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

You might also read

Related Articles

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

Sort by

Same author

Level-crossing processing and deep convolutional neural network for arrhythmia classification in telehealth services.

Physical and engineering sciences in medicine·2025

Same author

Identifying Risk Factors of Substance Use in Finland: A Machine Learning Approach.

Substance use & misuse·2025

Same author

Time Sequence Deep Learning Model for Ubiquitous Tabular Data with Unique 3D Tensors Manipulation.

Entropy (Basel, Switzerland)·2024

Same author

ECG based apnea detection by multirate processing hybrid of wavelet-empirical decomposition Hjorth features extraction and neural networks.

PloS one·2023

Same author

Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems.

Sensors (Basel, Switzerland)·2023

Same author

Automated Categorization of Multiclass Welding Defects Using the X-ray Image Augmentation and Convolutional Neural Network.

Sensors (Basel, Switzerland)·2023

Search research articles

Related Experiment Video

Updated: Oct 12, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface.

Abdulhamit Subasi^1,2, Saeed Mian Qaisar^2,3

¹Institute of Biomedicine, Faculty of Medicine, University of Turku, Kiinanmyllynkatu 10, Turku 20520, Finland.

Journal of Healthcare Engineering

|November 19, 2021

Summary

This summary is machine-generated.

This study introduces a new Brain-Computer Interface (BCI) method for identifying Motor Imagery (MI) tasks using AI. The novel approach achieves high accuracy in classifying brain signals for individuals with impairments.

Failed At:

2026-06-19T13:39:15.932413+00:00

More Related Videos

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Related Experiment Videos

Last Updated: Oct 12, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017