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

Feature selection for kernel methods in systems biology.

NAR genomics and bioinformatics·2022
Same author

Wasserstein Adversarial Regularization for Learning With Label Noise.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Greedy Methods, Randomization Approaches, and Multiarm Bandit Algorithms for Efficient Sparsity-Constrained Optimization.

IEEE transactions on neural networks and learning systems·2017
Same author

Optimal Transport for Domain Adaptation.

IEEE transactions on pattern analysis and machine intelligence·2016
Same author

DC Proximal Newton for Nonconvex Optimization Problems.

IEEE transactions on neural networks and learning systems·2015
Same author

Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

Artificial intelligence in medicine·2014
Same journal

Synaptic micromechanics and brain softening as a mechanobiological hypothesis for Alzheimer's disease.

Frontiers in neuroscience·2026
Same journal

The relationship between healthy sleep patterns and the risk of scoliosis: a large prospective cohort study.

Frontiers in neuroscience·2026
Same journal

Dynamic functional reorganization in post-stroke aphasia: a state-of-the-art fMRI review from disease evolution to intervention.

Frontiers in neuroscience·2026
Same journal

Correction: Case Report: A possible novel adult-onset, progressive MAO-A hypofunction.

Frontiers in neuroscience·2026
Same journal

Respiratory modulation of neurophysiology and symptoms in athletes with sports-related concussion: a randomized crossover trial.

Frontiers in neuroscience·2026
Same journal

Impact of C-reactive protein-triglyceride-glucose and systemic immune-inflammation indices on obstructive sleep apnea in older adults with depression.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: May 24, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Decoding Finger Movements from ECoG Signals Using Switching Linear Models.

Rémi Flamary1, Alain Rakotomamonjy

  • 1LITIS EA 4108 - INSA, Université de Rouen Saint Etienne du Rouvray, France.

Frontiers in Neuroscience
|March 13, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel switching model for predicting finger movements from electrocorticography (ECoG) signals. The model achieved high decoding performance, securing second place in the BCI Competition.

Keywords:
ECoGchannel selectionlinear modelswitching model

More Related Videos

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

Corticospinal Excitability Modulation During Action Observation
12:33

Corticospinal Excitability Modulation During Action Observation

Published on: December 31, 2013

Related Experiment Videos

Last Updated: May 24, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

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

Corticospinal Excitability Modulation During Action Observation
12:33

Corticospinal Excitability Modulation During Action Observation

Published on: December 31, 2013

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Brain-machine interfaces (BMIs) aim to translate neural signals into commands for external devices.
  • Electrocorticography (ECoG) offers a promising signal source for high-resolution BMI control.
  • Accurate movement prediction from ECoG signals remains a significant challenge, particularly for complex tasks like individual finger movements.

Purpose of the Study:

  • To develop and evaluate a novel switching model for decoding individual finger movements from ECoG signals.
  • To improve the precision and reliability of movement prediction in ECoG-based BMIs.
  • To address the challenge of non-linear relationships between ECoG signals and finger kinematics.

Main Methods:

  • Utilized a switching model architecture controlled by a hidden state to decode finger flexions.
  • Implemented a two-block system: one block estimates the moving finger, and the second predicts movements of all fingers based on the estimated state.
  • Applied the model to the dataset from the fourth BCI Competition for evaluating decoding performance.

Main Results:

  • The proposed switching model demonstrated high decoding performances, contingent on accurate hidden state estimation.
  • The model achieved a correlation of 0.42 between real and predicted finger movements.
  • This approach secured second place in the BCI Competition, highlighting its effectiveness.

Conclusions:

  • Switching models offer a robust framework for integrating prior knowledge and enhancing the prediction of fine, precise movements in ECoG-based BMIs.
  • The developed model shows significant potential for advancing high-degree precision control in applications like robotic arm and hand operation.
  • Further research into optimizing hidden state estimation could lead to even greater decoding accuracy.