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 Experiment Videos

Brain-computer interface analysis using continuous wavelet transform and adaptive neuro-fuzzy classifier.

Sam Darvishi1, Ahmed Al-Ani

  • 1Faculty of Engineering, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia. darvish.sam@gmail.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
Summary
This summary is machine-generated.

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

Nordic consensus document on Evolut FX+ transcatheter aortic valve implantation: optimizing index implantation and longer-term outcomes.

Frontiers in cardiovascular medicine·2025
Same author

Use of Biomarker Testing in Advanced Non-Small Cell Lung Cancer: A Retrospective Analysis.

Annals of the American Thoracic Society·2025
Same author

Enhancing poststroke hand movement recovery: Efficacy of RehabSwift, a personalized brain-computer interface system.

PNAS nexus·2024
Same author

Progress in Brain Computer Interface: Challenges and Opportunities.

Frontiers in systems neuroscience·2021
Same author

Utility of Precision Medicine in the Management of Diabetes: Expert Opinion from an International Panel.

Diabetes therapy : research, treatment and education of diabetes and related disorders·2020
Same author

Pacemaker Requirements following Self-Expanding Transcatheter Aortic Valve Replacement.

Cardiology·2019
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

This study analyzes electroencephalogram (EEG) signals for brain-computer interface (BCI) applications, using an Adaptive Neuron-Fuzzy Inference System (ANFIS) for classifying imaginary hand movements.

Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Brain-computer interfaces (BCI) enable communication and control through brain signals.
  • Electroencephalogram (EEG) is a non-invasive method for capturing brain activity.
  • Classifying EEG signals for motor imagery is crucial for BCI development.

Purpose of the Study:

  • To analyze electroencephalogram (EEG) signals corresponding to imaginary left and right hand movements.
  • To evaluate the efficacy of an Adaptive Neuron-Fuzzy Inference System (ANFIS) for classifying these EEG signals.
  • To compare the performance of ANFIS with a Support Vector Machine (SVM) classifier.

Main Methods:

  • Feature extraction from EEG signals using continuous wavelet transform.
  • Classification of motor imagery using an Adaptive Neuron-Fuzzy Inference System (ANFIS).

Related Experiment Videos

  • Comparative analysis of ANFIS against Support Vector Machine (SVM) performance.

    • Main Results:

    • ANFIS demonstrated effectiveness in classifying EEG signals for imaginary hand movements.
    • The ANFIS model provided interpretable parameters and linguistic rules.
    • Performance metrics showed ANFIS as a viable alternative to SVM for this BCI application.

    Conclusions:

    • ANFIS is a promising classification algorithm for brain-computer interface applications.
    • The combination of continuous wavelet transform and ANFIS offers robust feature extraction and classification.
    • Further research can explore ANFIS for more complex BCI tasks.