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

Maximum Power Transfer01:16

Maximum Power Transfer

1.1K
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
1.1K
The Maximum Power Transfer Theorem01:20

The Maximum Power Transfer Theorem

1.4K
Consider a linear AC Thevenin equivalent circuit connected to a load impedance.
The load connected draws the current, and the circuit delivers the power to the load. The alternating current flowing through the load is determined using the rectangular form of voltages, currents, network impedance, and load impedance. The average power delivered to the load is obtained from the product of the square of current and load resistance.
1.4K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

700
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
700
Energy and Power Signals01:17

Energy and Power Signals

1.3K
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
1.3K
Multimachine Stability01:25

Multimachine Stability

621
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
621
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

132
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
132

You might also read

Related Articles

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

Sort by
Same author

Behavioral imitation with artificial neural networks leads to personalized models of brain dynamics during videogame play.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Beta bursts in SMA mediate anticipatory muscle inhibition.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same author

Magnetoencephalographic source imaging improves localization of the epileptogenic zone in multimodal imaging evaluation.

Epilepsia·2026
Same author

Estimating Acrylamide and 5-Hydroxymethylfurfural Levels in Crackers Using Computer Vision: Effects on Consumer Acceptance.

Foods (Basel, Switzerland)·2026
Same author

Gamer in the scanner: Event-related analysis of fMRI activity during retro videogame play guided by automated annotations of game content.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

DeepEpiX: A software for visualization, annotation and automatic epileptical spike detection in MEG recordings.

Journal of neuroscience methods·2026

Related Experiment Video

Updated: Mar 22, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.8K

MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters.

Ana-Sofía Hincapié1, Jan Kujala2, Jérémie Mattout3

  • 1Psychology Department, University of Montreal, Montreal, QC, Canada H2V 2S9; Department of Computer Science, Pontificia Universidad Católica de Chile, 7820436 Santiago de Chile, Chile; School of Psychology and Interdisciplinary Center for Neurosciences, Pontificia Universidad Católica de Chile, 7820436 Santiago de Chile, Chile; Lyon Neuroscience Research Center, DyCog Team, Inserm U1028, CNRS UMR5292, 69675 Bron Cedex, France.

Computational Intelligence and Neuroscience
|April 20, 2016
PubMed
Summary

Minimum Norm Estimation (MNE) requires different regularization parameters for power and coherence analysis in magnetoencephalography (MEG). Optimal lambda for coherence is much smaller than for power, suggesting less regularization for coupling measures.

Related Experiment Videos

Last Updated: Mar 22, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.8K

Area of Science:

  • Neuroscience
  • Biophysics
  • Computational Neuroscience

Background:

  • Minimum Norm Estimation (MNE) is a key inverse method for magnetoencephalography (MEG) source reconstruction.
  • Tikhonov regularization is commonly used in MNE, but optimal parameter selection is crucial.
  • Current practice often uses a single regularization parameter for all analyses within a study.

Purpose of the Study:

  • To investigate whether the optimal regularization parameter (lambda) for spectral power analysis differs from that for oscillatory coupling analysis in MEG source data.
  • To determine the impact of source properties on optimal lambda selection for different analysis types.

Main Methods:

  • Extensive Monte-Carlo simulations of magnetoencephalography (MEG) data were performed.
  • 21,600 configurations of coupled sources with varying sizes, SNRs, and coupling strengths were generated.
  • Tikhonov regularization coefficients (lambda) were optimized to maximize detection performance for both power and coherence.

Main Results:

  • The optimal lambda for coherence analysis was found to be two orders of magnitude smaller than for power analysis.
  • Source spatial extent and signal-to-noise ratio (SNR) significantly influenced the optimal lambda choice.
  • The extent of coupling strength did not substantially affect the optimal lambda selection.

Conclusions:

  • Different regularization strategies are necessary for power and coherence analyses in MEG source imaging.
  • Less regularization is recommended for oscillatory coupling analysis compared to power estimation.
  • Optimal lambda selection is dependent on source characteristics like spatial extent and SNR.