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

Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

910
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
910
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

1.1K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
1.1K
Fast Fourier Transform01:10

Fast Fourier Transform

950
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
950
Properties of Fourier Transform I01:21

Properties of Fourier Transform I

662
The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
662
Properties of Fourier Transform II01:24

Properties of Fourier Transform II

779
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
779
Discrete Fourier Transform01:15

Discrete Fourier Transform

901
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...
901

You might also read

Related Articles

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

Sort by
Same author

Relative timing and coupling of neural population bursts in large-scale recordings from multiple neuron populations.

Frontiers in computational neuroscience·2026
Same author

Cross-population amplitude coupling in high-dimensional oscillatory neural time series.

Frontiers in computational neuroscience·2026
Same author

A Population Coupling Model Identifies Reduced Propagation from V1 to Higher Visual Areas During Locomotion.

bioRxiv : the preprint server for biology·2026
Same author

The Oomplet dataset toolkit as a flexible and extensible system for large-scale, multi-category image generation.

Scientific reports·2025
Same author

Relative timing and coupling of neural population bursts in large-scale recordings from multiple neuron populations.

bioRxiv : the preprint server for biology·2025
Same author

Origins of food selectivity in human visual cortex.

Trends in neurosciences·2025

Related Experiment Video

Updated: Feb 4, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.2K

Estimating Learning Effects: A Short-Time Fourier Transform Regression Model for MEG Source Localization.

Ying Yang1, Michael J Tarr1, Robert E Kass1

  • 1Carnegie Mellon University, Pittsburgh, USA.

Machine Learning and Interpretation in Neuroimaging : 4Th International Workshop, MLINI 2014, Held at NIPS 2014, Montreal QC, Canada, December 13, 2014 : Revised Selected Papers. MLINI (Workshop) (4Th : 2014 : Montreal, Quebec)
|September 25, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces STFT-R, a new method for magnetoencephalography (MEG) source localization. STFT-R enhances brain activity analysis by incorporating learning effects, improving the understanding of perceptual learning in the brain.

More Related Videos

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

7.3K
Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

18.5K

Related Experiment Videos

Last Updated: Feb 4, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.2K
Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

7.3K
Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

18.5K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biophysics

Background:

  • Magnetoencephalography (MEG) offers high temporal resolution for studying perceptual learning.
  • Source localization is crucial for mapping brain activity during learning but existing methods lack trial-by-trial learning effect incorporation.

Purpose of the Study:

  • To develop an improved source localization technique for magnetoencephalography (MEG) data.
  • To incorporate trial-by-trial learning effects into brain activity mapping.
  • To enhance the interpretability of brain signals related to perceptual learning.

Main Methods:

  • Modified the short-time Fourier transform (STFT) method to create STFT-R.
  • Integrated regression of STFT components with behavioral learning curves.
  • Employed a hierarchical L21 penalty for structured sparsity and region of interest (ROI) emphasis.

Main Results:

  • STFT-R demonstrated reduced reconstruction errors compared to minimum-norm estimate (MNE) on simulated data.
  • The method provided more interpretable results in a human learning experiment.
  • Identified specific time-frequency components of ROI signals correlated with learning.

Conclusions:

  • STFT-R offers a significant advancement in source localization for MEG.
  • The method effectively links brain activity dynamics to behavioral learning.
  • Enables more precise investigation of the neural basis of perceptual learning.