#!/usr/bin/env python # coding: utf-8 #

1 EEG Signal
2 Single channel
3 Cross-channel

# ## EEG Signal # In[33]: import numpy as np import matplotlib.pyplot as plt import spkit as sp from spkit.data import load_data print(sp.__version__) # In[34]: X,ch_names = load_data.eegSample() t = np.arange(X.shape[0])/128 nC = len(ch_names) print(X.shape) print(ch_names) # In[35]: plt.figure(figsize=(15,4)) plt.plot(t,X+np.arange(nC)*80) plt.grid() plt.xlim(t[0],t[-1]) plt.show() # ## Single channel # In[3]: x1 =X[:,0] #'AF3' - Frontal Lobe x2 =X[:,6] #'O1' - Occipital Lobe #Shannan entropy H_x1= sp.entropy(x1,alpha=1) H_x2= sp.entropy(x2,alpha=1) #Rényi entropy Hr_x1= sp.entropy(x1,alpha=2) Hr_x2= sp.entropy(x2,alpha=2) #Joint entropy H_x12= sp.entropy_joint(x1,x2) #Conditional Entropy H_x12= sp.entropy_cond(x1,x2) H_x21= sp.entropy_cond(x2,x1) #Mutual Information I_x12 = sp.mutual_Info(x1,x2) #Cross Entropy H_x12_cross= sp.entropy_cross(x1,x2) #Diff Entropy D_x12= sp.entropy_kld(x1,x2) print('Shannan entropy') print('Entropy of x1: H(x1) =\t ',H_x1) print('Entropy of x2: H(x2) =\t ',H_x2) print('-') print('Rényi entropy') print('Entropy of x1: H(x1) =\t ',Hr_x1) print('Entropy of x2: H(x2) =\t ',Hr_x2) print('-') print('Joint Entropy H(x1,x2) =\t',H_x12) print('Mutual Information I(x1,x2) =\t',I_x12) print('Conditional Entropy of : H(x1|x2) =\t',H_x12) print('Conditional Entropy of : H(x2|x1) =\t',H_x21) print('-') print('Cross Entropy of : H(x1,x2) =\t',H_x12_cross) print('Kullback–Leibler divergence : Dkl(x1,x2) =\t',D_x12) # In[10]: H_X= [sp.entropy(x,alpha=1) for x in X.T] # In[11]: plt.figure(figsize=(15,3)) plt.bar(np.arange(nC)*2,H_X) plt.xticks(np.arange(nC)*2,ch_names) plt.ylabel('Entropy') plt.xlabel('channel') plt.show() # ## Cross-channel # In[15]: MI = np.zeros([nC,nC]) JE = np.zeros([nC,nC]) CE = np.zeros([nC,nC]) KL = np.zeros([nC,nC]) for i in range(nC): x1 = X[:,i] for j in range(nC): x2 = X[:,j] #Mutual Information MI[i,j] = sp.mutual_Info(x1,x2) #Joint entropy JE[i,j]= sp.entropy_joint(x1,x2) #Cross Entropy CE[i,j]= sp.entropy_cross(x1,x2) #Diff Entropy KL[i,j]= sp.entropy_kld(x1,x2) # In[48]: plt.figure(figsize=(15,5)) plt.subplot(141) plt.imshow(MI,origin='lower') plt.yticks(np.arange(nC),ch_names) plt.xticks(np.arange(nC),ch_names,rotation=90) plt.title('Mutual Information') plt.subplot(142) plt.imshow(JE,origin='lower') plt.yticks(np.arange(nC),ch_names) plt.xticks(np.arange(nC),ch_names,rotation=90) plt.title('Joint Entropy') plt.subplot(143) plt.imshow(CE,origin='lower') plt.yticks(np.arange(nC),ch_names) plt.xticks(np.arange(nC),ch_names,rotation=90) plt.title('Cross Entropy') plt.subplot(144) plt.imshow(KL,origin='lower') plt.yticks(np.arange(nC),ch_names) plt.xticks(np.arange(nC),ch_names,rotation=90) plt.title('KL-Divergence') plt.show() # In[47]: plt.figure(figsize=(10,10)) plt.subplot(221) plt.imshow(MI,origin='lower') plt.yticks(np.arange(nC),ch_names) plt.xticks(np.arange(nC),ch_names,rotation=90) plt.title('Mutual Information') plt.subplot(222) plt.imshow(JE,origin='lower') plt.yticks(np.arange(nC),ch_names) plt.xticks(np.arange(nC),ch_names,rotation=90) plt.title('Joint Entropy') plt.subplot(223) plt.imshow(CE,origin='lower') plt.yticks(np.arange(nC),ch_names) plt.xticks(np.arange(nC),ch_names,rotation=90) plt.title('Cross Entropy') plt.subplot(224) plt.imshow(KL,origin='lower') plt.yticks(np.arange(nC),ch_names) plt.xticks(np.arange(nC),ch_names,rotation=90) plt.title('KL-Divergence') plt.subplots_adjust(hspace=0.3) plt.show() # In[ ]:

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