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

Learning from small datasets-review of workshop 6 of the 10th International BCI Meeting 2023.

Journal of neural engineering·2025
Same author

Influence of pitch modulation on event-related potentials elicited by Dutch word stimuli in a brain-computer interface language rehabilitation task.

Journal of neural engineering·2025
Same author

Dareplane: a modular open-source software platform for BCI research with application in closed-loop deep brain stimulation.

Journal of neural engineering·2025
Same author

Review of deep representation learning techniques for brain-computer interfaces.

Journal of neural engineering·2024
Same author

Aphasia recovery by language training using a brain-computer interface: a proof-of-concept study.

Brain communications·2022
Same author

Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation.

Brain computer interfaces (Abingdon, England)·2020

Related Experiment Video

Updated: Aug 24, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential

Jan Sosulski1, Michael Tangermann2

  • 1Department of Computer Science, University of Freiburg, Freiburg, Germany.

Journal of Neural Engineering
|October 21, 2022
PubMed
Summary

New ToeplitzLDA method improves brain-computer interface (BCI) performance by enhancing electroencephalogram (EEG) signal analysis. This approach boosts classification accuracy and reduces errors in event-related potential (ERP) based BCIs.

Keywords:
block-Toeplitz matrixbrain signal classificationhigh dimensional covariance estimationlinear discriminant analysisspatiotemporal data

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K
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

649

Related Experiment Videos

Last Updated: Aug 24, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K
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

649

Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Covariance matrices from electroencephalogram (EEG) time series are crucial for brain signal decoding using machine learning.
  • Estimating these matrices is challenging due to small datasets and high dimensionality in EEG data.
  • Current state-of-the-art for event-related potential (ERP) based brain-computer interfaces (BCI) uses shrinkage regularization for covariance estimation in linear discriminant analysis (LDA).

Purpose of the Study:

  • To improve linear discriminant analysis (LDA) for electroencephalogram (EEG) based brain-computer interfaces (BCI) by developing a more effective covariance estimation method.
  • To leverage domain-specific characteristics of EEG data to enhance covariance regularization beyond general approaches.
  • To introduce a block-Toeplitz structure for covariance matrices in LDA to exploit signal stationarity assumptions.

Main Methods:

  • Proposed a novel 'ToeplitzLDA' method enforcing a block-Toeplitz structure on the covariance matrix for LDA.
  • Assumed signal stationarity within short time windows for each EEG channel to justify the block-Toeplitz structure.
  • Conducted offline re-analysis of data from 213 subjects across 13 different ERP BCI protocols.

Main Results:

  • ToeplitzLDA significantly increased binary classification performance compared to shrinkage regularized LDA (up to 6 AUC points) and Riemannian classification (up to 2 AUC points).
  • In an unsupervised visual speller task, this method led to an average 81% relative reduction in spelling errors for 25 subjects.
  • ToeplitzLDA demonstrated lower memory and reduced time complexity for LDA training and robustness against increased temporal features.

Conclusions:

  • The proposed block-Toeplitz covariance estimation method enhances classification rates in ERP-based BCI protocols.
  • This approach can reduce calibration times for BCI applications, improving overall usability.
  • ToeplitzLDA's reduced computational and memory requirements make it particularly suitable for mobile BCI systems.