEMD-CCA in time-critical BCI systems.