In this tutorial we will utilize a python script to normalize or scale EEG data. This script however works for any kind of data.

To have access to spyder it is recommended to download first Anaconda.
After the download has been completed, you can run Anaconda and lunch Spyder. If the launch button does not show but an Install button shows instead, make sure to install Spyder and then run it.
After you open Spyder, you can create a new file and save the file in the same directory where your data is located. Now we are ready to start coding.

Let's assume we have collected some data from two tasks. So we have two csv files. In my case two EEG data files in csv format. The data we have is in a format in which we cannot make propper analysis and therefore we need to normalize it or scale it. We can create a script that goes through both of those files at the same time
and saves two data files in csv with normalized data for both tasks.

The files that I have used from the two tasks are collected from an EEG device and are in csv format:

Download EEG CSV File For Task 1Download EEG CSV File For Task 2

Now we can write a python script that scales the data in both datasets and saves it into the folder. Is important to note that this python script should be located into the same folder with the two datasets.

` ````
"""
@author: Fjorda
"""
import numpy as np
import pandas as pd
S1 = pd.read_csv('Task1_EEG.csv')
S2 = pd.read_csv('Task2_EEG.csv')
S = [S1, S2]
attribs = ['alpha_AF3', 'beta_AF3', 'gamma_AF3', 'delta_AF3', 'theta_AF3',
'alpha_AF4', 'beta_AF4', 'gamma_AF4', 'delta_AF4', 'theta_AF4',
'alpha_FP1', 'beta_FP1', 'gamma_FP1', 'delta_FP1', 'theta_FP1',
'alpha_FP2', 'beta_FP2', 'gamma_FP2', 'delta_FP2', 'theta_FP2',
'alpha_AF7', 'beta_AF7', 'gamma_AF7', 'delta_AF7', 'theta_AF7',
'alpha_AF8', 'beta_AF8', 'gamma_AF8', 'delta_AF8', 'theta_AF8']
X_max = -100000
X_min = 100000
for k in range(len(S)):
Sk = S[k]
x_max = -100000
x_min = 100000
for i in range(len(attribs)):
x = Sk[attribs[i]]
x = abs(x)
x_max = max(x_max, max(x))
x_min = min(x_min, min(x))
X_max = max(X_max, x_max)
X_min = min(X_min, x_min)
for k in range(len(S)):
Sk = S[k]
for i in range(len(attribs)):
x = Sk[attribs[i]]
x = abs(x)
x = (x - X_min)/(X_max - X_min)
Sk[attribs[i]] = x
filename = 'rTask' + str(k+1) + '_EEG.csv'
pd.DataFrame.to_csv(Sk, filename)
```

After we run the above script, we now have saved into our folder the two scaled files for both tasks.
Have a look at the scaled files below.

Download Scaled EEG CSV File For Task 1Download Scaled EEG CSV File For Task 2