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

The mediating effects of technology trust and perceived value in the relationship between eHealth literacy and attitude toward the usage of artificial intelligence in nursing: a cross-sectional study.

BMC nursing·2025
Same author

The Pathologic Roles and Therapeutic Implications of Ghrelin/GHSR System in Mental Disorders.

Depression and anxiety·2025
Same author

NeOPT: neural optical projection tomography with low-cost imaging setup.

Optics letters·2025
Same author

Glycogen synthase kinase 3 controls T-cell exhaustion by regulating NFAT activation.

Cellular & molecular immunology·2023
Same author

Transcriptome analysis of Δmig1Δmig2 mutant reveals their roles in methanol catabolism, peroxisome biogenesis and autophagy in methylotrophic yeast Pichia pastoris.

Genes & genomics·2018
Same author

[Generalized interaction LASSO based on alternating direction method of multipliers for liver disease classification].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2018
Same journal

[Advances in research on neuroelectrophysiological characteristics of post-stroke cognitive impairment based on quantitative electroencephalography and acupuncture interventions].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2026
Same journal

[Mechanisms and applications of magnesium ion-regulated stem cell functions in promoting tendon-bone interface healing].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2026
Same journal

[Applications and challenges of ultra-high molecular weight polyethylene fibers in minimally invasive medical devices].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2026
Same journal

[Research on auditory neurofeedback technology and its multi-disciplinary applications].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2026
Same journal

[Application and perspective of novel auditory intervention paradigms based on verbal and nonverbal stimuli for severe traumatic brain injury].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2026
Same journal

[Research progress on the neuromodulation targets in stroke rehabilitation].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2026
See all related articles

Related Experiment Video

Updated: Apr 6, 2026

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

12.0K

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

Jinjia Wang, Yuan Liu

    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
    |July 28, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new feature extraction method for brain-computer interfaces (BCIs) using multivariate empirical mode decomposition (MEMD) and power spectrum analysis. The novel approach enhances the accuracy of classifying motor imagery tasks from electroencephalogram (EEG) signals.

    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

    44.3K
    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    15.4K

    Related Experiment Videos

    Last Updated: Apr 6, 2026

    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

    12.0K
    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

    44.3K
    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    15.4K

    Area of Science:

    • Neuroscience
    • Signal Processing
    • Biomedical Engineering

    Background:

    • Non-stationary electroencephalogram (EEG) and magnetoencephalogram (MEG) signals pose challenges for brain-computer interface (BCI) systems.
    • Accurate feature extraction is crucial for reliable BCI performance.

    Purpose of the Study:

    • To develop and validate a novel feature extraction method for non-stationary EEG/MEG signals in BCI applications.
    • To improve the classification accuracy of motor imagery tasks.

    Main Methods:

    • Utilized multivariate empirical mode decomposition (MEMD) to decompose multichannel brain signals into intrinsic mode functions (IMFs).
    • Applied principal component analysis (PCA) for dimensionality reduction of power spectrum features extracted from IMFs.
    • Employed linear discriminant analysis (LDA) for classifying motor imagery tasks.

    Main Results:

    • Achieved 92.0% correct recognition for two-class motor imagery tasks.
    • Achieved 46.2% correct recognition for four-class motor imagery tasks.
    • Outperformed the winning method in BCI competitions III and IV.

    Conclusions:

    • The proposed MEMD-based feature extraction method is effective and stable for BCI applications.
    • This method offers a promising new approach for analyzing non-stationary brain signals.
    • The findings suggest significant potential for advancing BCI technology.