Using External Libraries

In this notebook, we explain how to extract the signals preprocess with external libraries and do predictive modeling.

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Table of Contents

Import Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn import svm

import phyaat
print('Version :' ,phyaat.__version__)
import phyaat as ph

PhyAAt Processing lib Loaded...
Version : 0.0.2

Read the data of subject=1

dirPath = ph.download_data(baseDir='../PhyAAt_Data', subject=1,verbose=0,overwrite=False)

baseDir='../PhyAAt_Data'

SubID = ph.ReadFilesPath(baseDir)

Subj = ph.Subject(SubID[1])

Total Subjects :  1

Subj.filter_EEG(band =[0.5],btype='highpass',order=5)

Extract Raw Signals

XE = Subj.getEEG().to_numpy()
XG = Subj.getGSR().to_numpy()
XP = Subj.getPPG().to_numpy()

plt.figure(figsize=(13,3))
plt.plot(XE[:10000,:]+np.arange(14)*100)
plt.show()

png

plt.figure(figsize=(12,3))
plt.plot(XG[:10000,:])
plt.show()

png

plt.figure(figsize=(12,3))
plt.plot(XP[:10000,:])
plt.show()

png

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