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

Muscles for Facial Expressions01:14

Muscles for Facial Expressions

The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role of...

You might also read

Related Articles

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

Sort by
Same author

Leveraging imperfection with MEDLEY: a multi-model approach harnessing bias in medical AI.

Frontiers in artificial intelligence·2026
Same author

A comprehensive evaluation of MRI-based radiogenomics and prognosis prediction in glioma.

Frontiers in oncology·2026
Same author

Enhancing Head and Neck Tumor Segmentation in MRI: The Impact of Image Preprocessing and Model Ensembling.

Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings·2025
Same author

Role of modeled high-grade glioma cell invasion and survival on the prediction of tumor progression after radiotherapy.

Physics in medicine and biology·2025
Same author

SegRap2023: A benchmark of organs-at-risk and gross tumor volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma.

Medical image analysis·2025
Same author

Radiomics and deep learning models for glioblastoma treatment outcome prediction based on tumor invasion modeling.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2024
Same journal

Non-invasive classification of stable HFpEF using a deep learning model trained on acoustic features of sustained vowels.

Biomedical engineering online·2026
Same journal

Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.

Biomedical engineering online·2026
Same journal

Potentials of BMSCs for regulating osteogenic-vascular-neural-lymphatic coupling in bone regeneration.

Biomedical engineering online·2026
Same journal

Protein adsorption at material interface: mechanistic design framework for engineering ceramic scaffolds for bone repair applications.

Biomedical engineering online·2026
Same journal

Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.

Biomedical engineering online·2026
Same journal

The influence of successful septal myectomy on myocardial stress distributions in the left ventricle: a computational analysis.

Biomedical engineering online·2026
See all related articles

Related Experiment Video

Updated: May 9, 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

EMG-based facial gesture recognition through versatile elliptic basis function neural network.

Mahyar Hamedi1, Sh-Hussain Salleh, Mehdi Astaraki

  • 1Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia. hamedi.mahyar@ieee.org

Biomedical Engineering Online
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

Facial gesture recognition using electromyograms (EMGs) is improved by identifying the Maximum Peak Value feature. A very fast versatile elliptic basis function neural network (VEBFNN) offers a promising approach for human-machine interfaces.

More Related Videos

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

Related Experiment Videos

Last Updated: May 9, 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

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

Area of Science:

  • Biomedical signal processing
  • Human-machine interfacing
  • Neural network applications

Background:

  • Facial gesture recognition via facial neuromuscular activities is key for human-machine interfaces.
  • Facial electromyograms (EMGs) analysis presents challenges in accuracy and computational cost.
  • A very fast versatile elliptic basis function neural network (VEBFNN) is proposed for classifying facial gestures.

Purpose of the Study:

  • To classify different facial gestures using facial EMG data.
  • To explore the effectiveness of various time-domain features for facial EMG analysis.
  • To introduce the most discriminating facial EMG time-domain feature for gesture recognition.

Main Methods:

  • Recorded EMGs of ten facial gestures from ten subjects using surface electrodes.
  • Extracted ten time-domain features and investigated their statistical relationships using Mutual Information.
  • Formed feature combinations and employed VEBFNN for classification, comparing its performance with SVM and MLP.
  • Evaluated system performance using recognition accuracy and training time.

Main Results:

  • Maximum Peak Value achieved the highest recognition accuracy at 87.1% among all tested features.
  • The VEBFNN demonstrated a very fast training time of 0.105 seconds.
  • Recognition Accuracy (RA) was found to be a more effective criterion for feature set selection than Minimum-Redundancy-Maximum-Relevance (MRMR).

Conclusions:

  • Identified Maximum Peak Value as the most discriminating facial EMG time-domain feature for gesture recognition.
  • VEBFNN is a promising method for EMG-based facial gesture classification in human-machine interaction systems.
  • The study highlights the importance of feature selection and efficient classifiers for accurate and fast facial gesture recognition.