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

F Distribution01:19

F Distribution

4.7K
The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
4.7K
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

2.6K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
2.6K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.5K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.5K
One-Way ANOVA01:18

One-Way ANOVA

8.9K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
8.9K
Two-Way ANOVA01:17

Two-Way ANOVA

2.8K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.8K
Significance Testing: Overview01:04

Significance Testing: Overview

5.7K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
5.7K

You might also read

Related Articles

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

Sort by
Same author

A novel transformer architecture for EEG decoding and neuroscientific analysis.

Scientific reports·2026
Same author

A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech Recognition Features: Diagnostic Study.

JMIR medical informatics·2026
Same author

PixelCut: A Unified Solution for Zero-Configuration 16S rRNA Trimming via Computer Vision.

Current issues in molecular biology·2026
Same author

Effect of Mechanical Environment Alterations in 3D Stem Cell Culture on the Therapeutic Potential of Extracellular Vesicles.

Biomaterials research·2025
Same author

Low-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography.

Sensors (Basel, Switzerland)·2025
Same author

REDalign: accurate RNA structural alignment using residual encoder-decoder network.

BMC bioinformatics·2024
Same journal

CEST MRI reveals nicotine-induced alterations in glutamate-associated molecular connectivity in the mouse brain.

Frontiers in neuroscience·2026
Same journal

Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study.

Frontiers in neuroscience·2026
Same journal

Screening the optimal rTSMS frequency to orchestrate immune-fibrotic remodeling for adult spinal cord repair.

Frontiers in neuroscience·2026
Same journal

Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and <i>in vitro</i> validation.

Frontiers in neuroscience·2026
Same journal

Sex-divergent intrinsic brain function in Parkinson's disease: elevated nigral fluctuations and premotor-visuospatial coupling in female patients.

Frontiers in neuroscience·2026
Same journal

Spatial transcriptomics on an expanded dataset at the brain-electrode interface: exploration of variability and identification of novel biomarkers.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: Oct 8, 2025

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

10.9K

F-Value Time-Frequency Analysis: Between-Within Variance Analysis.

Hong Gi Yeom1, Hyundoo Jeong2

  • 1Department of Electronics Engineering, Chosun University, Gwangju, South Korea.

Frontiers in Neuroscience
|December 27, 2021
PubMed
Summary
This summary is machine-generated.

A new F-value time-frequency (FTF) analysis method reveals statistical differences in neural signals across conditions. This method effectively identifies distinct brain activity patterns for various imagined movements using electroencephalography (EEG) data.

Keywords:
ANOVAF-valueanalysis of varianceelectroencephalographytime-frequency analysis

More Related Videos

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

6.8K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.4K

Related Experiment Videos

Last Updated: Oct 8, 2025

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

10.9K
How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

6.8K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.4K

Area of Science:

  • Neuroscience
  • Signal Processing
  • Biomedical Engineering

Background:

  • Neural signal analysis is crucial for understanding brain mechanisms, disease treatment, and technology development.
  • Identifying condition-specific changes in neural signals often requires complex statistical methods.
  • Existing methods face challenges in clearly distinguishing neural signal characteristics across different experimental conditions.

Purpose of the Study:

  • To introduce a novel analysis method, F-value time-frequency (FTF) analysis, for evaluating neural signals.
  • To demonstrate the utility of applying the F-value from ANOVA to time-frequency analysis.
  • To assess the effectiveness of FTF analysis in identifying condition-specific neural signal variations.

Main Methods:

  • Developed the FTF analysis method by integrating the F-value of ANOVA into time-frequency analysis.
  • Applied the FTF method to analyze electroencephalography (EEG) data.
  • Collected EEG signals during four distinct imagined movement conditions: left hand, right hand, foot, and tongue.

Main Results:

  • The FTF analysis successfully identified statistically significant differences in neural signals across the tested conditions.
  • The method revealed distinct time-frequency characteristics that varied between different imagined movements.
  • Analysis showed that neural signal patterns were similar within the same condition but different across conditions.

Conclusions:

  • The proposed FTF analysis method provides a robust approach for analyzing neural signals.
  • FTF analysis effectively highlights statistical differences in time-frequency characteristics related to specific conditions.
  • This method holds potential for broad applications in neuroscience and other fields requiring analysis of condition-dependent frequency variations.