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%matplotlib inline

2 - Static Connectivity

In this short tutorial, we will compute the static connectivity of the EEG singals.

Load data



In [2]:

    
import numpy as np



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import scipy
from scipy import io



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eeg = np.load("data/eyes_opened.npy")

num_trials, num_channels, num_samples = np.shape(eeg)



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eeg1 = np.squeeze(eeg[0, :, :])

Static connectivity

As a first example, we are going to compute the static connectivity of the EEG signals using the IPLV estimator.



In [6]:

    
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

from dyconnmap.fc import iplv

Define the frequency band we are interested to examine, in Hz



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band = [1.0, 4.0]

Define the sampling frequency, in Hz



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sampling_frequency = 160.0

We will invoke the estimator using the full by-name arguments. The last arguement, pairs is None by default, which means all "full connectivity", otherwise you check the documentation about the structure of the value.



In [9]:

    
ts, avg = iplv(eeg1, fb=band, fs=sampling_frequency, pairs=None)



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print("""Time series array shape: {0}
Average time series array shape: {1}""".format(np.shape(ts), np.shape(avg)))









    



Time series array shape: (64, 64, 9600)
Average time series array shape: (64, 64)

Make the connectivity matrix symmetric



In [11]:

    
avg_symm = avg + avg.T
np.fill_diagonal(avg_symm, 1.0)

Plot

Plot the matrix using the standard Matplotlib functions



In [12]:

    
import matplotlib.pyplot as plt

mtx_min = 0.0 # we know it's 0.0 because of the estimator's properties
mtx_max = np.max(avg)

plt.figure(figsize=(6, 6))
cax = plt.imshow(avg_symm, vmin=mtx_min, vmax=mtx_max, cmap=plt.cm.Spectral)
cb = plt.colorbar(fraction=0.046, pad=0.04)
cb.ax.set_ylabel('Imaginary PLV', fontdict={'fontsize': 20})
plt.title('Connectivity Matrix', fontdict={'fontsize': 20})
plt.xlabel('Sensor', fontdict={'fontsize': 20})
plt.ylabel('Sensor', fontdict={'fontsize': 20})
plt.show()