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

Discrete Fourier Transform01:15

Discrete Fourier Transform

1.3K
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...
1.3K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

992
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
992
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

1.5K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
1.5K
Fast Fourier Transform01:10

Fast Fourier Transform

1.3K
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Syncope as the Initial Presentation of Takayasu Arteritis in a 57-Year-Old Female: A Case Report and Literature Review.

Clinical case reports·2026
Same author

Virgin Coconut Oil Attenuates Diabetic Kidney Disease via Gut Microbiota-Metabolism-Inflammation Axis Modulation in Type 2 Diabetic Mice.

Food science & nutrition·2026
Same author

The mechanisms of myricetin and quercetin in regulating miRNA-140 and MMP/TIMP signaling pathway in osteoarthritis treatment.

Pakistan journal of pharmaceutical sciences·2026
Same author

Electrical muscle stimulation towards self-physiotherapy on myofascial pain syndrome.

Frontiers in rehabilitation sciences·2026
Same author

TaWRKY33 Positively Regulates TaERF1-A, Thereby Activating TaP5CS<sub>2</sub> <sup>-</sup>Mediated Proline Biosynthesis, Which Enhances Drought Tolerance in Wheat (Triticum aestivum L.).

Plant, cell & environment·2026
Same author

GWAS-by-subtraction reveals new genetic architecture and health implications of type 2 diabetes-independent gestational diabetes mellitus.

Genome medicine·2026

Related Experiment Video

Updated: May 7, 2026

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

1.5K

Improved discrete Fourier transform based spectral feature for surface electromyogram signal classification.

Jiayuan He, Dingguo Zhang, Xinjun Sheng

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary

    An improved discrete Fourier transform (iDFT) offers a novel feature for surface electromyogram (sEMG) classification. This method enhances pattern recognition for prosthetic control by analyzing signal spectrum changes during different motions.

    More Related Videos

    Extraction of the EPP Component from the Surface EMG
    07:16

    Extraction of the EPP Component from the Surface EMG

    Published on: December 16, 2009

    12.1K
    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
    06:34

    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

    Published on: July 7, 2023

    3.6K

    Related Experiment Videos

    Last Updated: May 7, 2026

    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

    1.5K
    Extraction of the EPP Component from the Surface EMG
    07:16

    Extraction of the EPP Component from the Surface EMG

    Published on: December 16, 2009

    12.1K
    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
    06:34

    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

    Published on: July 7, 2023

    3.6K

    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Rehabilitation Technology

    Background:

    • Surface electromyogram (sEMG) signals are crucial for understanding muscle activity and developing advanced prosthetic control.
    • Current sEMG pattern classification methods face challenges in achieving high accuracy and robustness across different users and conditions.
    • Spectral analysis of sEMG signals is a promising avenue for feature extraction, but existing methods like Auto Regression (AR) and Power Spectral Density (PSD) have limitations.

    Purpose of the Study:

    • To introduce and evaluate an improved discrete Fourier transform (iDFT) as a novel feature for sEMG pattern classification.
    • To demonstrate the superiority of the iDFT feature over traditional spectral features (AR and PSD) in terms of separability.
    • To assess the performance of iDFT across both experienced and inexperienced subjects and identify optimal parameters for its application.

    Main Methods:

    • The study proposes an iDFT feature that captures global spectral information within local frequency bands of sEMG signals.
    • This approach aims to increase the inter-class distance, thereby improving the discriminative power of sEMG patterns.
    • Experiments were conducted comparing iDFT with AR and PSD features, analyzing their separability and performance under varying conditions.

    Main Results:

    • The iDFT feature demonstrated significantly better separability compared to AR and PSD features for sEMG pattern classification.
    • This improved performance was consistent across both experienced and inexperienced subjects.
    • The optimal bandwidth for the iDFT feature was found to be between 30 and 50 Hz, with minimal influence from different signal division methods.

    Conclusions:

    • The iDFT feature presents a novel and effective approach for sEMG pattern classification, outperforming existing spectral methods.
    • Its low computational cost and insensitivity to sampling frequency make it a practical choice for real-time applications.
    • The iDFT method offers a competitive and robust solution for advanced prosthetic control systems.