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

Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

3.5K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
3.5K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.8K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.8K
Coefficient of Correlation01:12

Coefficient of Correlation

7.0K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
7.0K
Correlation01:09

Correlation

13.1K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
13.1K
Correlation of Experimental Data01:23

Correlation of Experimental Data

349
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
349
Correlations02:20

Correlations

34.7K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
34.7K

You might also read

Related Articles

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

Sort by
Same author

Dietary Intake of Chinese Lactating Women Is Associated with the Fatty Acid Profile of Their Milk.

Annals of nutrition & metabolism·2021
Same author

Gender differences of the improvement in balance control based on the real-time visual feedback system with smart wearable devices.

Acta of bioengineering and biomechanics·2021
Same author

Thermally Activated Upconversion with Metal-Free Sensitizers Enabling Exceptional Anti-Stokes Shift and Anti-counterfeiting Application.

ACS applied materials & interfaces·2021
Same author

Provision of physical activity advice for patients with chronic diseases in Shenzhen, China.

BMC public health·2021
Same author

Study of a full-digital multi-waveform nuclear pulse signal generator.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2021
Same author

Active dry yeast supplementation improves the growth performance, rumen fermentation, and immune response of weaned beef calves.

Animal nutrition (Zhongguo xu mu shou yi xue hui)·2021

Related Experiment Video

Updated: Oct 29, 2025

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.9K

Measuring Phase-Amplitude Coupling Based on the Jensen-Shannon Divergence and Correlation Matrix.

Zhaohui Li, Xiaochen Bai, Rui Hu

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |July 8, 2021
    PubMed
    Summary

    We developed a new method using Jensen-Shannon divergence to measure phase-amplitude coupling (PAC) between brain oscillations. This novel approach accurately quantifies PAC strength and outperforms existing methods in noisy conditions.

    More Related Videos

    Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating
    10:39

    Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating

    Published on: October 11, 2016

    9.8K
    Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
    12:19

    Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

    Published on: April 4, 2017

    8.6K

    Related Experiment Videos

    Last Updated: Oct 29, 2025

    Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
    05:59

    Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

    Published on: October 6, 2023

    2.9K
    Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating
    10:39

    Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating

    Published on: October 11, 2016

    9.8K
    Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
    12:19

    Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

    Published on: April 4, 2017

    8.6K

    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Signal Processing

    Background:

    • Phase-amplitude coupling (PAC) is crucial for neural information processing and cognition.
    • Existing PAC measurement methods have limitations in performance and noise resistance.

    Purpose of the Study:

    • To introduce a novel PAC measurement method utilizing Jensen-Shannon (JS) divergence and correlation matrices.
    • To evaluate the proposed method's effectiveness, robustness, and performance compared to existing algorithms.

    Main Methods:

    • Constructing a correlation matrix from high-frequency oscillation (HFO) amplitude distributions within low-frequency oscillation (LFO) phase bins.
    • Calculating matrix elements using JS divergence between amplitude distributions.
    • Estimating PAC strength via omega complexity extracted from the correlation matrix.

    Main Results:

    • The proposed method accurately reflects PAC strength and shows minimal variation with data length.
    • It demonstrates superior performance against additive white Gaussian and spike noise compared to five existing algorithms.
    • The method effectively detects PAC across wide frequency ranges and replicates previous findings on real neural data.

    Conclusions:

    • The novel JS divergence-based method provides a powerful and robust tool for quantifying PAC in neural oscillations.
    • This approach offers improved accuracy and noise resilience for analyzing neural information processing.
    • The method is validated for analyzing real local field potential data, demonstrating its practical utility.