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

Connectome quality converges predictably to reveal optimal stopping points during proofreading.

bioRxiv : the preprint server for biology·2026
Same author

Association between motor cortex grey matter loss and inability to control an ECoG-based implanted Brain-Computer Interface in ALS.

medRxiv : the preprint server for health sciences·2026
Same author

A computational framework for fitting biophysical basal-ganglia network models, applied to Parkinsonian beta oscillations.

Journal of neural engineering·2026
Same author

Electrocorticography During Deep Brain Stimulation Surgery for Movement Disorders: Single-Center Experience.

Brain sciences·2026
Same author

Physical contact reveals a hidden layer of cortical architecture.

bioRxiv : the preprint server for biology·2026
Same author

Across-speaker articulatory reconstruction from sensorimotor cortex for generalizable brain-computer interfaces.

Journal of neural engineering·2026

Related Experiment Video

Updated: Jul 12, 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

43.4K

Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months.

Shiyu Luo1, Miguel Angrick2, Christopher Coogan2

  • 1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|October 24, 2023
PubMed
Summary

This study shows a brain-computer interface (BCI) using electrocorticography (ECoG) implants allows an ALS patient to control computers with speech commands. The BCI demonstrated high accuracy and reliability over three months without retraining.

Keywords:
amyotrophic lateral sclerosis (ALS)brain-computer interfacesneural decodingspeech brain-computer interface (BCI)

More Related Videos

Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis
07:00

Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis

Published on: October 13, 2016

8.2K
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

611

Related Experiment Videos

Last Updated: Jul 12, 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

43.4K
Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis
07:00

Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis

Published on: October 13, 2016

8.2K
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

611

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Neurological disorders like ALS severely impair communication and mobility.
  • Brain-computer interfaces (BCIs) offer a potential solution for assistive control.
  • Accurate, reliable, and low-setup BCI systems are crucial for patient independence.

Purpose of the Study:

  • To evaluate the long-term accuracy and reliability of an electrocorticography (ECoG)-based speech BCI for assistive control.
  • To assess the feasibility of a self-paced, unassisted BCI system for individuals with severe dysarthria due to ALS.

Main Methods:

  • A participant with ALS used a chronically implanted ECoG device over the ventral sensorimotor cortex.
  • Six intuitive speech commands were decoded to operate computer applications.
  • System performance was monitored over a 3-month period without retraining or recalibration.

Main Results:

  • The ECoG-based speech BCI achieved high accuracy in detecting and decoding speech commands (median 90.59%).
  • The system operated reliably over 3 months, demonstrating consistent performance.
  • The BCI enabled self-paced, unassisted control without requiring external timing cues.

Conclusions:

  • Chronically implanted ECoG-based speech BCIs can provide reliable assistive control for extended periods.
  • Minimal retraining and calibration support the feasibility of unassisted home use for ALS patients.
  • This technology holds promise for restoring communication and control for individuals with severe motor impairments.