Science Score: 26.0%

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    Low similarity (8.5%) to scientific vocabulary

Keywords

eeg eeg-analysis eeg-signals fft python
Last synced: 6 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: alamkanak
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 486 MB
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eeg eeg-analysis eeg-signals fft python
Created over 5 years ago · Last pushed almost 3 years ago
Metadata Files
Readme Citation

README.md

EEG Correlation Analysis

This project calculates PSD and phases of EEG datasets. The input files are what were outputted by https://github.com/alamkanak/EEG-Processing-Matlab. Taking them as input, this repository processes them and outputs powers and phases of the EEG signals as excel files and graphs. Please read the README.md of https://github.com/alamkanak/EEG-Processing-Matlab before starting to use this repository.

Install

  1. Create a conda environment with python 3.7
  2. Run the commands mentioned in requirements.txt file
  3. Download the three datasets in data folder
  4. Open jupyter notebook in the root directory

Important note

Most of the files in this repository were used for experimentations. However, the relevent python codes for the resulting paper are included in the files 164-d1.ipynb, 157-d2.ipynb, 166-d3.ipynb, 167-d1-plot.ipynb, 168-d2-plot.ipynb, and 169-d3-plot.ipynb. The following are the description of these files.

Dataset 1

The file 157-d2.ipynb does all the processing of dataset 1. It reads four types of files and processes them:

  • Artifactual Hjorth transformed files: raw-hjorth/*.mat
  • Artifactual Raw EEG files: raw/*.mat
  • Cleaned Hjorth transformed files: clean-hjorth/*.csv
  • Cleaned EEG files: clean/*.mat

The processing outputs are stored in 157-d2-power-v2.csv and 157-d2-phase-v2.csv files. The output files are not stored in the repository for large filesize. The output files are further processed in Rstudio.

The plots for the paper are produced in 168-d2-plot.ipynb.

Dataset 2

The file 166-d3.ipynb does all the processing of dataset 2. It reads four types of files and processes them:

  • Cleaned Hjorth transformed files: clean-hjorth/*.mat
  • Cleaned Raw EEG files: clean/*.mat
  • Artifactual Hjorth transformed files: raw-hjorth/*.mat
  • Artifactual raw EEG files: raw/*.mat

The processing outputs are stored in 166-d3-powers-v2.csv and 166-d3-phases-v2.csv files. The output files are not stored in the repository for large filesize. The output files are further processed in Rstudio.

The plots for the paper are produced in 169-d3-plot.ipynb.

Dataset 3

The file 164-d1.ipynb does all the processing of dataset 3. It reads four types of files and processes them:

  • Cleaned Hjorth transformed files: 06-clean-prestimulus-hjorth.mat
  • Cleaned Raw EEG files: 06-clean-prestimulus.p
  • Artifactual Hjorth transformed files: 010-raw-hjorth.mat
  • Artifactual raw EEG files: raw.p

The processing outputs are stored in 164-d1-powers.csv and 164-d1-phases.csv files. The output files are not stored in the repository for large filesize. The output files are further processed in Rstudio.

The plots for the paper are produced in 167-d1-plot.ipynb.

Correlation analysis

For the three datasets, all correlation analysis is available in 167-d1-plot.ipynb, 168-d2-plot.ipynb, and 169-d3-plot.ipynb respectively.

Statistical analysis (not included in the published paper)

The correlation between methodological choices and power-phase quantities were performed separately in different files for different datasets. - Dataset 1: d2.R - Dataset 2: No analysis was performed with dataset 2. - Dataset 3: d1.R

Owner

  • Name: Raquib-ul Alam (Kanak)
  • Login: alamkanak
  • Kind: user
  • Location: Toronto, Canada
  • Company: Emergence AI

Hybrid of two realms: machine learning and software engineering

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