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radsel

Radius selection using kernel density estimation for the computation of nonlinear measures

https://github.com/johmedr/radsel

Science Score: 67.0%

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Repository

Radius selection using kernel density estimation for the computation of nonlinear measures

Basic Info
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  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

RADSEL: Radius selection for nonlinear measures.

The RADSEL function automates the selection of an optimal radius parameter for estimating nonlinear measures from finite-length temporal signals. It leverages the Kernel Density Estimator (KDE) framework to improve the precision of measures such as the correlation dimension and Kolmogorov-Sinai entropy. This function is particularly useful for analyzing signals generated by nonlinear systems and experimental electroencephalographic time series.

Citation

If you use RADSEL in your research or academic work, please cite the following paper:

Johan Medrano, Abderrahmane Kheddar, Annick Lesne, Sofiane Ramdani; 
"Radius selection using kernel density estimation for the computation of nonlinear measures." 
Chaos, 1 August 2021; 31 (8): 083131. 
DOI: 10.1063/5.0055797

Direct Link: https://doi.org/10.1063/5.0055797

Getting Started

Prerequisites

Ensure you have MATLAB installed on your machine to use RADSEL. The function is compatible with MATLAB R2018a and later versions.

Installation

Clone this repository or download the ZIP file to get started with RADSEL:

bash git clone https://github.com/yourusername/RADSEL.git

Navigate to the RADSEL directory in MATLAB to access the function.

Usage

To use RADSEL, load your data into MATLAB and call the function with your data matrix as the input. The function syntax is as follows:

matlab radius = RADSEL(X, P, D, N, STD, IQR)

  • X: Input data matrix (samples x dimensions).
  • P (optional): Norm order (default is 2, Euclidean distance).
  • D (optional): Number of dimensions to consider (default is all columns in X).
  • N (optional): Number of samples to consider (default is all rows in X).
  • STD (optional): Standard deviation of the input data (default is calculated from X).
  • IQR (optional): Interquartile range of the input data (default is calculated from X).

Examples

Load your data and call RADSEL:

matlab load('yourData.mat'); % Replace 'yourData.mat' with your data file optimalRadius = RADSEL(data); disp(['Optimal Radius: ', num2str(optimalRadius)]);

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Owner

  • Name: Johan Medrano
  • Login: johmedr
  • Kind: user
  • Company: Wellcome Centre for Human Neuroimaging

Research fellow

Citation (CITATION.cff) 

cff-version: 1.2.0
message: >-
  If you use this software, please cite it using the metadata from this file.
type: software
title: 'fooof_mat'
authors:
- given-names: 'Johan'
  family-names: 'Medrano'
  orcid: 'https://orcid.org/0000-0002-7558-2071'
repository-code: 'https://github.com/johmedr/radsel'
license: MIT
preferred-citation:
  type: article
  authors:
  - given-names: 'Johan'
    family-names: 'Medrano'
    orcid: 'https://orcid.org/0000-0002-7558-2071'
  - given-names: 'Abderrahmane'
    family-names: 'Kheddar'
    orcid: 'https://orcid.org/0000-0001-9033-9742'
  - given-names: 'Annick'
    family-names: 'Lesne'
    orcid: 'https://orcid.org/0000-0002-6647-612X'
  - given-names: 'Sofiane'
    family-names: 'Ramdani'
    orcid: 'https://orcid.org/0000-0001-8925-3546'
  doi: '10.1063/5.0055797'
  journal: 'Chaos'
  title: 'Radius selection using kernel density estimation for the computation of nonlinear measures'
  issue: 8
  volume: 31
  year: 2021

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