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

You might also read

Related Articles

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

Sort by
Same author

Synergy of postural adaptation and exteroception for robust CPG-driven quadrupedal locomotion.

Scientific reports·2026
Same author

Unified three-dimensional bipedal locomotion control via ground reaction force-based joint compliance modulation.

Journal of the Royal Society, Interface·2026
Same author

Modeling Astrocyte-Driven Repair of Visuomotor Deficits in Alzheimer's Thalamic Circuitry.

IEEE transactions on computational biology and bioinformatics·2026
Same author

Human-inspired bipedal locomotion: from neuromechanics to mathematical modelling and robotic applications.

Journal of the Royal Society, Interface·2026
Same author

High Sensitivity Cardiac Troponin I Detection via MP-Locked Aptamer and Multimeric DNAzyme-Coupled Hyperbranched Hybridization Chain Reaction.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Deep learning-based robotic cloth manipulation applications: systematic review, challenges and opportunities for physical AI.

Frontiers in robotics and AI·2026

Related Experiment Video

Updated: Mar 2, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.2K

A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection.

Saugat Bhattacharyya1, Amit Konar2, D N Tibarewala3

  • 1CAMIN Team, INRIA-LIRMM, University of MontpellierMontpellier, France.

Frontiers in Neuroscience
|May 18, 2017
PubMed
Summary

This study introduces a novel method for reliably detecting error feedback signals (ErrP) from electroencephalography (EEG) in brain-computer interfaces (BCI). The system effectively identifies errors across different users and sessions, enhancing BCI rehabilitation applications.

Keywords:
brain-computer interfaceelectroencephalographyensemble classifiererror related potentialtransfer learning

More Related Videos

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

22.0K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.6K

Related Experiment Videos

Last Updated: Mar 2, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.2K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

22.0K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.6K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Brain-computer interfaces (BCI) offer rehabilitative potential but are prone to errors.
  • Error Related Potentials (ErrP) are crucial EEG signals for detecting system inaccuracies.
  • Effective online ErrP detection is vital for closed-loop BCI systems.

Purpose of the Study:

  • To propose a novel scheme for online detection of error feedback directly from EEG signals.
  • To develop a transferable error detection system applicable across different sessions and subjects.
  • To enhance the reliability and real-time feedback capabilities of BCI systems.

Main Methods:

  • Utilized a P300-speller dataset for training and testing.
  • Developed a decoder using an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifiers.
  • Trained the decoder on EEG features from 16 subjects and tested on 10 independent subjects for single-trial classification.

Main Results:

  • Achieved an accuracy of 73.97% in detecting ErrP signals.
  • Obtained an F1-score of 83.53%, indicating robust performance.
  • Reported an Area Under the Curve (AUC) of 73.18% for the classification model.

Conclusions:

  • The proposed scheme demonstrates effective online detection of ErrP signals in a transferable manner.
  • The developed BCI system shows promise for real-time error correction in rehabilitative applications.
  • The ensemble classifier approach provides a reliable method for error detection across subjects and sessions.