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

Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
Data Collection by Experiments01:13

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...
Crossover Experiments01:16

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Open and closed-loop control systems01:17

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...

You might also read

Related Articles

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

Sort by
Same author

A mosaic of whole-body representations on the human precentral gyrus.

Nature·2026
Same author

Stereotactic electroencephalogram lead placement in patient with hemophilia B: illustrative case.

Journal of neurosurgery. Case lessons·2026
Same author

Long-term independent use of an intracortical brain-computer interface for speech and cursor control.

Nature medicine·2026
Same author

Neural decoding of speech using deep neural ensembles.

bioRxiv : the preprint server for biology·2026
Same author

Muscle-driven hand simulations emphasize the critical role of the extensor mechanism.

bioRxiv : the preprint server for biology·2026
Same author

A framework for quantifying the mechanics of dexterous grasp.

bioRxiv : the preprint server for biology·2026
Same journal

Cortex-anchored sensor-space harmonics for event-related EEG.

Journal of neural engineering·2026
Same journal

Neural mechanisms of mixed speech and grasp representation in sensorimotor cortices.

Journal of neural engineering·2026
Same journal

Developing a binary communication protocol between biological neural networks using virtual white matter.

Journal of neural engineering·2026
Same journal

Spatiotemporally distinctive astrocytic and neuronal responses to repetitive intracortical microstimulation.

Journal of neural engineering·2026
Same journal

A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Journal of neural engineering·2026
Same journal

Functional and effective connectivity methods from SEEG for characterizing epileptogenic networks in refractory epilepsy: a comprehensive review and future directions.

Journal of neural engineering·2026
See all related articles

Related Experiment Video

Updated: Jun 21, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.7K

BRAND: a platform for closed-loop experiments with deep network models.

Yahia H Ali1, Kevin Bodkin2, Mattia Rigotti-Thompson1

  • 1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States of America.

Journal of Neural Engineering
|April 5, 2024
PubMed
Summary
This summary is machine-generated.

The new Backend for Realtime Asynchronous Neural Decoding (BRAND) enables fast, language-agnostic integration of artificial neural networks (ANNs) for real-time brain-computer interfaces. This system achieves low latency, facilitating advanced neuroscience and machine learning applications in closed-loop experiments.

Keywords:
artificial neural networkbrain–computer interfaceclosed-loopreal-time

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.0K

Related Experiment Videos

Last Updated: Jun 21, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.7K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.0K

Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Artificial neural networks (ANNs) are powerful for neural decoding but face deployment challenges in real-time systems.
  • Existing frameworks struggle to support high-level ANNs (Python, Julia) alongside low-latency acquisition languages (C, C++).

Purpose of the Study:

  • Introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND) to bridge the gap between ANNs and real-time experimental needs.
  • Enable seamless integration of advanced machine learning models into closed-loop neuroscience research.

Main Methods:

  • BRAND utilizes a Linux process-based architecture with nodes communicating via data streams in a graph.
  • An asynchronous design allows parallel execution of acquisition, control, and analysis across different timescales.
  • Redis facilitates fast inter-process communication, supporting 54 programming languages for flexible ANN deployment.

Main Results:

  • BRAND demonstrated inter-process latency under 600 microseconds for high-throughput neural data.
  • A brain-computer interface using BRAND achieved <8 ms latency for recurrent neural network (RNN) decoding.
  • Successfully operated a cursor control task in a clinical trial, integrating signal processing, RNN decoding, and task control.

Conclusions:

  • BRAND provides a fast, modular, and language-agnostic framework for real-time neuroscience experiments.
  • Lowers integration barriers for cutting-edge machine learning and neuroscience tools.
  • Facilitates advanced applications like real-time inference with complex models such as Latent Factor Analysis via Dynamical Systems.