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

Brain Waves01:23

Brain Waves

4.1K
Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:
4.1K
Moment of Inertia about an Arbitrary Axis01:20

Moment of Inertia about an Arbitrary Axis

641
The moment of inertia is typically associated with principal axes, but it can also be computed for any random axis. When an arbitrary axis is under consideration, the moment of inertia is determined by integrating the mass distribution of the object along that specific axis. It is crucial in applications like the design of machinery, where components rotate about various axes, and balance and stability are essential.
In this scenario, the perpendicular distance between the chosen arbitrary axis...
641
Angular Momentum about an Arbitrary Axis01:11

Angular Momentum about an Arbitrary Axis

465
Imagine a rigid body with a mass denoted as 'm', which has its center of mass at point G and is rotating around an inertial reference frame. The angular momentum at an arbitrary point P can be calculated by taking the cross product of the position vector and linear momentum vector for each individual mass element.
The velocity of a mass element comprises its translational velocity and the relative velocity instigated by the body's rotation. Substituting the velocity equation into...
465
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.7K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
14.7K
Organization of the Brain01:30

Organization of the Brain

2.7K
The brain is an integral component of the nervous system and serves as the center for processing sensory inputs, making decisions, and directing bodily actions. This complex organ is organized into three primary sections: the hindbrain, midbrain, and forebrain, each responsible for a range of vital functions.
Hindbrain
The hindbrain, located at the base of the brain, plays a vital role in regulating automatic processes that sustain life. It includes the medulla oblongata, which is essential for...
2.7K
Brain Imaging01:14

Brain Imaging

743
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
743

You might also read

Related Articles

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

Sort by
Same author

Discriminative Index: A Novel Indicator for Evaluating Machine Learning Algorithms in Laboratory Medicine.

Diagnostics (Basel, Switzerland)·2026
Same author

Fully Automated Subtraction of Heart Activity for Fetal Magnetoencephalography Data<sup>.</sup>

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same author

World's fastest brain-computer interface: Combining EEG2Code with deep learning.

PloS one·2019
Same author

Asynchronous non-invasive high-speed BCI speller with robust non-control state detection.

Scientific reports·2019
Same author

Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data.

Computer methods and programs in biomedicine·2019
Same author

Questioning the evidence for BCI-based communication in the complete locked-in state.

PLoS biology·2019

Related Experiment Video

Updated: Feb 3, 2026

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation
08:50

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Published on: August 20, 2019

15.1K

Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed Brain-Computer

Sebastian Nagel1, Martin Spüler1

  • 1Department of Computer Engineering, Wilhelm-Schickard-Institute for Computer Science, University of Tübingen, 72076 Tübingen, Germany.

Plos One
|October 23, 2018
PubMed
Summary

This study introduces a novel method to predict brain responses to any visual stimulus using electroencephalography (EEG). This approach enhances brain-computer interface (BCI) speed and flexibility.

More Related Videos

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

958
Excitotoxic Stimulation of Brain Microslices as an In vitro Model of Stroke
07:00

Excitotoxic Stimulation of Brain Microslices as an In vitro Model of Stroke

Published on: February 4, 2014

8.9K

Related Experiment Videos

Last Updated: Feb 3, 2026

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation
08:50

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Published on: August 20, 2019

15.1K
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

958
Excitotoxic Stimulation of Brain Microslices as an In vitro Model of Stroke
07:00

Excitotoxic Stimulation of Brain Microslices as an In vitro Model of Stroke

Published on: February 4, 2014

8.9K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Visual evoked potentials (VEPs) are EEG signals reflecting brain responses to visual stimuli.
  • Traditional VEP analysis relies on averaging responses or classifiers for predefined patterns.
  • Existing methods are limited in flexibility and the range of stimulation patterns they can handle.

Purpose of the Study:

  • To develop a generalizable method for modeling VEP generation.
  • To predict brain responses to arbitrary visual stimulation patterns from EEG.
  • To enhance the speed and flexibility of VEP-based brain-computer interfaces (BCIs).

Main Methods:

  • Developed a model for the general process of VEP generation.
  • Applied the model to predict responses to single-flash, steady-state (SSVEPs), and complex visual stimuli.
  • Evaluated the method in an online BCI scenario and through offline analysis.

Main Results:

  • The method successfully models various VEP types, including complex patterns.
  • Achieved an average information transfer rate (ITR) of 108.1 bit/min in an online BCI.
  • Demonstrated theoretical ITR potential exceeding 470 bit/min with flexible target modulation.

Conclusions:

  • The proposed method offers a flexible and powerful approach to VEP analysis.
  • It significantly advances the capabilities of VEP-based BCIs, enabling higher speeds and more complex interactions.
  • This modeling technique opens new possibilities for understanding brain responses to visual stimuli.