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%matplotlib inline

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

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/eeg_eyes_opened.npy")

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

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eeg_ts = 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.

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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.

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

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print(f"""Time series array shape: {np.shape(ts)}
Average time series array shape: {np.shape(avg)}""")

Make the connectivity matrix symmetric

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

Plot¶

Plot the matrix using the standard Matplotlib functions

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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('ROI', fontdict={'fontsize': 20})
plt.ylabel('ROI', fontdict={'fontsize': 20})
plt.show()

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