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@@ -1,6 +1,3 @@
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-[![G-Node GIN](https://gin.g-node.org/img/favicon.png)](https://gin.g-node.org/hiobeen/Mouse_hdEEG_ASSR_Hwang_et_al/)
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-**👈🏼 Click to open in G-Node GIN repository!**
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-
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</br>
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</br>
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@@ -18,6 +15,8 @@ A set of high-density EEG (electroencephalogram) recording obtained from awake,
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**Step-by-step tutorial is included, fully functioning with _Google Colaboratory_ environment.**
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[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1S3iMT5zQKsJFlhJOt9WsqKcc8dDJXe89)
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+</br>
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+</br>
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# 2. File organization
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Raw EEG data are saved in EEGLAB dataset format (*.set). Below are the list of files included in this dataset.
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@@ -54,12 +53,15 @@ Raw EEG data are saved in EEGLAB dataset format (*.set). Below are the list of f
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* written and tested on Google Colab - Python 3 environment
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+</br>
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+</br>
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# 3. How to get started (Python 3 without _gin_)
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As the data are saved in EEGLAB format, you need to install appropriate module to access the data in Python3 environment. The fastest way would be to use <code>read_epochs_eeglab()</code> function in *MNE-python* module. You can download the toolbox from the link below (or use <code>pip install mne</code> in terminal shell).
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*[MNE-python]* https://martinos.org/mne/stable/index.html
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+</br>
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## Part 1. Accessing dataset
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### 1-1. Download dataset and MNE-python module
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@@ -412,6 +414,7 @@ plt.gcf().savefig(dir_fig+'fig1-4.png', format='png', dpi=300);
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![png](figures/output_12_0.png)
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+</br>
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## Part 2. Plotting Event-Related Potentials
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@@ -714,6 +717,7 @@ plt.gcf().savefig(dir_fig+'fig2-5.png', format='png', dpi=300);
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![png](figures/output_26_0.png)
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+</br>
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## Part 3. Drawing topography
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@@ -1120,6 +1124,7 @@ plt.gcf().savefig(dir_fig+'fig3-4.png', format='png', dpi=300);
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![png](figures/output_37_0.png)
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+</br>
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### 4-1. Appendix: Power topography of Dataset 1
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