Importing libraries/spkit¶
In [3]:
import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
from spkit.data import load_data
sp.__version__
Out[3]:
'0.0.9.3'
14 channel EEG sample signas¶
In [4]:
X,ch_names = load_data.eegSample()
fs = 128
Xf = sp.filter_X(X,band=[0.5], btype='highpass',fs=fs,verbose=0).T
Xf.shape
Out[4]:
(2048, 14)
In [5]:
t = np.arange(Xf.shape[0])/fs
plt.figure(figsize=(12,5))
plt.plot(t,Xf+np.arange(-7,7)*200)
plt.xlim([t[0],t[-1]])
plt.xlabel('time (sec)')
plt.yticks(np.arange(-7,7)*200,ch_names)
plt.grid()
plt.title('Xf: 14 channel - EEG Signal (filtered)')
plt.show()
Artifact removal using ICA¶
In [6]:
XR = sp.eeg.ICA_filtering(Xf.copy(),verbose=1,kur_thr=2,corr_thr=0.8,winsize=128)
XR.shape
ICA Artifact Removal : extended-infomax 100.05%|##################################################|2112\2113| Done..
Out[6]:
(2048, 14)
In [7]:
plt.figure(figsize=(12,5))
plt.plot(t,XR+np.arange(-7,7)*200)
plt.xlim([t[0],t[-1]])
plt.xlabel('time (sec)')
plt.yticks(np.arange(-7,7)*200,ch_names)
plt.grid()
plt.title('XR: Corrected Signal')
plt.show()
plt.figure(figsize=(12,5))
plt.plot(t,(Xf-XR)+np.arange(-7,7)*200)
plt.xlim([t[0],t[-1]])
plt.xlabel('time (sec)')
plt.yticks(np.arange(-7,7)*200,ch_names)
plt.grid()
plt.title('Xf - XR: Difference (removed signal)')
plt.show()
