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@@ -1,11 +1,13 @@
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# 1. Dataset information
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+A set of high-density EEG (electroencephalogram) recording obtained from awake, freely-moving mice (*mus musculus*) (n = 6). Detailed description of experimental method is described in the original research article using the same dataset
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+
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* Title: High-density EEG recording in mice for auditory steady-state response with optogenetic stimulation in the basal forebrain
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* Authors: Eunjin Hwang, Hio-Been Han, Jeongyeong Kim, & Jee Hyun Choi [corresponding: jeechoi@kist.re.kr]
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* Version: 1.0.0
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* Related publication: [Hwang et al., 2019, *Brain Structure and Function*](https://link.springer.com/article/10.1007/s00429-019-01845-5).
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-* Brief summary: A set of high-density EEG (electroencephalogram) recording obtained from awake, freely-moving mice (*mus musculus*) (n = 6). Detailed description of experimental method is described in the original research article using the same dataset
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+** Step-by-step tutorial is included, fully functioning with *Google Colaboratory* environment. [Open in COLAB [*data_description.ipynb*]](http://colab.research.google.com) **
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# 2. File organization
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@@ -14,7 +16,7 @@ Raw EEG data are saved in EEGLAB dataset format (*.set). Below are the list of f
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**a) Meta data file (1 csv file)**
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- >[metadata.csv]
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+ [metadata.csv]
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**b) Electrode montage file (1 csv file)**
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@@ -34,13 +36,12 @@ Raw EEG data are saved in EEGLAB dataset format (*.set). Below are the list of f
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[data_description.ipynb, data_description.py (written and tested on Python 3 environment)
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-# 3. How to get started (Python 3 environment)
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-
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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 *read_epochs_eeglab()* function in *MNE-python* module. You can download the toolbox from the link below.
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*[MNE-python]* https://martinos.org/mne/stable/index.html
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-
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+> Warning: Direct clonning using *git clone git@gin.g-node.org:/hiobeen/Mouse_hdEEG_ASSR_Hwang_et_al.git* may not work because of the large size of each dataset (>100 MB).
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## Part 1. Accessing dataset
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@@ -49,7 +50,7 @@ As the data are saved in EEGLAB format, you need to install appropriate module t
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The dataset has been uploaded on G-Node and can be accessed by git command, by typing *git clone https://gin.g-node.org/hiobeen/Mouse_hdEEG_ASSR_Hwang_et_al*.
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Implementation of scripts below will resulted in downloads of dataset files. In addition, you need to install *MNE-Python* module using *pip* command.
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-```{.python}
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+```python
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# Demo 1-1. Setting an enviroment
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dir_origin = '/content/' # <- Change this part in local machine
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dir_dataset= 'dataset/'
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@@ -653,7 +654,7 @@ plt.close()
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```
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-###3-3. Band-limited power topography
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+### 3-3. Band-limited power topography
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Other than raw voltage, topography of band-limited power at stimulation frequency (40 Hz) can be drawn as well. In this example, stimulus-evoked 40 Hz power were estimated using bandpower() function.
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Hio-Been Han