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%matplotlib inline

3 - Dynamic Connectivity

In this short tutorial, we will build and expand on the previous tutorials by computing the dynamic connectivity, using Time-Varying Functional Connectivity Graphs.

In the near future, the standard method of "sliding window" will be supported.

Load data



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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, :, :])

Dynamic connectivity

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



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

from dyconnmap import tvfcg
from dyconnmap.fc import IPLV

First, setup the configuration options

frequency band, fb
sampling frequency, fs
cycle-criterion, cc
steping samples, step



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fb = [1.0, 4.0]
cc = 3.0
fs = 160.0
step = 32

Declare and instantiate which estimator we will use to compute the dynamic connectivity. In this case we use again IPLV.

Notice As you might have noticed, the following line intantiates an object. We only need to pass two parameters, the fb and fs.



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estimator = IPLV(fb, fs)

Now we are ready to estimate the dynamic functional connectivity.



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fcgs = tvfcg(eeg1, estimator, fb, fs, cc, step)



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num_fcgs, num_channels, num_channels = np.shape(fcgs)



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print("{0} FCGs of shape {1}x{2}".format(num_fcgs, num_channels, num_channels))









    



285 FCGs of shape 64x64



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print("FCGs array data type is {0}".format(fcgs.dtype))









    



FCGs array data type is complex128

Because of the nature of the estimator, notice the FCG's data type; for compatibility reasons, it is np.complex128. We have to use np.real to get the real part.



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rfcgs = np.real(fcgs)

Plot

Plot a few FCGs using the standard Matplotlib functions



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import matplotlib.pyplot as plt

slices = np.linspace(0, num_fcgs - 1, 5, dtype=np.int32)
num_slices = len(slices)

mtx_min = 0.0
mtx_max = np.max(rfcgs)

f, axes = plt.subplots(ncols=num_slices, figsize=(14, 14))
for i, s in enumerate(slices):
    slice_mtx = rfcgs[s, :, :] + rfcgs[s, :, :].T
    np.fill_diagonal(slice_mtx, 1.0)
    cax  = axes[i].imshow(slice_mtx, vmin=mtx_min, vmax=mtx_max, cmap=plt.cm.Spectral)
    axes[i].set_title('#{0}'.format(s))

# move the colorbar to the side ;)
f.subplots_adjust(right=0.8)
cbar_ax = f.add_axes([0.82, 0.445, 0.0125, 0.115])
cb = f.colorbar(cax, cax=cbar_ax)
cb.set_label('Imaginary PLV')



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