Source code for neurokit2.eeg.eeg_diss
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from .eeg_gfp import eeg_gfp
[docs]
def eeg_diss(eeg, gfp=None, **kwargs):
"""**Global dissimilarity (DISS)**
Global dissimilarity (DISS) is an index of configuration differences between two electric
fields, independent of their strength. Like GFP, DISS was first introduced by Lehmann and
Skrandies (1980). This parameter equals the square root of the mean of the squared differences
between the potentials measured at each electrode (versus the average reference), each of which
is first scaled to unitary strength by dividing by the instantaneous GFP.
Parameters
----------
eeg : np.ndarray
An array (channels, times) of M/EEG data or a Raw or Epochs object from MNE.
gfp : list
The Global Field Power (GFP). If ``None``, will be obtained via ``eeg_gfp()``.
**kwargs
Optional arguments to be passed into ``nk.eeg_gfp()``.
Returns
-------
np.ndarray
DISS of each sample point in the data.
Examples
---------
.. ipython:: python
import neurokit2 as nk
eeg = nk.mne_data("filt-0-40_raw")
eeg = eeg.set_eeg_reference('average')
gfp = nk.eeg_gfp(eeg)
diss = nk.eeg_diss(eeg, gfp=gfp)
@savefig p_eeg_diss1.png scale=100%
nk.signal_plot([gfp[0:300], diss[0:300]], standardize=True)
@suppress
plt.close()
References
----------
* Lehmann, D., & Skrandies, W. (1980). Reference-free identification of components of
checkerboard-evoked multichannel potential fields. Electroencephalography and clinical
neurophysiology, 48(6), 609-621.
"""
if isinstance(eeg, (pd.DataFrame, np.ndarray)) is False:
eeg = eeg.get_data()
if gfp is None:
gfp = eeg_gfp(eeg, **kwargs)
normalized = eeg / gfp
diff = np.diff(normalized, axis=1)
diss = np.mean(np.power(diff, 2), axis=0)
# Preserve length
diss = np.insert(diss, 0, 0, axis=0)
return diss
