From 356b8f4c0eaaebd9b16ea7fa429e6d47d6574cbb Mon Sep 17 00:00:00 2001 From: AyaKabbara Date: Wed, 12 Oct 2022 10:50:35 +0300 Subject: [PATCH] readme changes --- README.md | 37 +++++++++++-------------------------- 1 file changed, 11 insertions(+), 26 deletions(-) diff --git a/README.md b/README.md index a871a53..6cb1de3 100644 --- a/README.md +++ b/README.md @@ -78,47 +78,32 @@ Note: The name of csv files should be changed accordingly. ### Reproducing Figure 2B, Figure 3B using EEGLAB -1. Add path to corresponding dependencies listed above using: -``` - addpath(genpath('dependencyDir')) -``` - -2. Run the analysis [eeglab_preprocessing.m](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/eeglab/eeglab_preprocessing.m). -3. Create figures 2B,3B and generate the related values in tables 1,2 using the R script [RewardProcessing_Plots_and_Statistics.R](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/graphiques/RewardProcessing_Plots_and_Statistics.R).Note: the name of csv files should be changed accordingly. +1. Run the analysis [eeglab_preprocessing.m](src/eeglab/eeglab_preprocessing.m). +2. Create figures 2B,3B and generate the related values in tables 1,2 using the R script [RewardProcessing_Plots_and_Statistics.R](src/graphiques/RewardProcessing_Plots_and_Statistics.R).Note: the name of csv files should be changed accordingly. ### Reproducing Figure 2C,Figure 3C using Brainstorm -1. Add path to corresponding dependencies listed above using: -``` - addpath(genpath('dependencyDir')) -``` -2. Convert the dataset into EEGLAB set using the following functions: [toset.m](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/BST/toset.m) -3. Run the analysis with [bsPreprocessing.m](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/BST/bsPreprocessing.m). -4. Create figures 2C,3C and generate the related values in tables 1,2 using the R script [RewardProcessing_Plots_and_Statistics.R](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/graphiques/RewardProcessing_Plots_and_Statistics.R).Note: the name of csv files should be changed accordingly. +1. Run the analysis with [bsPreprocessing.m](src/BST/bsPreprocessing.m). +2. Create figures 2C,3C and generate the related values in tables 1,2 using the R script [RewardProcessing_Plots_and_Statistics.R](src/graphiques/RewardProcessing_Plots_and_Statistics.R).Note: the name of csv files should be changed accordingly. ### Reproducing Figure 2D,Figure 3D using FieldTrip -1. Add path to corresponding dependencies listed above using: -``` - addpath(genpath('dependencyDir')) -``` - -2. Run the analysis with [ftPreprocessing.m](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/BST/ftPreprocessing.m). -3. Create figures 2D,3D and generate the related values in tables 1,2 using the R script [RewardProcessing_Plots_and_Statistics.R](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/graphiques/RewardProcessing_Plots_and_Statistics.R).Note: the name of csv files should be changed accordingly. +2. Run the analysis with [ftPreprocessing.m](src/fieldtrip/ftPreprocessing.m). +3. Create figures 2D,3D and generate the related values in tables 1,2 using the R script [RewardProcessing_Plots_and_Statistics.R](src/graphiques/RewardProcessing_Plots_and_Statistics.R).Note: the name of csv files should be changed accordingly. ### Reproducing Figure 4 -1. Prepare the csv files containing the feature to be plotted using [fig4_preparation.m](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/graphiques/fig4_preparation.m). This script calls the function [extract_features.m](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/graphiques/extract_features.m). -2. Create the figure using [Fig4.py](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/graphiques/Fig4.py) +1. Prepare the csv files containing the feature to be plotted using [fig4_preparation.m](src/graphiques/fig4_preparation.m). This script calls the function [extract_features.m](src/graphiques/extract_features.m). +2. Create the figure using [Fig4.py](src/graphiques/Fig4.py) ### Reproducing Figure 5 -1. Generate the similarity matrix (for both conditions) using the function [similarity_calc.m](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/graphiques/similarity_calc.m) -2. Create the figure using [Fig5.py](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/graphiques/Fig5.py) +1. Generate the similarity matrix (for both conditions) using the function [similarity_calc.m](src/graphiques/similarity_calc.m) +2. Create the figure using [Fig5.py](src/graphiques/Fig5.py) ### Reproducing Figure 6 -Create Figure 6.A using [eeglab_stats.m](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/eeglab/eeglab_stats.m), Figure 6.B using [bs_stats.m](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/BST/bs_stats.m) and Figure 6.C using [ft_stats.m](https://github.com/AyaKabbara/StageEEGpre/blob/main/src/fieldtrip/ft_stats.m) +Create Figure 6.A using [eeglab_stats.m](src/eeglab/eeglab_stats.m), Figure 6.B using [bs_stats.m](src/BST/bs_stats.m) and Figure 6.C using [ft_stats.m](src/fieldtrip/ft_stats.m)