업데이트 'README.md'

README.md

@@ -1,11 +1,13 @@
 # 1. Dataset information
 
+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 
+
 * Title: High-density EEG recording in mice for auditory steady-state response with optogenetic stimulation in the basal forebrain
 * Authors: Eunjin Hwang, Hio-Been Han, Jeongyeong Kim, & Jee Hyun Choi [corresponding: jeechoi@kist.re.kr]
 * Version: 1.0.0
 * Related publication: [Hwang et al., 2019, *Brain Structure and Function*](https://link.springer.com/article/10.1007/s00429-019-01845-5). 
 
-* 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 
+** Step-by-step tutorial is included, fully functioning with *Google Colaboratory* environment. [Open in COLAB [*data_description.ipynb*]](http://colab.research.google.com) **
 
 
 # 2. File organization
@@ -14,7 +16,7 @@ Raw EEG data are saved in EEGLAB dataset format (*.set). Below are the list of f
 
 **a) Meta data file (1 csv file)**
 
-    >[metadata.csv]
+    [metadata.csv]
     
 **b) Electrode montage file (1 csv file)**
 
@@ -34,13 +36,12 @@ Raw EEG data are saved in EEGLAB dataset format (*.set). Below are the list of f
     [data_description.ipynb, data_description.py (written and tested on Python 3 environment)
     
     
-# 3. How to get started (Python 3 environment)
-
+# 3. How to get started (Python 3 without _gin_)
 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.
 
 *[MNE-python]* https://martinos.org/mne/stable/index.html
 
-
+> 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).
 
 ## Part 1. Accessing dataset
 
@@ -49,7 +50,7 @@ As the data are saved in EEGLAB format, you need to install appropriate module t
 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*.
 Implementation of scripts below will resulted in downloads of dataset files. In addition, you need to install *MNE-Python* module using *pip* command.
 
-```{.python}
+```python
 # Demo 1-1. Setting an enviroment
 dir_origin = '/content/' # <- Change this part in local machine
 dir_dataset= 'dataset/'
@@ -653,7 +654,7 @@ plt.close()
 ```
 
 
-###3-3. Band-limited power topography
+### 3-3. Band-limited power topography
 
 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.