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

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

1.1K
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
1.1K
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

1.3K
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
1.3K
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

750
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
750
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

918
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
918
Chebyshev's Theorem to Interpret Standard Deviation01:15

Chebyshev's Theorem to Interpret Standard Deviation

4.4K
Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
4.4K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

2.0K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Low-rank tensor decomposition for cross-bispectral analysis of EEG data.

Journal of neuroscience methods·2026
Same author

Source to sensor coupling (SoSeC) as an effective tool to localize interacting sources from EEG and MEG data.

Journal of neuroscience methods·2025
Same author

Predictive role of endogenous phase lags between target brain regions in dual-site transcranial alternating current stimulation.

Brain stimulation·2025
Same author

Causal interactions between amplitude correlation and phase coupling in cortical networks.

Scientific reports·2025
Same author

Dynamic changes in large-scale functional connectivity prior to stimulation determine performance in a multisensory task.

Frontiers in systems neuroscience·2025
Same author

Semi-analytic three-shell forward calculation for magnetoencephalography.

NeuroImage·2024

Related Experiment Video

Updated: Apr 27, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.0K

Univariate normalization of bispectrum using Hölder's inequality.

Forooz Shahbazi1, Arne Ewald2, Guido Nolte3

  • 1Fraunhofer Institute FOKUS, Kaiserin Augusta Allee. 31, 10589 Berlin, Germany; Technische Universität Berlin, Machine Learning Group, Marchstr.23, 10587 Berlin, Germany.

Journal of Neuroscience Methods
|July 1, 2014
PubMed
Summary

A new univariate normalization method for cross-bispectrum analysis was developed. This method ensures bounded bicoherence values, crucial for studying complex biological systems like the brain.

More Related Videos

How to Create and Use Binocular Rivalry
14:34

How to Create and Use Binocular Rivalry

Published on: November 10, 2010

78.0K
Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex
08:42

Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex

Published on: February 8, 2020

10.4K

Related Experiment Videos

Last Updated: Apr 27, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.0K
How to Create and Use Binocular Rivalry
14:34

How to Create and Use Binocular Rivalry

Published on: November 10, 2010

78.0K
Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex
08:42

Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex

Published on: February 8, 2020

10.4K

Area of Science:

  • Neuroscience
  • Complex Systems Analysis
  • Signal Processing

Background:

  • Biological systems, including the brain, are complex non-linear systems.
  • Studying their dynamics requires methods that detect non-linearities.
  • Cross-bispectrum (third-order cumulant) measures interfrequency interactions between signals.

Purpose of the Study:

  • To propose a novel univariate normalization factor for cross-bispectra.
  • To ensure the normalized measure (bicoherence) is bounded between zero and one.
  • To compare the statistical significance of this new normalization against existing methods.

Main Methods:

  • Development of a univariate normalization factor for cross-bispectra.
  • Mathematical proof using a generalization of Hölder's inequality to establish bounds.
  • Comparison with three existing normalizations using resampling tests on real EEG data.

Main Results:

  • The proposed univariate normalization ensures bicoherence values are bounded between 0 and 1.
  • Statistical significance of bicoherence values showed slight improvements with univariate normalization.
  • Differences in significance were minimal or negligible across subjects.

Conclusions:

  • The normalization factor plays a minor role in the statistical power of bicoherence values.
  • Univariate normalization is the only method satisfying all criteria for proper normalization.
  • This method is suitable for analyzing non-linear dynamics in biological systems.