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

Dimensional Analysis01:23

Dimensional Analysis

883
Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
883
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

13.9K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
13.9K
Correlation of Experimental Data01:23

Correlation of Experimental Data

231
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,...
231
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.4K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
2.4K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

515
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
515
Determination of Pi Terms01:15

Determination of Pi Terms

273
The Buckingham Pi theorem is a valuable method in dimensional analysis, reducing complex relationships between variables into dimensionless terms. Relevant variables in analyzing the lift force on an airplane wing include lift force, air density, wing area, aircraft velocity, and air viscosity. Expressing each variable in terms of fundamental dimensions — mass, length, and time — provides a consistent foundation for constructing these dimensionless terms.
The theorem indicates that...
273

You might also read

Related Articles

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

Sort by
Same author

Mitigating diffusion-length limitations in back-contact CZTSSe solar cells <i>via</i> synergistic field-effect engineering: a two-dimensional TCAD simulation study.

RSC advances·2026
Same author

Comment on: Sex-specific associations of lipid profiles with type 2 diabetes: Insights from the Shiraz University employees cohort.

Primary care diabetes·2026
Same author

PTP4A3 dephosphorylates EGFR to promote metastasis and enhance sensitivity to lapatinib in hepatocellular carcinoma.

Oncogene·2026
Same author

Refining metabolic-response signals after liraglutide therapy in type 1 diabetes.

Diabetic medicine : a journal of the British Diabetic Association·2026
Same author

Clarifying metformin exposure when interpreting glycemic responses after imeglimin initiation.

Acta diabetologica·2026
Same author

ADAR1p110 promotes hepatocellular carcinoma metastasis via the miR-451a/TUBA1A axis.

Genes & diseases·2026

Related Experiment Video

Updated: Jul 6, 2025

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K

CW_ICA: an efficient dimensionality determination method for independent component analysis.

Yuyan Yi1, Nedret Billor1, Arne Ekstrom2

  • 1Department of Mathematics and Statistics, Auburn University, Auburn, AL, 36849, USA.

Scientific Reports
|January 3, 2024
PubMed
Summary

Determining the correct number of independent components (ICs) is vital for signal processing. Column-wise independent component analysis (CW_ICA) offers a robust, efficient method for automatically selecting the optimal number of ICs.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

Related Experiment Videos

Last Updated: Jul 6, 2025

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

Area of Science:

  • Signal Processing
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Independent Component Analysis (ICA) is a critical blind source separation technique.
  • Accurate determination of the number of independent components (ICs) is essential for optimal ICA performance.
  • Incorrect IC number selection leads to under- or over-decomposition, compromising results.

Purpose of the Study:

  • To introduce a novel, robust method for automatically determining the optimal number of ICs.
  • To address the limitations of existing methods in IC number determination.
  • To enhance the reliability and efficiency of ICA pre-processing.

Main Methods:

  • Propose Column-wise Independent Component Analysis (CW_ICA).
  • CW_ICA partitions mixed signals into two blocks and applies ICA independently.
  • A quantitative measure based on rank-based correlation of ICs from both blocks determines the optimal IC number.

Main Results:

  • CW_ICA demonstrated reliable and robust performance in determining the optimal number of ICs.
  • The method was validated using both simulated data and real-world scalp electroencephalography (EEG) data.
  • Comparative analysis showed CW_ICA outperforms existing determination methods.

Conclusions:

  • CW_ICA provides an effective and automated solution for selecting the optimal number of ICs.
  • The method is computationally efficient and versatile, integrating with various ICA algorithms.
  • CW_ICA enhances the practical application of ICA in signal pre-processing, particularly for EEG analysis.