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

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

3.0K
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
3.0K
Brain Imaging01:14

Brain Imaging

1.0K
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Curated endoscopic retrograde cholangiopancreatography images dataset.

Scientific data·2026
Same author

Visual electrophysiology in carotid endarterectomy: a systematic review.

International journal of retina and vitreous·2026
Same author

Relationship Between Scheimpflug-Based Ocular Biomechanics and Myopia Progression in Adolescents.

Bioengineering (Basel, Switzerland)·2026
Same author

Therapy Optimization in mHSPC: Insights from a Multidisciplinary Uro-Oncology Expert Panel.

Advances in therapy·2026
Same author

Diagnostic Agreement Between a General-Purpose AI Model and Retinal Specialists in Color Fundus Photography-A Pilot Study.

Journal of clinical medicine·2026
Same author

Changes in Ocular Biomechanics During Adolescence and Its Relationship with Lifestyle and Myopic Progression: The Oporto Myopia Study.

Bioengineering (Basel, Switzerland)·2026

Related Experiment Video

Updated: Apr 18, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.3K

Adaptive BCI based on software agents.

Javier Castillo-Garcia, Anibal Cotrina, Alessandro Benevides

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces adaptive Brain-Computer Interfaces (BCIs) using software agents for feature reduction and classifier selection. The adaptive BCI achieved a 93% success rate with minimal EEG channels and features.

    More Related Videos

    Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
    06:11

    Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

    Published on: April 18, 2025

    2.1K
    Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
    07:14

    Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

    Published on: December 23, 2025

    921

    Related Experiment Videos

    Last Updated: Apr 18, 2026

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    5.3K
    Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
    06:11

    Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

    Published on: April 18, 2025

    2.1K
    Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
    07:14

    Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

    Published on: December 23, 2025

    921

    Area of Science:

    • Neuroscience
    • Computer Science
    • Biomedical Engineering

    Background:

    • Feature selection and modeling BCI topology are challenging due to brain signal variability.
    • Developing user-independent BCI systems with optimal features requires innovative approaches.

    Purpose of the Study:

    • To propose a novel method for feature reduction and classifier selection in adaptive Brain-Computer Interfaces (BCIs).
    • To determine an optimal BCI topology that accommodates user variability and minimizes feature requirements.

    Main Methods:

    • Utilized software agents incorporating Genetic Algorithms (GA) for feature reduction and classifier selection.
    • Employed entropy and mutual information within GA to select the optimal number of features.
    • Defined a cost function using success rate and Cohen's Kappa coefficient to evaluate classifier performance.

    Main Results:

    • Developed an adaptive BCI model demonstrating interrelation between channel count, features, and classifier.
    • Achieved feature reduction and optimal classifier selection through the proposed adaptive BCI approach.
    • Attained a 93% success rate using only three EEG channels on the BCI Competition III dataset IVa.

    Conclusions:

    • The adaptive BCI framework effectively addresses challenges in feature selection and user variability.
    • The proposed method identifies a minimal feature subset and optimal classifier for robust BCI performance.
    • This approach enables efficient and high-performing BCIs with reduced data requirements.