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

Mesh Analysis with Current Sources01:10

Mesh Analysis with Current Sources

2.0K
Mesh analysis becomes simpler when analyzing circuits with current sources, whether independent or dependent. The presence of current sources reduces the number of equations required for analysis. Two cases illustrate this:
Current Source in One Mesh: The analysis process is straightforward when a current source is found in only one mesh within the circuit. Mesh currents are assigned as usual, with the mesh containing the current source excluded from the analysis. Kirchhoff's voltage law...
2.0K
Nodal Analysis with Voltage Sources01:11

Nodal Analysis with Voltage Sources

2.0K
Nodal analysis is a remarkably effective method used in electrical engineering to simplify the analysis of complex circuits, including those with dependent or independent voltage sources. Its strength lies in its systematic approach to breaking down circuits into manageable components, making it easier for engineers to understand and solve.
Consider a circuit that contains four resistors and two voltage sources, as shown in Figure 1. One of these voltage sources is connected between a...
2.0K
Sinusoidal Sources01:18

Sinusoidal Sources

1.2K
Direct current (DC) refers to an electric current that flows in a single direction, maintaining a constant polarity. This is in contrast to alternating current (AC), which periodically changes its direction and magnitude. AC forms the backbone of modern electricity transmission and distribution systems due to its efficient long-distance transmission capabilities.
In homes, the power supplies use sinusoidal sources to provide electricity. These sources generate a voltage that varies sinusoidally...
1.2K
AC Sources01:20

AC Sources

4.1K
Direct current is a flow of electric charge in only one direction and has a steady state of constant voltage in the circuit. Rectifiers, batteries, commutator-equipped generators, and fuel cells are some examples of devices that generate direct current. Nowadays, most applications use a time-varying voltage source. Alternating current is a flow of electric charge that periodically reverses direction. An alternating current is produced by an alternating emf that is generated in a power plant. If...
4.1K
Sources of Law01:26

Sources of Law

1.9K
Laws form the essential rules set by governing authorities to shape and control societal behavior. In nursing, laws guide actions, safeguard patient rights, define nurses' scope of practice, and maintain professional standards. Understanding the legal framework governing nursing involves recognizing four primary sources of law: constitutional, statutory, administrative (regulatory), and common law.
Constitutional law is foundational, deriving from federal and state constitutions, and...
1.9K
Source Transformation01:15

Source Transformation

11.8K
Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
11.8K

You might also read

Related Articles

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

Sort by
Same author

brain2print AI powered web tool for creating 3D printable brain models.

Scientific reports·2025
Same author

Open challenges for the automatic synthesis of clinical trials.

BMC research notes·2025
Same author

Grammar-constrained decoding for structured information extraction with fine-tuned generative models applied to clinical trial abstracts.

Frontiers in artificial intelligence·2025
Same author

Efficient federated learning for distributed neuroimaging data.

Frontiers in neuroinformatics·2024
Same author

Brainchop: Providing an Edge Ecosystem for Deployment of Neuroimaging Artificial Intelligence Models.

Aperture neuro·2024
Same author

First look at neutron emission shape characteristics of ignition hotspots at the National Ignition Facility (invited).

The Review of scientific instruments·2024
Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026
Same journal

Novel TMS coils designed using an inverse boundary element method.

Physics in medicine and biology·2026
Same journal

PAC-Net: patch adaptive cut-off network with differentiable module-wise K-learning for robust and efficient medical image segmentation.

Physics in medicine and biology·2026
Same journal

Four-dimensional on-beam computed tomography reconstruction using projection-difference images.

Physics in medicine and biology·2026
Same journal

Higher-order synergy-based ranking in transcriptomic communities via latent factors and O-information.

Physics in medicine and biology·2026
Same journal

Calculating biological dose distributions in hadrontherapy using GATE: the BioDose actor.

Physics in medicine and biology·2026
See all related articles

Related Experiment Video

Updated: Feb 11, 2026

Plasmid Stability Analysis with Open-Source Droplet Microfluidics
07:43

Plasmid Stability Analysis with Open-Source Droplet Microfluidics

Published on: December 27, 2024

1.2K

Probabilistic forward model for electroencephalography source analysis.

Sergey M Plis1, John S George, Sung C Jun

  • 1MS-D454, Applied Modern Physics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. pliz@cs.unm.edu

Physics in Medicine and Biology
|September 1, 2007
PubMed
Summary
This summary is machine-generated.

Accurate electroencephalography (EEG) source localization needs precise head models. Estimating skull conductivity directly from EEG data does not reliably improve source localization or conductivity values.

More Related Videos

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

15.9K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

22.0K

Related Experiment Videos

Last Updated: Feb 11, 2026

Plasmid Stability Analysis with Open-Source Droplet Microfluidics
07:43

Plasmid Stability Analysis with Open-Source Droplet Microfluidics

Published on: December 27, 2024

1.2K
Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

15.9K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

22.0K

Area of Science:

  • Neuroscience
  • Biophysics
  • Medical Imaging

Background:

  • Accurate source localization using electroencephalography (EEG) and magnetoencephalography (MEG) relies on precise head models, including skull conductivity.
  • Magnetic Resonance Imaging (MRI) excels at soft tissue anatomy but lacks skull resolution, while Computed Tomography (CT) provides this but is not routine.
  • Current noninvasive methods for mapping skull conductivity in 3D are insufficient.

Purpose of the Study:

  • To develop a probabilistic forward modeling framework to quantify uncertainties in EEG/MEG source localization.
  • To investigate the feasibility of estimating skull conductivity directly from EEG data.
  • To assess the impact of skull conductivity uncertainties on source localization accuracy.

Main Methods:

  • Introduced a probabilistic forward modeling approach to propagate parameter uncertainties into source localization errors.
  • Explored simultaneous optimization of dipole parameters and skull conductivity values using EEG data.
  • Utilized Cramer-Rao bounds to evaluate the reliability of conductivity estimation and its effect on localization.

Main Results:

  • The proposed probabilistic framework allows for the propagation of uncertainties in head model parameters, such as skull conductivity, into source localization estimates.
  • Simultaneously optimizing for dipole parameters and skull conductivity from EEG data, as previously suggested, did not improve source localization accuracy.
  • This joint optimization approach failed to yield reliable estimates of skull conductivity.

Conclusions:

  • Skull conductivity must be accurately measured independently or uncertainties must be explicitly incorporated into source location estimates.
  • Probabilistic forward modeling is crucial for managing uncertainties in EEG/MEG source localization when precise conductivity values are unavailable.
  • The study highlights limitations in current noninvasive methods for determining skull conductivity and its impact on neuroimaging.