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

Biophysical and Biochemical Assays for Screening Small Molecule Inhibitors Targeting Toxin-Ribosome Interactions.

Toxins·2026
Same author

[Coupling Coordination Measurement and Convergence of Provincial Carbon Emissions and New Quality Productivity in China].

Huan jing ke xue= Huanjing kexue·2026
Same author

Gastrointestinal diseases-induced cardiovascular dysfunction: Focus on clinical presentation, pathogenesis and therapeutic implications of gastrocardiac syndrome.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2026
Same author

Intraductal papillary neoplasm of the biliary tract with typical clinicopathological, endoscopic features: A case report.

World journal of gastrointestinal surgery·2026
Same author

[Mechanism of Xiangshao Granules in alleviating anxiety and depression in mice based on integrated metabolomics and gut microbiota].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2025
Same author

Two-Dimensional Dual-Switchable Ferroelectric Altermagnets: Altering Electrons and Magnons.

Nano letters·2025
Same journal

Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

IEEE transactions on bio-medical engineering·2026
Same journal

Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

IEEE transactions on bio-medical engineering·2026
Same journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026
Same journal

Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

IEEE transactions on bio-medical engineering·2026
Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026
See all related articles

Related Experiment Video

Updated: Jun 25, 2026

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

Automatic EEG artifact removal: a weighted support vector machine approach with error correction.

Shi-Yun Shao1, Kai-Quan Shen, Chong Jin Ong

  • 1Department of Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore. shao@nus.edu.sg

IEEE Transactions on Bio-Medical Engineering
|March 11, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces an improved automatic electroencephalogram (EEG) artifact removal technique. The novel method effectively removes artifacts while preserving essential brain activity, outperforming existing approaches.

More Related Videos

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

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
10:35

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

Related Experiment Videos

Last Updated: Jun 25, 2026

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

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

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
10:35

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electroencephalogram (EEG) recordings are susceptible to artifacts that can obscure neural activity.
  • Existing artifact removal methods often struggle with imbalanced data and lack structural information integration.
  • Effective artifact removal is crucial for accurate analysis of brain activity.

Purpose of the Study:

  • To present a novel automatic electroencephalogram (EEG) artifact removal method.
  • To address limitations of previous methods by incorporating a weighted Support Vector Machine (SVM) and structural information.
  • To demonstrate the superiority of the proposed method on real-life EEG data.

Main Methods:

  • Development of a weighted Support Vector Machine (SVM) formulation to handle imbalanced component classification.
  • Integration of structural information into the component classification process.
  • Validation using real-life EEG recordings and comparison against benchmark artifact removal techniques.

Main Results:

  • The proposed method demonstrates superior performance in EEG artifact removal compared to benchmark methods.
  • Achieved a better balance between artifact removal and preservation of inherent brain activities.
  • Qualitative evaluation confirmed significant preservation of neural signals post-artifact removal.

Conclusions:

  • The novel EEG artifact removal method offers significant advantages over existing techniques.
  • The weighted SVM and structural information integration contribute to improved artifact removal efficacy.
  • The method effectively preserves underlying brain activity, enhancing the reliability of EEG analysis.