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

Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
Energy and Power Signals01:17

Energy and Power Signals

In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:

You might also read

Related Articles

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

Sort by
Same author

Analysis of electrohysterogram signals for predicting obstetric outcome using machine learning methods: a scoping review.

BMC pregnancy and childbirth·2026
Same author

A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument.

Diagnostics (Basel, Switzerland)·2023
Same author

Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification Using Nerve Conduction Studies.

Computational intelligence and neuroscience·2022
Same author

A Novel AMARS Technique for Baseline Wander Removal Applied to Photoplethysmogram.

IEEE transactions on biomedical circuits and systems·2017
Same author

Active inductor based fully integrated CMOS transmit/ receive switch for 2.4 GHz RF transceiver.

Anais da Academia Brasileira de Ciencias·2016
Same author

Nonlinear-index-of-refraction measurement in a resonant region by the use of a fiber Mach-Zehnder interferometer.

Applied optics·2010

Related Experiment Video

Updated: May 7, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Surface electromyography signal processing and classification techniques.

Rubana H Chowdhury1, Mamun B I Reaz, Mohd Alauddin Bin Mohd Ali

  • 1Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia. rubana86@hotmail.com.

Sensors (Basel, Switzerland)
|September 20, 2013
PubMed
Summary

This review covers electromyography (EMG) signal processing, focusing on artifact removal and classification methods. It compares various analysis techniques to enhance EMG applications in clinical and assistive technologies.

More Related Videos

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

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

Related Experiment Videos

Last Updated: May 7, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

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

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

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Neuroscience

Background:

  • Electromyography (EMG) signals are crucial for clinical, rehabilitation, and human-machine interaction applications.
  • Signal noise and artifacts significantly hinder the performance and reliability of EMG-based systems.
  • Accurate detection, processing, and classification of EMG signals are essential for precise evaluation.

Purpose of the Study:

  • To review pre-processing methods for artifact elimination in EMG signal acquisition.
  • To explain various methods for processing and classifying EMG signals.
  • To compare the performance of different EMG signal analysis techniques.

Main Methods:

  • Literature review of recent developments in EMG signal processing.
  • Focus on artifact removal strategies during EMG recording.
  • Explanation and comparison of EMG signal processing and classification algorithms.

Main Results:

  • Identified key pre-processing techniques for artifact reduction in EMG signals.
  • Summarized diverse methods for EMG signal processing and classification.
  • Provided a comparative overview of the performance of various EMG analysis approaches.

Conclusions:

  • Effective pre-processing and appropriate classification methods are vital for improving EMG signal utility.
  • This review highlights recent advancements and offers insights into selecting optimal EMG analysis techniques.
  • Advancements in EMG signal analysis pave the way for more sophisticated biomedical and assistive technologies.