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

Effect of a Personalized Mobile App on Glucose Control in Adults With Prediabetes and Type 2 Diabetes: Exploratory Pilot Randomized Controlled Trial.

JMIR human factors·2026

Same author

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study.

Circulation·2026

Same author

Comparing Stakeholders' Perspectives on Parkinson Disease Management and Digital Technologies: Exploratory International Survey.

JMIR formative research·2026

Same author

Efficacy, Safety, and Tolerability of Atogepant in Migraine Patients from the UAE.

Neurology and therapy·2026

Same author

Applications of artificial intelligence and computational approaches to imaging for hypertension identification, phenotyping, and outcome prediction: a systematic review.

European heart journal. Digital health·2026

Same author

Quantifying emotion-dependent brain-eye interactions during audiovisual emotional stimulation.

Frontiers in systems neuroscience·2026

Same journal

Interpretable Model for Clinical Use in Left Atrial Appendage Segmentation via an Optimised Deformable-Attention U-Net With Spatial-Channel Fusion.

Healthcare technology letters·2026

Same journal

Driving Innovation: Transatlantic Attitudes to the <i>Bionics Bus</i> as a Vehicle for Health Transformation and STEM Engagement.

Healthcare technology letters·2026

Same journal

Gamified Digital Solutions for Tinnitus Health Literacy: The Erasmus+ Project TinWise.

Healthcare technology letters·2026

Same journal

Effect of Technology-Supported Measures Used for Care Transition Decisions for Chronic Disease Patients: A Systematic Review and Meta-Analysis.

Healthcare technology letters·2026

Same journal

Bibliometric Trends in the Integration of Computer Vision With Healthcare.

Healthcare technology letters·2026

Same journal

Parameter-Efficient Deep Learning Models for Vital Sign Estimation From PPG.

Healthcare technology letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 2, 2026

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Enhanced inter-subject brain computer interface with associative sensorimotor oscillations.

Simanto Saha¹, Khawza I Ahmed¹, Raqibul Mostafa¹

¹Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh.

Healthcare Technology Letters

|May 23, 2017

Summary

This summary is machine-generated.

This study introduces wavelet coherence (WC) to select optimal channels for brain-computer interfaces (BCIs). This method improves motor imagery classification accuracy by reducing noise and inter-subject variability in electroencephalography (EEG) data.

Keywords:

BCI EEG actual event related sources associative sensorimotor oscillations biomedical electrodes brain dynamics brain sensorimotor regions brain-computer interfaces classification accuracy cortical events electroencephalography electrophysiological signatures enhanced intersubject brain computer interface handicapped aids high-dimensional electrode montages intersubject variability medical signal processing motor imagery psychophysiological states scalp sensorimotor oscillations signal classification wavelet coherence analysis

More Related Videos

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Related Experiment Videos

Last Updated: Mar 2, 2026

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Electroencephalography (EEG) records brain activity but is susceptible to noise and inter-subject variability.
High-dimensional EEG data can contain outliers that obscure relevant neural signals.
Existing methods may struggle to isolate specific brain dynamics for tasks like motor imagery.

Purpose of the Study:

To develop an optimized EEG channel selection method for improving brain-computer interface (BCI) performance.
To reduce the impact of outliers and inter-subject variability in EEG data.
To enhance the classification accuracy of motor imagery (MI) tasks.

Main Methods:

Proposed a wavelet coherence (WC) analysis for selecting associative inter-subject EEG channels.
Applied the WC method to a dataset from the BCI Competition III (dataset IVa).
Tested the approach on motor imagery (MI) data from five healthy subjects performing hand and foot movements.

Main Results:

Achieved a classification accuracy of 81.79% using 16 WC-selected channels.
This significantly outperformed the 56.79% accuracy obtained using all 118 available channels.
The selected channels were primarily located in sensorimotor regions, consistent with known brain dynamics.

Conclusions:

Optimally selected associative channels effectively reduce outliers and dissimilar cortical patterns.
The WC-based channel selection method significantly boosts the performance of motor imagery-based BCIs.
This approach offers a promising strategy for enhancing the efficiency and real-time capabilities of BCI systems.