Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Correction: Impact of crown and inlay size variations on the accuracy of various digital impression techniques.

Scientific reports·2026

Same author

Impact of crown and inlay size variations on the accuracy of various digital impression techniques.

Scientific reports·2025

Same author

Global, regional, and national epidemiology of neurological disorders and subcategories: incidence and disability-adjusted life years, 1990-2021.

European journal of medical research·2025

Same author

Unveiling transfer RNA modifications of oil palm and their dynamic changes during fruit ripening.

BMC plant biology·2025

Same author

Blockade of ITGA2/3/5 Promotes Adipogenic Differentiation of Human Adipose-derived Mesenchymal Stem Cells.

Cell biochemistry and biophysics·2024

Same author

Unraveling the complex interconnections between food-energy-water nexus sustainability and the supply-demand of related ecosystem services.

Journal of environmental management·2024

Search research articles

Related Experiment Video

Updated: Jul 10, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

[An EMD based time-frequency distribution and its application in EEG analysis].

Xiaobing Li¹, Meng Chu, Tianshuang Qiu

¹Department of Electronic Engineering, Dalian University of Technology, Dalian 116024, China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|November 22, 2007

Summary

This summary is machine-generated.

A novel Empirical Mode Decomposition (EMD) method effectively suppresses cross-terms in the Wigner-Ville Distribution (WVD) for analyzing nonlinear, non-stationary signals like epileptic EEG data.

More Related Videos

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

Related Experiment Videos

Last Updated: Jul 10, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

Area of Science:

Signal processing
Time-frequency analysis
Biomedical engineering

Context:

Nonlinear and non-stationary signals present analysis challenges.
The Hilbert-Huang Transform (HHT) utilizes Empirical Mode Decomposition (EMD) to break down complex signals into Intrinsic Mode Functions (IMFs).
The Wigner-Ville Distribution (WVD) is a powerful time-frequency analysis tool but suffers from cross-term interference.

Purpose:

To develop a new method for suppressing Wigner-Ville Distribution (WVD) cross-terms.
To leverage Empirical Mode Decomposition (EMD) for enhanced time-frequency analysis.
To apply the developed method to the analysis of epileptic electroencephalogram (EEG) signals.

Summary:

A novel method based on Empirical Mode Decomposition (EMD) is introduced to mitigate cross-term interference in the Wigner-Ville Distribution (WVD).
This EMD-based approach effectively decomposes signals into Intrinsic Mode Functions (IMFs), facilitating cleaner time-frequency representations.
The method was successfully applied to analyze epileptic EEG signals, demonstrating its practical utility.

Impact:

Effective suppression of WVD cross-terms enhances signal analysis resolution.
Provides a more accurate time-frequency analysis for nonlinear and non-stationary signals.
Offers a valuable tool for analyzing complex biomedical signals, such as epileptic EEG patterns.