Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

¹H NMR Signal Multiplicity: Splitting Patterns

¹H NMR Signal Multiplicity: Splitting Patterns

When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...

Design Example

Design Example

The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...

Frequency Response of a Circuit

Frequency Response of a Circuit

Inductive circuits present intriguing challenges in electrical engineering, particularly during the transition from the time domain to the frequency domain. This transformation involves converting inductors into impedances and utilizing phasor representation.
The transfer function is pivotal in characterizing how these circuits react to various frequencies, facilitating a profound understanding of their behavior. An essential parameter is the time constant, signifying the...

NMR Spectroscopy: Spin–Spin Coupling

NMR Spectroscopy: Spin–Spin Coupling

The spin state of an NMR-active nucleus can have a slight effect on its immediate electronic environment. This effect propagates through the intervening bonds and affects the electronic environments of NMR-active nuclei up to three bonds away; occasionally, even farther. This phenomenon is called spin–spin coupling or J-coupling. Coupling interactions are mutual and result in small changes in the absorption frequencies of both nuclei involved. While nuclei of the same element are involved...

Cross Product

Cross Product

The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.

You might also read

Related Articles

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

Sort by

Same author

A phenomenological multiscale framework for orientational interactions and viscoelasticity in migrating epithelial monolayers.

Bio Systems·2026

Same author

Cardiorespiratory Dynamics as a Non-Autonomous System of Coupled Oscillators with Time-Varying Frequency Modulation.

Entropy (Basel, Switzerland)·2026

Same author

Irregular curvature at focal adhesions modulates Piezo1 activity and low-frequency ultrasound-induced apoptosis in cancer cells.

Physics of life reviews·2026

Same author

Marangoni-driven redistribution and activity of Piezo1 molecules in epithelial and cancer cells.

Advances in colloid and interface science·2026

Same author

Modelling brain metabolism with interacting nonautonomous phase oscillators.

Frontiers in network physiology·2026

Same author

Anisotropy and shear stress accumulation during collective migration of epithelial cells.

European biophysics journal : EBJ·2026

Search research articles

Related Experiment Video

Updated: Feb 27, 2026

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

Neural Cross-Frequency Coupling Functions.

Tomislav Stankovski^1,2, Valentina Ticcinelli¹, Peter V E McClintock¹

¹Nonlinear and Biomedical Physics Group, Department of Physics, Lancaster UniversityLancaster, United Kingdom.

Frontiers in Systems Neuroscience

|July 1, 2017

Summary

We introduce dynamical Bayesian inference to quantify neural coupling functions, revealing distinct patterns between eyes-open and eyes-closed states. This method offers new insights into brain oscillations and potential disease mechanisms.

Keywords:

EEG coupling function cross-frequency coupling dynamical Bayesian inference effective connectivity eyes-open neural oscillations resting brain

More Related Videos

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

Related Experiment Videos

Last Updated: Feb 27, 2026

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Neural interactions are typically defined by coupling strength and directionality.
Understanding the functional mechanisms of neural interactions requires advanced analytical methods.
Existing methods often fall short in characterizing the complex dynamics of neural coupling.

Purpose of the Study:

To introduce and validate a novel method for estimating neural coupling functions using dynamical Bayesian inference.
To quantify the strength and form of δ-to-α phase-to-phase neural coupling from electroencephalographic (EEG) data.
To investigate differences in neural coupling functions between eyes-open (EO) and eyes-closed (EC) resting states.

Main Methods:

Dynamical Bayesian inference was employed to estimate coupling functions from noisy neural oscillation data.
Coupling was decomposed into functional components, quantifying strength and form.
Phase-shuffled surrogates were used for statistical significance testing of coupling characteristics.

Main Results:

δ-to-α phase-to-phase coupling functions were successfully reconstructed, quantified, and analyzed over time.
The strength and variability of coupling functions significantly differed between EO and EC states across brain regions.
Direct coupling was confirmed to be stronger during EC, with significantly reduced coupling function variability.

Conclusions:

Dynamical Bayesian inference provides a robust framework for characterizing neural coupling functions beyond simple strength and directionality.
The study reveals distinct neural coupling dynamics associated with visual input (EO vs. EC states).
This approach holds promise for advancing our understanding of neural mechanisms in neurological diseases affecting δ and α brainwaves.