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

Regression Toward the Mean01:52

Regression Toward the Mean

7.1K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.1K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

34.0K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
34.0K
Multiple Regression01:25

Multiple Regression

4.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.0K
Correlation and Regression00:53

Correlation and Regression

3.5K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.5K
Regression Analysis01:11

Regression Analysis

8.4K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.4K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

1.6K
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Moving intentions from brains to machines.

Trends in cognitive sciences·2026
Same author

Natural sounds can be reconstructed from human neuroimaging data using deep neural network representation.

PLoS biology·2025
Same author

Inter-individual and inter-site neural code conversion without shared stimuli.

Nature computational science·2025
Same author

Visual Image Reconstruction from Brain Activity via Latent Representation.

Annual review of vision science·2025
Same author

Spurious reconstruction from brain activity.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

[How Can We Reconstruct Mental Imagery from Brain Activities?]

Brain and nerve = Shinkei kenkyu no shinpo·2024
Same journal

Predicting vasovagal syncope during head-up tilt test: three machine learning approaches.

Frontiers in neuroinformatics·2026
Same journal

Decoding basal ganglia motor circuit dysfunction from handwriting: a physics-informed neural signal interpretation framework for Parkinson's disease screening.

Frontiers in neuroinformatics·2026
Same journal

FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.

Frontiers in neuroinformatics·2026
Same journal

A 3D-printed phantom to validate subject orientation in 3D imaging and recordings.

Frontiers in neuroinformatics·2026
Same journal

IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.

Frontiers in neuroinformatics·2026
Same journal

Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.

Frontiers in neuroinformatics·2026
See all related articles

Related Experiment Video

Updated: Feb 6, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

699

Sparse Ordinal Logistic Regression and Its Application to Brain Decoding.

Emi Satake1, Kei Majima1, Shuntaro C Aoki2

  • 1Graduate School of Informatics, Kyoto University, Kyoto, Japan.

Frontiers in Neuroinformatics
|August 31, 2018
PubMed
Summary
This summary is machine-generated.

We developed sparse ordinal logistic regression (SOLR) to decode ordinal variables from brain activity. SOLR significantly outperforms existing methods, offering a new standard for brain decoding research.

Keywords:
bayesian estimationdecodingfunctional magnetic resonance imagingordinal logistic regressionsparseness

More Related Videos

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.2K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.7K

Related Experiment Videos

Last Updated: Feb 6, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

699
Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.2K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.7K

Area of Science:

  • Neuroscience
  • Machine Learning
  • Cognitive Science

Background:

  • Multivariate classification and regression are established for brain decoding of neural activity.
  • Existing methods struggle with ordinal variables, common in cognitive and behavioral research.

Purpose of the Study:

  • Introduce a novel algorithm, sparse ordinal logistic regression (SOLR), for decoding ordinal variables from neural data.
  • Evaluate SOLR's performance against established decoding techniques.

Main Methods:

  • Developed SOLR by combining ordinal logistic regression with Bayesian sparse weight estimation.
  • Validated SOLR using both simulated data and real functional magnetic resonance imaging (fMRI) data.

Main Results:

  • SOLR demonstrated superior performance compared to non-sparse ordinal logistic regression.
  • SOLR also outperformed standard classification and linear regression models with similar sparsity.

Conclusions:

  • SOLR offers a principled and effective approach for decoding ordinal variables in brain activity.
  • The findings highlight the advantage of tailored modeling for ordinal outputs in brain decoding.