This repository provides an implementation of modeling EEG signals using polynomial regression. The code and analysis is done using R and the detailed interpretation of the results is presented in an R Markdown file.
To view the HTML version of the detailed report, click below.
HTML version of the modeling of EEG signals using polynomial regression in RPubs
Read the Blog at Towards Data Science, Medium
EEG stands for Electroencephalogram which are electrical signals that measured the electrical activity of the brain (St et al., 2016). To get the EEG result, electrodes consisting of small metal discs with thin wires are pasted onto scalp. The electrodes detect tiny electrical charges that result from the activity of your brain cells and thus obtained charges are amplified and appear as a graph on a computer screen, or as a recording that may be printed out on paper. The main purpose of EEG is to detect potential problems (encephalitis, hemorrhage, epilepsy,Parkinson's disease and other) with brain cell communication by painless method (Healthline, 2012).
Here in the data set, we have four input EEG signals, one output signals and time for each signals.
- Visualize the trend and patterns in the EEG signals over time.
- Assess the fit of the polynomial regression model to the EEG signals and estimate the parameters of the model.
- Use a simulation-based approach to estimate the posterior distribution of the model parameters, allowing for uncertainty in the model and data.. ion-based approach to estimate the posterior distribution of the model parameters, allowing for uncertainty in the model and data.
| Attribute | Value |
|---|---|
| Platform | aarch64-apple-darwin20 |
| Arch | aarch64 |
| OS | darwin20 |
| System | aarch64, darwin20 |
| Major | 4 |
| Minor | 2.2 |
| Year | 2022 |
| Month | 10 |
| Day | 31 |
| SVN Rev | 83211 |
| Language | R |
| Version String | R version 4.2.2 (2022-10-31) |
| Nickname | Innocent and Trusting |
The following libraries are used in this project:
ggplot2- implementation of the grammar of graphics for complex visualizationsGGally- creates a matrix of scatter plots, with histograms or density plotstidyverse- data manipulation, exploration, and visualizationtidymodels- splits the data into training and testingmoments- calculates and plots descriptive statisticsdplyr- a data manipulation toolkit for working with structured dataggExtra- adds additional functionality to ggplot2readr- a fast and friendly library for file readingggpubr- customizes ggplot2 based publication ready plots
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request.
Distributed under the MIT License. See LICENSE.txt for more information.
To view the citation mentioned in this file, please refer to the accompanying HTML version of the file.