Note
Go to the end to download the full example code.
The ModKMeans object#
This tutorial introduces the main clustering object
pycrostates.cluster.ModKMeans structure in detail. The Modified
K-means algorithm is based on Pascual-Marqui et al.[1].
Note
The lemon datasets used in this tutorial is composed of EEGLAB files. To
use the MNE reader mne.io.read_raw_eeglab(), the pymatreader
optional dependency is required. Use the following installation method
appropriate for your environment:
pip install pymatreaderconda install -c conda-forge pymatreader
Note that an environment created via the MNE installers includes
pymatreader by default.
from mne.io import read_raw_eeglab
from pycrostates.cluster import ModKMeans
from pycrostates.datasets import lemon
# load sample dataset
raw_fname = lemon.data_path(subject_id='010017', condition='EC')
raw = read_raw_eeglab(raw_fname, preload=True)
raw.crop(0, 10) # crop the dataset to speed up computation
raw.pick('eeg') # select EEG channels
raw.set_eeg_reference('average') # Apply a common average reference
/home/docs/checkouts/readthedocs.org/user_builds/pycrostates/checkouts/0.6.1/tutorials/cluster/00_modK.py:35: RuntimeWarning: Limited 1 annotation(s) that were expanding outside the data range.
raw = read_raw_eeglab(raw_fname, preload=True)
/home/docs/checkouts/readthedocs.org/user_builds/pycrostates/checkouts/0.6.1/tutorials/cluster/00_modK.py:35: RuntimeWarning: The data contains 'boundary' events, indicating data discontinuities. Be cautious of filtering and epoching around these events.
raw = read_raw_eeglab(raw_fname, preload=True)
The modified K-means[1] can be instantiated with the
number of cluster centers n_clusters to fit. By default, the
modified K-means will only work with EEG data, but other channel types can be
selected with the picks argument.
Note
A K-means algorithm starts with a random initialization. Thus, 2 separate
fit will not yield the same cluster centers. To achieve
reproductible fits, the random_state argument can be used.
n_clusters = 5
ModK = ModKMeans(n_clusters=n_clusters, random_state=42)
After creating a ModKMeans, the next step is to
fit the model. In other words, fitting a clustering algorithm will determine
the microstate maps, also called cluster centers. A clustering
algorithm can be fitted with Raw,
Epochs or ChData objects.
Note
Fitting a clustering algorithm is a computationally expensive operation.
Depending on your configuration, you can change the argument n_jobs
to take advantage of multiprocessing to reduce computation time.
ModK.fit(raw, n_jobs=5)
Now that our algorithm is fitted, we can visualize the
cluster centers, also called microstate maps or microstate
topographies using pycrostates.cluster.ModKMeans.plot().
Setting show_gradient will plot a line between maximum and minimum
value of each :topography to help visualization.
ModK.plot(show_gradient=True)
<Figure size 1500x300 with 5 Axes>
The cluster centers can be retrieved as a numpy array with the
cluster_centers_ attribute.
array([[-0.10613817, -0.08201757, -0.12199506, -0.18139598, -0.15155429,
-0.0663304 , 0.02897557, -0.14330831, -0.18287982, -0.04394161,
0.08147253, -0.09112043, -0.17166961, -0.08167475, 0.18571105,
0.14984234, -0.08960343, -0.05970102, 0.07963704, 0.20116081,
-0.13068572, -0.01966152, -0.02754429, 0.04342553, 0.17641103,
0.21959322, 0.05706008, 0.07466215, 0.11798016, 0.19335687,
0.18025677, -0.1144574 , -0.1478337 , -0.09806771, -0.04387633,
-0.17137089, -0.18059281, -0.11470747, -0.02275574, -0.12266696,
-0.19038825, 0.06154834, 0.09349215, -0.12954759, -0.13551292,
0.03178528, 0.17005063, -0.06435352, -0.10106101, -0.00582698,
0.17539028, 0.17856175, -0.0303554 , 0.00050519, 0.10891339,
0.21417162, 0.03736816, 0.04401132, 0.09312374, 0.18100948,
0.24512021],
[-0.13111746, -0.12902983, -0.11675051, -0.15042743, -0.16057124,
-0.1403919 , -0.09526587, -0.08700381, -0.12771863, -0.1217802 ,
-0.08104217, -0.0239761 , -0.05611484, -0.09596225, -0.03857956,
-0.01434362, 0.03886455, -0.0018377 , -0.00541398, 0.04019904,
-0.15093261, 0.14666805, 0.11686381, 0.07052023, 0.10434424,
0.13091987, 0.16818435, 0.23991 , 0.2183577 , 0.23749697,
0.16065367, -0.12242205, -0.146566 , -0.14186211, -0.11287111,
-0.13646411, -0.15545744, -0.15299391, -0.12082361, -0.07459982,
-0.11163429, -0.09491519, -0.05882318, -0.03486554, -0.07277418,
-0.06587263, -0.02630238, 0.05202749, 0.01674379, -0.0182837 ,
0.02217596, 0.05108456, 0.15117688, 0.08485902, 0.07661277,
0.13336997, 0.27111671, 0.22909926, 0.17786281, 0.205983 ,
0.23069625],
[ 0.02779826, 0.03672887, -0.01403513, 0.08561708, 0.12284625,
0.09263314, -0.01734032, 0.02030784, 0.14198503, 0.1391721 ,
0.01744079, -0.08370413, 0.10970859, 0.17051393, 0.09571316,
-0.07978598, -0.02033802, 0.15985952, 0.14735075, -0.01071003,
0.08177897, -0.20210553, 0.00769019, 0.13026738, 0.01006383,
-0.20961216, -0.29306345, -0.21966413, -0.16913333, -0.20494208,
-0.27757552, 0.00050768, 0.06783559, 0.07105905, 0.02605742,
0.04486 , 0.11633317, 0.12026605, 0.04057292, -0.02651827,
0.09721272, 0.09415869, -0.04232562, 0.00503379, 0.1622934 ,
0.15225462, 0.00137598, -0.13584456, 0.08847654, 0.17401177,
0.07749566, -0.14216958, -0.11198173, 0.0924721 , 0.09103009,
-0.12064577, -0.28421354, -0.0990506 , 0.01412113, -0.07116736,
-0.29897722],
[-0.04938093, -0.07785227, 0.00770551, 0.01101049, -0.05837494,
-0.12745891, -0.15698158, 0.06574787, 0.03392793, -0.12405229,
-0.18658613, 0.11186122, 0.12852662, -0.04732016, -0.23913259,
-0.1712453 , 0.15846057, 0.04256348, -0.10098047, -0.1684352 ,
-0.05965646, 0.22080474, 0.16657397, 0.02944206, -0.07509873,
-0.06890225, 0.21737555, 0.2243419 , 0.15036062, 0.06991825,
0.03945278, -0.01795136, -0.02561908, -0.08698183, -0.09866253,
0.03043127, -0.00789797, -0.09340749, -0.13986545, 0.07017042,
0.07929222, -0.1920744 , -0.17015418, 0.12104299, 0.04927906,
-0.12340832, -0.20853367, 0.16821225, 0.13028452, -0.03041975,
-0.16128474, -0.11930113, 0.21977491, 0.0935787 , -0.03421184,
-0.07221452, 0.2775595 , 0.22292352, 0.10947146, 0.03114246,
0.01220959],
[-0.17620438, -0.1719866 , -0.17832709, -0.16244999, -0.16469275,
-0.1358235 , -0.12722994, -0.10439042, -0.06896094, -0.0470142 ,
-0.05118532, -0.09523244, 0.01847518, 0.02329203, 0.12642891,
-0.03402674, 0.01108625, 0.13405863, 0.17001125, 0.11175882,
-0.17734789, -0.01852281, 0.12458657, 0.21693135, 0.18852973,
0.04692831, -0.03457755, 0.08719755, 0.12062435, 0.13383 ,
0.00104893, -0.17736363, -0.1742995 , -0.16735761, -0.15338764,
-0.16563225, -0.15219861, -0.14775802, -0.13353123, -0.12379262,
-0.07489002, -0.00684089, -0.076171 , -0.04831051, 0.04883857,
0.08626472, 0.0396429 , -0.06345966, 0.08263075, 0.1539134 ,
0.16933995, -0.00330307, 0.05710078, 0.18121899, 0.21100428,
0.12345016, 0.05184786, 0.16488236, 0.23127897, 0.21062865,
0.08943864]])
By default, the cluster centers are named from 0 to
n_clusters - 1 and are ordered based on the fit. You can reorder
(pycrostates.cluster.ModKMeans.reorder_clusters()) and
rename (pycrostates.cluster.ModKMeans.rename_clusters()) each
microstates to match your preference.
ModK.reorder_clusters(order=[3, 0, 1, 2, 4])
ModK.rename_clusters(new_names=['A', 'B', 'C', 'D', 'F'])
ModK.plot()
<Figure size 1500x300 with 5 Axes>
The map polarities can be inverted using the
pycrostates.cluster.ModKMeans.invert_polarity().
method. Note that it only affects visualization, it has not effect during
backfitting as polarities are ignored.
ModK.invert_polarity([False, False, True, True, False])
ModK.plot()
<Figure size 1500x300 with 5 Axes>
Total running time of the script: (0 minutes 17.431 seconds)
Estimated memory usage: 256 MB