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

You might also read

Related Articles

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

Sort by
Same author

Disrupted Causal Connectivity Anchored in the Posterior Cingulate Cortex in Amnestic Mild Cognitive Impairment.

Frontiers in neurology·2017
Same author

The Triple Functions of D2 Silencing in Treatment of Periapical Disease.

Journal of endodontics·2017
Same author

Excellent Thermoelectric Properties in monolayer WSe<sub>2</sub> Nanoribbons due to Ultralow Phonon Thermal Conductivity.

Scientific reports·2017
Same author

Activation of Akt by SC79 protects myocardiocytes from oxygen and glucose deprivation (OGD)/re-oxygenation.

Oncotarget·2017
Same author

ColorSketch: A Drawing Assistant for Generating Color Sketches from Photos.

IEEE computer graphics and applications·2017
Same author

Enclosure Transform for Interest Point Detection From Speckle Imagery.

IEEE transactions on medical imaging·2017

Related Experiment Video

Updated: Mar 15, 2026

Adaptation of Microelectrode Array Technology for the Study of Anesthesia-induced Neurotoxicity in the Intact Piglet Brain
08:23

Adaptation of Microelectrode Array Technology for the Study of Anesthesia-induced Neurotoxicity in the Intact Piglet Brain

Published on: May 12, 2018

9.9K

Current Source Mapping by Spontaneous MEG and ECoG in Piglets Model.

Lin Gao1, Jue Wang1, Julia Stephen2

  • 1Institute of Biomedical Engineering, Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, P. R. China.

Biomedical Signal Processing and Control
|August 30, 2016
PubMed
Summary
This summary is machine-generated.

Principal Component Analysis (PCA) can enhance Magnetoencephalography (MEG) imaging by removing background noise. This method improves the detection of local brain activity, particularly around lesions, in animal models.

Keywords:
Background ActivityLesionMEGPiglet Neocortical ModelPrinciple Component Analysis

More Related Videos

Noninvasive EEG Recordings from Freely Moving Piglets
04:05

Noninvasive EEG Recordings from Freely Moving Piglets

Published on: July 13, 2018

7.9K
A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior
06:59

A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior

Published on: May 21, 2020

4.7K

Related Experiment Videos

Last Updated: Mar 15, 2026

Adaptation of Microelectrode Array Technology for the Study of Anesthesia-induced Neurotoxicity in the Intact Piglet Brain
08:23

Adaptation of Microelectrode Array Technology for the Study of Anesthesia-induced Neurotoxicity in the Intact Piglet Brain

Published on: May 12, 2018

9.9K
Noninvasive EEG Recordings from Freely Moving Piglets
04:05

Noninvasive EEG Recordings from Freely Moving Piglets

Published on: July 13, 2018

7.9K
A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior
06:59

A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior

Published on: May 21, 2020

4.7K

Area of Science:

  • Neuroscience
  • Biophysics
  • Biomedical Engineering

Background:

  • Spontaneous brain activity exhibits strong spatial correlations in EEG and MEG.
  • High background activity in MEG can obscure weak, localized neuronal signals.
  • Current source mapping in MEG faces challenges due to signal masking.

Purpose of the Study:

  • To investigate if Principal Component Analysis (PCA) can isolate local neuronal activity from background noise in MEG.
  • To demonstrate that removing the dominant principal component enhances the visibility of local signals.
  • To validate this approach in a preclinical model of brain lesions.

Main Methods:

  • Applied Principal Component Analysis (PCA) to Magnetoencephalography (MEG) data.
  • Removed the first principal component (PC) to filter dominant background activity.
  • Used dense MEG signal detection (2x2 mm^2) in piglets' neocortical models with lesions.
  • Analyzed signals in Delta, Theta, and Alpha frequency bands.

Main Results:

  • Removing the first PC revealed strong activity in the lesion region of piglets' neocortical models.
  • This enhanced activity was clearly visible in Delta, Theta, and Alpha bands after PCA filtering.
  • Original MEG recordings without PCA did not show the lesion activity as clearly.

Conclusions:

  • PCA decomposition is effective in enhancing the detection of local neuronal activity in MEG.
  • Filtering dominant background components via PCA improves spatial mapping of localized brain function.
  • This technique shows promise for improving MEG's ability to image subtle neurological changes, such as those around lesions.