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

Intracranially injected chimeric antigen receptor T cells eradicate glioblastoma cells but have limited potential to persist in the brain in a syngeneic mouse model.

Cancer immunology, immunotherapy : CII·2026
Same author

Development and validation of a machine learning model for predicting intracranial atherosclerotic disease in large vessel occlusion prior to endovascular therapy.

Clinical neurology and neurosurgery·2026
Same author

Ruptured cerebral arteriovenous malformation associated with enlarged parietal foramina: a rare surgical case with pathogenetic implications.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery·2026
Same author

CTA-derived biplane 3D roadmap fusion for catheter navigation in spinal angiography.

Journal of neuroradiology = Journal de neuroradiologie·2026
Same author

Hitting the Sulcus With a More Tangential Angle May Result in Resistance Felt During Implantation of Stereotactic Electroencephalography Electrodes.

Neurosurgery practice·2026
Same author

Biplane 3D roadmap-guided traversal of an occluded sinus for transvenous embolization of a TS-SS/marginal sinus dAVF.

Journal of neurointerventional surgery·2026

Related Experiment Video

Updated: Dec 24, 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

43.9K

Neural decoding of electrocorticographic signals using dynamic mode decomposition.

Yoshiyuki Shiraishi1, Yoshinobu Kawahara, Okito Yamashita

  • 1Osaka University, Institute for Advanced Co-Creation Studies, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.

Journal of Neural Engineering
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

Dynamic Mode Decomposition (DMD) improved brain-computer interface (BCI) accuracy by decoding electrocorticographic (ECoG) signals using spatiotemporal patterns. This advancement enhances communication for paralyzed patients.

More Related Videos

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

5.9K
Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

26.6K

Related Experiment Videos

Last Updated: Dec 24, 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

43.9K
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

5.9K
Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

26.6K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-computer interfaces (BCIs) utilizing electrocorticographic (ECoG) signals aim to restore communication for severely paralyzed individuals.
  • Current ECoG-based BCIs face limitations due to the restricted information extracted from signals, hindering clinical translation.
  • Decoding motor intentions from ECoG signals often relies on frequency band powers, which may not fully capture complex neural dynamics.

Purpose of the Study:

  • To develop and evaluate a novel method for decoding ECoG signals by analyzing spatiotemporal patterns.
  • To enhance the amount of information extracted from ECoG signals for improved BCI performance.
  • To investigate the efficacy of Dynamic Mode Decomposition (DMD) in capturing movement-related neural activity.

Main Methods:

  • ECoG signals from 11 patients undergoing hand movements were analyzed.
  • Dynamic Mode Decomposition (DMD) was employed to extract spatiotemporal patterns (DMD modes) from ECoG signals.
  • Support Vector Machine (SVM) classifier was used for decoding movement types based on DMD modes and Fast Fourier Transform (FFT) powers.
  • Grassmann kernel was utilized to assess the distinctiveness of DMD modes.

Main Results:

  • Decoding accuracy using DMD modes significantly surpassed that achieved with FFT powers.
  • Classification accuracy substantially decreased when the phase components of DMD modes were randomized, confirming the importance of phase information.
  • The DMD mode around 100 Hz exhibited the highest classification accuracy among analyzed frequency bands.

Conclusions:

  • DMD effectively captures complex spatiotemporal dynamics in ECoG signals, crucial for characterizing different movement types.
  • The proposed DMD-based approach significantly improves decoding accuracy in BCIs.
  • This method holds promise for enhancing communication capabilities for severely paralyzed patients through advanced BCIs.