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

Downsampling01:20

Downsampling

108
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
108
Aliasing01:18

Aliasing

100
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
100
Upsampling01:22

Upsampling

159
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
159
Sampling Theorem01:15

Sampling Theorem

244
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
244

You might also read

Related Articles

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

Sort by
Same author

Subspace communication in the hippocampal-retrosplenial axis.

Nature·2026
Same author

Hierarchical Gating of Cortical Population Dynamics Drives Pain.

bioRxiv : the preprint server for biology·2026
Same author

A cautionary tale for AI and machine learning in psychiatry.

Translational psychiatry·2026
Same author

A Holistic and Dynamic Network-Level View of the Autonomic Nervous System.

Annual review of biomedical engineering·2025
Same author

Retinal ganglion cell input to superior colliculus encodes salient information.

bioRxiv : the preprint server for biology·2025
Same author

The efficacy of resveratrol in the treatment of liver fibrosis: a systematic review and meta-analysis of preclinical studies.

Frontiers in nutrition·2025
Same journal

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2026
Same journal

EARLY DETECTION OF COGNITIVE DECLINE USING VOICE ASSISTANT COMMANDS.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

CROSS-DOMAIN DIFFUSION BASED SPEECH ENHANCEMENT FOR VERY NOISY SPEECH.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

CROSS-DOMAIN SPEECH ENHANCEMENT WITH A NEURAL CASCADE ARCHITECTURE.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

NEURAL CASCADE ARCHITECTURE FOR JOINT ACOUSTIC ECHO AND NOISE SUPPRESSION.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

ATTENTION-BASED FUSION FOR BONE-CONDUCTED AND AIR-CONDUCTED SPEECH ENHANCEMENT IN THE COMPLEX DOMAIN.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
See all related articles

Related Experiment Video

Updated: May 9, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

2.9K

ESTIMATING DIRECTED SPECTRAL INFORMATION FLOW BETWEEN MULTI-RESOLUTION TIME SERIES.

Qiqi Xian1,2, Zhe Sage Chen1,2

  • 1Dept. Psychiatry, Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USA.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
|May 5, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new method to measure Granger causality in multimodal time series data with different sampling rates. This approach quantifies frequency-dependent directed information flow, overcoming limitations of traditional techniques.

Keywords:
Spectral Granger causalitycanonical correlation analysismulti-resolution time series

More Related Videos

Blood Flow Imaging with Ultrafast Doppler
05:57

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

7.4K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

6.9K

Related Experiment Videos

Last Updated: May 9, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

2.9K
Blood Flow Imaging with Ultrafast Doppler
05:57

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

7.4K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

6.9K

Area of Science:

  • Time series analysis
  • Information theory
  • Causality inference

Background:

  • Directed information flow and Granger causality are crucial in science and engineering.
  • Existing methods struggle with multimodal data and varying temporal resolutions.
  • Assessing causality across different data types remains a challenge.

Purpose of the Study:

  • To propose a novel analysis approach for Granger causality in multimodal time series.
  • To address the limitations of traditional methods in handling distinct temporal resolutions.
  • To introduce quantitative characterizations and statistical assessment of frequency-dependent directed information flow.

Main Methods:

  • Development of a new analysis framework for generalized spectral causality.
  • Quantitative characterization of directed information flow.
  • Statistical assessment of frequency-dependent causality.
  • Validation using intensive computer simulations.

Main Results:

  • The proposed approach successfully quantifies frequency-dependent directed information flow.
  • Demonstrated effectiveness in bivariate and trivariate systems under various conditions.
  • Provided a robust method for assessing Granger causality in complex datasets.

Conclusions:

  • The new method, generalized spectral causality, effectively addresses Granger causality in multimodal time series with different temporal resolutions.
  • Offers a significant advancement for analyzing directed information flow in complex scientific and engineering applications.
  • Validated through simulations, this approach provides reliable causal insights.