Note

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EEG Topographic Map¶

import numpy as np
import matplotlib.pyplot as plt
#from spkit import ICA
import spkit as sp
print('spkit version :', sp.__version__)

spkit version : 0.0.9.7

Load and filter EEG Signal¶

# Load sample EEG Data ( 16 sec, 128 smapling rate, 14 channel)
# Filter signal (with IIR highpass 0.5Hz)

X, fs, ch_names = sp.data.eeg_sample_14ch()

Xf = sp.filter_X(X.copy(),fs=128.0, band=[0.5], btype='highpass',ftype='SOS')

XR = sp.eeg.ATAR(Xf.copy(),wv='db3', winsize=128, thr_method='ipr',beta=0.2, k1=10, k2=100,OptMode='elim')

Rhythmic Powers,¶

# Total

Pm = sp.eeg.rhythmic_powers(X=XR.copy(),fs=fs,fBands=[[8,14], [15,32]],Sum=True,Mean=False,SD =True)[0]
Pm = 20*np.log10(Pm)

# in windows

Pmt = sp.eeg.rhythmic_powers_win(X=XR.copy(),winsize=128*2,overlap=64,fs=fs,
                                 fBands=[[8,14], [15,32]],Sum=True,Mean=False,SD =True)[0]
Pmt = 20*np.log10(Pmt)

Topomap of Total power¶

fig,ax = plt.subplots(1,2,figsize=(10,4))
Z1,im1 = sp.eeg.topomap(data=Pm[0],ch_names=ch_names,shownames=False,axes=ax[0],return_im=True)
Z2,im2 =sp.eeg.topomap(data=Pm[1],ch_names=ch_names,shownames=False,axes=ax[1],return_im=True)

ax[0].set_title(r'$\alpha$ : [8-14] Hz')
ax[1].set_title(r'$\beta$ : [15-32] Hz')
plt.colorbar(im1, ax=ax[0],label='dB')
plt.colorbar(im2, ax=ax[1],label='dB')
plt.suptitle('Spkit-style')
plt.show()

fig,ax = plt.subplots(1,2,figsize=(10,4))
Z1,im1 = sp.eeg.topomap(data=Pm[0],ch_names=ch_names,shownames=False,axes=ax[0],return_im=True,style='eeglab-mne')
Z2,im2 =sp.eeg.topomap(data=Pm[1],ch_names=ch_names,shownames=False,axes=ax[1],return_im=True,style='eeglab-mne')

ax[0].set_title(r'$\alpha$ : [8-14] Hz')
ax[1].set_title(r'$\beta$ : [15-32] Hz')
plt.colorbar(im1, ax=ax[0],label='dB')
plt.colorbar(im2, ax=ax[1],label='dB')
plt.suptitle('eeglab/mne-style')
plt.show()

$Spkit-style, $\alpha$ : [8-14] Hz, $\beta$ : [15-32] Hz$
$eeglab/mne-style, $\alpha$ : [8-14] Hz, $\beta$ : [15-32] Hz$

Topomap for specific time-windows¶

t1,t2 = 7,18

plt.figure(figsize=(10,4))
plt.subplot(211)
plt.plot(Pmt[:,0,:])
plt.xlim([0,Pmt.shape[0]-1])
plt.vlines([t1,t2],ymin=Pmt[:,0,:].min()-5,ymax=Pmt[:,0,:].max(),color='k')
plt.grid()
plt.ylabel(r'$\alpha$ (dB)')
plt.subplot(212)
plt.plot(Pmt[:,1,:])
plt.xlim([0,Pmt.shape[0]-1])
plt.vlines([t1,t2],ymin=Pmt[:,0,:].min()-5,ymax=Pmt[:,0,:].max(),color='k')
plt.grid()
plt.ylabel(r'$\beta$ (dB)')
plt.xlabel('window #')
plt.text(t1,0,'t1',fontsize=15,ha='center')
plt.text(t2,0,'t2',fontsize=15,ha='center')
plt.subplots_adjust(hspace=0)
plt.show()

fig,ax = plt.subplots(1,2,figsize=(10,4))
Z1,im1 = sp.eeg.topomap(data=Pmt[t1,0,:],ch_names=ch_names,vmin=Pmt[t1].min(),vmax=Pmt[t1].max(),
                        shownames=False,axes=ax[0],return_im=True)
Z2,im2 =sp.eeg.topomap(data=Pmt[t1,1,:],ch_names=ch_names,vmin=Pmt[t1].min(),vmax=Pmt[t1].max(),
                       shownames=False,axes=ax[1],return_im=True)

ax[0].set_title(r'$\alpha$ : [8-14] Hz')
ax[1].set_title(r'$\beta$ : [15-32] Hz')
#plt.colorbar(im1, ax=ax[0],label='dB')
plt.colorbar(im2, ax=ax[1],label='dB')
plt.suptitle('t1')
plt.show()

fig,ax = plt.subplots(1,2,figsize=(10,4))
Z1,im1 = sp.eeg.topomap(data=Pmt[t2,0,:],ch_names=ch_names,vmin=Pmt[t2].min(),vmax=Pmt[t2].max(),
                        shownames=False,axes=ax[0],return_im=True)
Z2,im2 =sp.eeg.topomap(data=Pmt[t2,1,:],ch_names=ch_names,vmin=Pmt[t2].min(),vmax=Pmt[t2].max(),
                       shownames=False,axes=ax[1],return_im=True)

ax[0].set_title(r'$\alpha$ : [8-14] Hz')
ax[1].set_title(r'$\beta$ : [15-32] Hz')
#plt.colorbar(im1, ax=ax[0],label='dB')
plt.colorbar(im2, ax=ax[1],label='dB')
plt.suptitle('t2')
plt.show()

$t1, $\alpha$ : [8-14] Hz, $\beta$ : [15-32] Hz$
$t2, $\alpha$ : [8-14] Hz, $\beta$ : [15-32] Hz$