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

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
Transfer Function to State Space01:23

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Downsampling01:20

Downsampling

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...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...

You might also read

Related Articles

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

Sort by
Same author

From Attention Control to Stimulus Selection: Neural Mechanisms Revealed by Multivariate Pattern and Functional Connectivity Analyses.

bioRxiv : the preprint server for biology·2026
Same author

Rhythmic sampling and competition of target and distractor representations in visual sensory memory.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same author

The Ventral Attention Network Mediates Attentional Reorienting to Cross-Modal Expectancy Violations: Evidence from EEG and fMRI.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same author

Neural Mechanisms of Willed Attention Control.

bioRxiv : the preprint server for biology·2026
Same author

Neural representation of emotional valence in human amygdala and surrounding regions.

NeuroImage·2026
Same author

Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

Nature neuroscience·2025
Same journal

Detection of cochlear microphonic for differential diagnosis between auditory neuropathy mice and noise-induced sensorineural hearing loss mice.

Journal of neuroscience methods·2026
Same journal

Assessment metrics for pain control in rats: A methodological commentary.

Journal of neuroscience methods·2026
Same journal

Infant EEG preprocessing pipelines: A capability framework and current gaps in practice.

Journal of neuroscience methods·2026
Same journal

Methods for measuring neural activity during voluntary wheel running.

Journal of neuroscience methods·2026
Same journal

Serotype-dependent differences in AAV cellular transduction rates in the hypothalamus of Arctic ground squirrels.

Journal of neuroscience methods·2026
Same journal

Rapid generation of human sensory neurons from iPSC for modeling of peripheral neuropathies.

Journal of neuroscience methods·2026
See all related articles

Related Experiment Videos

Denoising neural data with state-space smoothing: method and application.

Hariharan Nalatore1, Mingzhou Ding, Govindan Rangarajan

  • 1Applied Research International, New Delhi, India.

Journal of Neuroscience Methods
|May 12, 2009
PubMed
Summary
This summary is machine-generated.

Noise in neural data can lead to incorrect conclusions. This study uses a state-space smoothing method to denoise local field potentials, improving the interpretability of neural activity and reducing discrepancies in causality analysis.

Related Experiment Videos

Area of Science:

  • Neuroscience
  • Signal Processing
  • Computational Biology

Background:

  • Neural data, such as local field potentials (LFPs), are frequently contaminated by noise.
  • Noise in neural recordings can lead to erroneous statistical analyses and misinterpretations of brain activity.
  • Accurate analysis of neural signals is crucial for understanding brain function during complex tasks.

Purpose of the Study:

  • To apply a state-space smoothing method to denoise neural datasets.
  • To improve the physiological interpretability of neural activity, specifically in the high gamma band.
  • To assess the impact of denoising on Granger causality analysis between brain regions.

Main Methods:

  • Utilized a state-space smoothing method combining Kalman filter theory and the Expectation-Maximization algorithm.
  • Applied the denoising technique to two distinct datasets of local field potentials from monkeys performing a visuomotor task.
  • Analyzed neural activity in the high gamma band (60-90 Hz) and Granger causality between motor cortices.

Main Results:

  • Denoising markedly improved the physiological interpretability of high gamma band neural activity in the prefrontal cortex.
  • Noise was identified as a significant factor affecting the analysis of neural data in the first dataset.
  • After denoising, the discrepancy in Granger causality findings between two monkeys was significantly reduced for the second dataset.

Conclusions:

  • State-space smoothing effectively denoises local field potential data.
  • Denoising enhances the reliability and interpretability of neural signal analysis, particularly for high-frequency activity.
  • This method offers a robust approach to mitigate noise artifacts in neuroscience research, improving the validity of findings.