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

Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

Extraction: Partition and Distribution Coefficients

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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 stretching vibration...

You might also read

Related Articles

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

Sort by

Same author

Gut microbiome modulation by Veillonella ratti induces resistance to EAE pathogenesis via microbe-derived metabolites.

Experimental & molecular medicine·2026

Same author

Geochemical factors associated with seasonal shifts in the abalone gut microbiota across urchin-barren habitats.

Frontiers in microbiology·2026

Same author

Potential role of the ocular surface microbiome in dry eye: microbial interactions and symptom alleviation.

mSystems·2026

Same author

Glucagon-like peptide-1 receptor agonists and the risk of surgical intervention in idiopathic intracranial hypertension: A propensity-matched cohort study.

Clinical neurology and neurosurgery·2026

Same author

Traumatic coronal spondyloptosis of the spine: case report and review of the literature.

Journal of spine surgery (Hong Kong)·2026

Same author

Microbial Production and Industrial Applications of (<i>S</i>)-Equol for Precision Health and Functional Food Innovation.

Journal of agricultural and food chemistry·2026

Search research articles

Related Experiment Video

Updated: Jul 10, 2026

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

Spatio-temporal analysis of mixed AEF and SEF using dynamic singular value decomposition.

Bong-Soo Kim¹, Yoshinori Uchikawa

¹Department of Electronic and Computer Engineering, School of Science and Engineering, Tokyo Denki University, Ishizaka, Hatoyama, Hiki-gun, Saitama, Japan. kim@f.dendai.ac.jp

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 16, 2007

Summary

Dynamic singular value decomposition (DSVD) effectively analyzes overlapping somatosensory evoked fields (SEF) and auditory evoked fields (AEF) in human cortex activity. DSVD shows higher correlation in the contralateral hemisphere, proving its utility for spatio-temporal analysis.

More Related Videos

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

A Multimodal Wide-Field Fourier-Transform Raman Microscope

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

Related Experiment Videos

Last Updated: Jul 10, 2026

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

A Multimodal Wide-Field Fourier-Transform Raman Microscope

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

Area of Science:

Neuroscience
Biophysics
Biomedical Engineering

Background:

Magnetoencephalography (MEG) measures brain activity via magnetic fields.
Somatosensory evoked fields (SEF) and auditory evoked fields (AEF) are crucial for sensory processing.
Overlapping SEF and AEF present analytical challenges.

Purpose of the Study:

To evaluate the efficacy of Dynamic Singular Value Decomposition (DSVD) for analyzing overlapping SEF and AEF.
To investigate hemispheric differences in spatio-temporal brain activity using DSVD.
To validate DSVD's utility in characterizing human cortical activity.

Main Methods:

MEG recordings of SEF (median nerve stimulation) and AEF (right ear tone burst).
Application of DSVD with defined time windows and shift times to spatio-temporal MEG data.
Analysis of bilateral (contralateral and ipsilateral) hemispheric data.
Calculation of correlation coefficients between hemispheric DSVD results.
Time-frequency analysis of contralateral hemisphere data.

Main Results:

DSVD effectively captured temporal changes in magnetic field waveforms.
DSVD analysis of spatio-temporal data showed strong correspondence with contralateral hemisphere results.
Higher correlation coefficients (>0.7) were observed between DSVD results of the contralateral (left) hemisphere compared to the ipsilateral (right) hemisphere.
Time-frequency analysis provided further insights into contralateral cortical activity.

Conclusions:

DSVD is a valuable tool for analyzing complex, overlapping spatio-temporal brain activity, such as combined SEF and AEF.
The method highlights the dominant role of the contralateral hemisphere in processing these sensory inputs.
DSVD offers a robust approach for neurophysiological studies of human cortical function.