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

Stereotype Content Model02:16

Stereotype Content Model

13.1K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
13.1K
Random and Systematic Errors01:20

Random and Systematic Errors

11.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
11.2K
Classification of Systems-I01:26

Classification of Systems-I

742
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
742
Classification of Systems-II01:31

Classification of Systems-II

651
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
651
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

2.1K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
2.1K
Survival Tree01:19

Survival Tree

499
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
499

You might also read

Related Articles

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

Sort by
Same author

Insights into motor control: predict muscle activity from upper limb kinematics with LSTM networks.

Scientific reports·2026
Same author

Hand Motion Catalog of Human Center-Out Transport Trajectories Measured Redundantly in 3D Task-Space.

Scientific data·2025
Same author

Hybrid brain-computer interface using error-related potential and reinforcement learning.

Frontiers in human neuroscience·2025
Same author

Faster implicit motor sequence learning of new sequences compatible in terms of movement transitions.

NPJ science of learning·2025
Same author

Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots.

Frontiers in neurorobotics·2024
Same author

Human-Robot Intimacy: Acceptance of Robots as Intimate Companions.

Biomimetics (Basel, Switzerland)·2024
Same journal

Role of AQP4 in ameliorating heat stress-induced cellular injury in a cell line model through active heat acclimation.

Frontiers in human neuroscience·2026
Same journal

Correction: Cognitive state monitoring for neuroadaptive information visualization.

Frontiers in human neuroscience·2026
Same journal

The synthetic self-hypothesis: dopaminergic redirection through self-face recognition in stuttering therapy.

Frontiers in human neuroscience·2026
Same journal

A randomised, placebo-controlled, triple-blind clinical trial to investigate the efficacy of <i>Ginkgo biloba</i> extract EGb 761<sup>®</sup> in cognitive impairment associated with post COVID-19 syndrome-the EGb COCOS protocol.

Frontiers in human neuroscience·2026
Same journal

Examining the independent and combined effects of autistic and ADHD traits on multisensory integration.

Frontiers in human neuroscience·2026
Same journal

Prediction of hormone receptor status in breast cancer brain metastases using an MRI-based multimodal deep learning framework.

Frontiers in human neuroscience·2026
See all related articles

Related Experiment Video

Updated: May 1, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

A generic error-related potential classifier based on simulated subjects.

Aline Xavier Fidêncio1,2,3, Christian Klaes3, Ioannis Iossifidis2

  • 1Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum, Bochum, Germany.

Frontiers in Human Neuroscience
|August 1, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed a universal classifier for error-related potentials (ErrPs), brain signals indicating errors. This approach bypasses lengthy calibration, offering a more efficient brain-computer interface (BCI) solution.

Keywords:
EEGErrP classifierSEREEGAadaptive brain-machine (computer) interfaceerror-related potential (ErrP)generic decodersimulation

More Related Videos

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

553
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

2.3K

Related Experiment Videos

Last Updated: May 1, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

553
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

2.3K

Area of Science:

  • Neuroscience
  • Brain-Computer Interfaces (BCI)
  • Machine Learning

Background:

  • Error-related potentials (ErrPs) are non-invasive brain signals elicited by erroneous events, including those caused by external agents.
  • BCI research utilizes ErrPs for performance enhancement, such as error correction, but requires extensive, time-consuming subject-specific calibration.
  • Existing calibration methods are tedious for participants and limit the practical application of ErrP-based BCIs.

Purpose of the Study:

  • To investigate the effectiveness of ErrPs in closed-loop BCI systems, focusing on precise single-trial classification.
  • To develop a generic ErrP classifier that generalizes across subjects and datasets, eliminating the need for extensive calibration.
  • To utilize simulated data with the SEREEGA toolbox for systematic validation of ErrP detection and classifier performance.

Main Methods:

  • Utilized the open-source SEREEGA toolbox to simulate data, ensuring the presence of ErrP signals and systematic variation of parameters.
  • Generated training instances from simulated data and evaluated a generic support vector machine (SVM) classifier on both simulated and real-world datasets.
  • Compared the performance of the generic classifier against a leave-one-subject-out approach for error class detection.

Main Results:

  • The generic SVM classifier achieved balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% across different validation datasets.
  • The proposed classifier demonstrated performance comparable to subject-specific methods while showing significant generalization capabilities without adaptation.
  • SEREEGA facilitated systematic parameter adjustment for ErrP variability, enabling robust validation of closed-loop BCI setups.

Conclusions:

  • A generic ErrP classifier offers a promising alternative to conventional, lengthy calibration techniques in BCI applications.
  • The developed classifier exhibits effective generalization across diverse datasets and subjects, paving the way for universal ErrP detection.
  • Future work aims to refine this universal classifier to accurately detect ErrPs in real EEG data, enhancing BCI efficiency and user experience.