Note
Go to the end to download the full example code.
Run AMICA on EEG Data#
In this tutorial we will:
Run AMICA on EEG data
Compare our results to Fortran AMICA.
Export our AMICA instance to an
mne.preprocessing.ICAobject.
import amica
from amica import AMICA
import matplotlib.pyplot as plt
import mne
import numpy as np
Download sample data#
data_path = amica.datasets.data_path()
Load Fortran AMICA initial weights and results#
This is not necessary to run AMICA in Python, but we are going to compare amica-python results to those obtained from the original Fortran implementation.
initial_weights, initial_scales, initial_locations = amica.utils.load_initial_weights(
data_path / "eeglab_sample_data" / "amicaout_test", n_components=32, n_mixtures=3
)
Load EEG data#
amica_outdir = data_path / "eeglab_sample_data" / "amicaout_test"
fortran_results = amica.utils.load_fortran_results(
amica_outdir, n_components=32, n_mixtures=3
)
raw = mne.io.read_raw_eeglab(
data_path / "eeglab_sample_data"/ "eeglab_data.set", preload=True
)
data = raw.get_data().T # Shape (n_samples, n_channels)
data *= 1e6 # Convert from Volts to microVolts
Reading /home/circleci/amica_test_data/eeglab_sample_data/eeglab_data.fdt
Reading 0 ... 30503 = 0.000 ... 238.305 secs...
Run AMICA-Python#
transformer = AMICA(
max_iter=2000,
w_init=initial_weights,
sbeta_init=initial_scales,
mu_init=initial_locations,
tol=1e-7,
verbose=2,
)
transformer.fit(data)
INFO getting the mean ...
INFO Getting the covariance matrix ...
INFO doing eigenvalue decomposition for 32 features ...
INFO minimum eigenvalues: [4.87990051 6.92011971 7.65621479]
INFO maximum eigenvalues: [9711.14308385 3039.68504351 1244.41294471]
INFO num eigvals kept: 32
INFO Sphering the data...
INFO numeigs = 32, nw = 32
INFO 1: block size = 30504
INFO Solving. (please be patient, this may take a while)...
INFO Iteration 1, lrate = 0.05000, LL = -3.5136812, nd = 0.0578126, D =
0.00000 took 0.54 seconds
INFO Iteration 2, lrate = 0.05000, LL = -3.4687938, nd = 0.0254382, D =
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INFO Iteration 3, lrate = 0.05000, LL = -3.4616091, nd = 0.0243398, D =
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INFO Starting Newton ... setting numdecs to 0
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INFO Iteration 566, lrate = 1.00000, LL = -3.3984565, nd = 0.0002664, D =
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WARNING Likelihood decreasing!
INFO Iteration 567, lrate = 0.60000, LL = -3.3984571, nd = 0.0003020, D =
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WARNING Likelihood decreasing!
INFO Iteration 568, lrate = 0.40000, LL = -3.3984547, nd = 0.0002243, D =
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INFO Iteration 569, lrate = 0.50000, LL = -3.3984529, nd = 0.0001489, D =
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INFO Iteration 585, lrate = 1.00000, LL = -3.3984468, nd = 0.0002201, D =
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WARNING Likelihood decreasing!
INFO Reducing maximum Newton lrate
INFO Iteration 586, lrate = 0.50000, LL = -3.3984469, nd = 0.0002419, D =
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WARNING Likelihood decreasing!
INFO Iteration 587, lrate = 0.35000, LL = -3.3984446, nd = 0.0001465, D =
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INFO Iteration 588, lrate = 0.45000, LL = -3.3984435, nd = 0.0000961, D =
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INFO Iteration 621, lrate = 0.50000, LL = -3.3984331, nd = 0.0000693, D =
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INFO Iteration 623, lrate = 0.50000, LL = -3.3984325, nd = 0.0000686, D =
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INFO Iteration 626, lrate = 0.50000, LL = -3.3984316, nd = 0.0000666, D =
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INFO Iteration 627, lrate = 0.50000, LL = -3.3984313, nd = 0.0000663, D =
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INFO Iteration 628, lrate = 0.50000, LL = -3.3984311, nd = 0.0000664, D =
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INFO Iteration 630, lrate = 0.50000, LL = -3.3984305, nd = 0.0000658, D =
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INFO Iteration 631, lrate = 0.50000, LL = -3.3984302, nd = 0.0000656, D =
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INFO Iteration 632, lrate = 0.50000, LL = -3.3984299, nd = 0.0000652, D =
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INFO Iteration 633, lrate = 0.50000, LL = -3.3984296, nd = 0.0000650, D =
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INFO Iteration 635, lrate = 0.50000, LL = -3.3984291, nd = 0.0000642, D =
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INFO Iteration 641, lrate = 0.50000, LL = -3.3984274, nd = 0.0000633, D =
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INFO Iteration 650, lrate = 0.50000, LL = -3.3984250, nd = 0.0000630, D =
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INFO Iteration 653, lrate = 0.50000, LL = -3.3984242, nd = 0.0000629, D =
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INFO Iteration 655, lrate = 0.50000, LL = -3.3984236, nd = 0.0000630, D =
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INFO Iteration 656, lrate = 0.50000, LL = -3.3984234, nd = 0.0000628, D =
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INFO Iteration 678, lrate = 0.50000, LL = -3.3984175, nd = 0.0000647, D =
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INFO Iteration 681, lrate = 0.50000, LL = -3.3984167, nd = 0.0000645, D =
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INFO Iteration 703, lrate = 0.50000, LL = -3.3984110, nd = 0.0000696, D =
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INFO Iteration 710, lrate = 0.50000, LL = -3.3984094, nd = 0.0000655, D =
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INFO Iteration 1201, lrate = 0.50000, LL = -3.3983329, nd = 0.0000402, D =
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INFO Iteration 1204, lrate = 0.50000, LL = -3.3983326, nd = 0.0000417, D =
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INFO Iteration 1206, lrate = 0.50000, LL = -3.3983324, nd = 0.0000424, D =
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INFO Exiting because likelihood increasing by less than 1e-07 for more than
5 iterations ...
INFO Finished in 748.13 seconds
Compare results#
def plot_topomaps(A, output="python"):
fig, ax = plt.subplots(
nrows=8,
ncols=4,
figsize=(8, 12),
constrained_layout=True
)
for i, this_ax in zip(range(32), ax.flat):
mne.viz.plot_topomap(
A[:, i],
pos=raw.info,
axes=this_ax,
show=False,
)
this_ax.set_title(f"Component {i}")
fig.suptitle(f"AMICA Component Topomaps ({output})", fontsize=16)
return fig, ax
fig1, ax1 = plot_topomaps(transformer.mixing_, output="python")

The Fortran mixing matrix is in sphered space. We need to unwhiten it first.
A_fortran = np.linalg.pinv(fortran_results['S']) @ fortran_results['A']
fig2, ax2 = plot_topomaps(A_fortran, output="fortran")

Export to MNE#
ica = transformer.to_mne(info=raw.info)
ica.plot_components(nrows=8, ncols=4)

<MNEFigure size 780x1909 with 32 Axes>
Total running time of the script: (12 minutes 37.826 seconds)