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

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

725
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
725
Visual System01:26

Visual System

1.5K
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
1.5K
Convolution Properties II01:17

Convolution Properties II

496
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
496
Vision01:24

Vision

59.0K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
59.0K
Neural Circuits01:25

Neural Circuits

2.4K
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...
2.4K
Classification of Systems-I01:26

Classification of Systems-I

482
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
482

You might also read

Related Articles

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

Sort by
Same author

An Electrospinography Database of Gait-Related Tasks and Motor Imagery Exercises.

Scientific data·2026
Same author

Deep learning for high-resolution material texture enhancement in 3D environments.

Scientific reports·2026
Same author

Denoising Non-Invasive Electroespinography Signals by Different Cardiac Artifact Removal Algorithms.

Biosensors·2026
Same author

Impact of meningioma and glioma on whole-brain dynamics.

Scientific reports·2026
Same author

Disrupted Brain Hierarchical Organization in Alzheimer's Disease Progression.

medRxiv : the preprint server for health sciences·2025
Same author

Characterization of error-related potentials during the command of a lower-limb exoskeleton based on deep learning.

Journal of neuroengineering and rehabilitation·2025
Same journal

In radiation oncology, the best way to maintain patient safety when implementing cutting edge technologies such as AI-based and real-time adaptive techniques is through prospective hazard analysis.

Physical and engineering sciences in medicine·2026
Same journal

Compact neural network algorithm for electrocardiogram classification.

Physical and engineering sciences in medicine·2026
Same journal

Fat-suppression performance for in-stent plaque imaging after carotid artery stenting using three-dimensional T<sub>1</sub>-weighted MRI: a phantom study.

Physical and engineering sciences in medicine·2026
Same journal

Deep learning based depth of anaesthesia monitoring using EEG: a 4-layer CNN model with PSD and BSR correlation features.

Physical and engineering sciences in medicine·2026
Same journal

Design and implementation of an automated quality assurance tool for Hounsfield unit-to-relative electron density calibration in cone beam computed tomography imaging.

Physical and engineering sciences in medicine·2026
Same journal

Complementary roles of GPU-accelerated Monte Carlo and ArcCHECK in TomoTherapy quality assurance.

Physical and engineering sciences in medicine·2026
See all related articles

Related Experiment Video

Updated: Dec 15, 2025

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

2.2K

Convolutional neural networks and genetic algorithm for visual imagery classification.

Fabio R Llorella1, Gustavo Patow2, José M Azorín3

  • 1ViRVIG-UdG. Universitat de Girona, Girona, Spain. fricardocorreo@gmail.com.

Physical and Engineering Sciences in Medicine
|July 15, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Brain-Computer Interface (BCI) technique using visual imagery and neural networks. The system achieved a 60% success rate in classifying imagined objects, advancing BCI technology.

Keywords:
Brain–Computer InterfaceDeep learningGenetic algorithmsKerasVisual imagery

More Related Videos

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

737
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.8K

Related Experiment Videos

Last Updated: Dec 15, 2025

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

2.2K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

737
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.8K

Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Brain-Computer Interface (BCI) systems enable direct brain-to-world communication.
  • Existing BCIs predominantly rely on evoked potentials or motor imagery.
  • A need exists for alternative BCI paradigms, such as those utilizing visual imagery.

Purpose of the Study:

  • To develop and evaluate a novel BCI technique based on visual imagery.
  • To utilize neural networks for classifying brain signals generated during visual imagination.
  • To compare the performance of this visual imagery BCI against existing state-of-the-art methods.

Main Methods:

  • Employed densely connected neural networks and convolutional neural networks.
  • Utilized a genetic algorithm for optimizing neural network parameters.
  • Trained and tested the system on classifying brain signals corresponding to four imagined objects and a relaxation state.

Main Results:

  • Achieved a 60% success rate in classifying five distinct mental states (four objects + relaxation).
  • Demonstrated superior performance compared to previous benchmarks in visual imagery classification.
  • Successfully classified neural signals associated with imagining a tree, dog, airplane, and house.

Conclusions:

  • Visual imagery-based BCIs are a viable alternative to traditional methods.
  • Advanced neural network architectures can effectively decode visual imagery signals.
  • This approach shows significant potential for enhancing BCI applications and accessibility.