[GSOC] Add decomposition plotting

doc/conf.py

@@ -124,6 +124,8 @@
     "n_estimated_nodes",
     "n_samples",
     "n_channels",
+    "n_patterns",
+    "n_filters",
     "Renderer",
     "n_ytimes",
     "n_ychannels",

examples/decoding/cohy_decomposition.py

@@ -3,10 +3,10 @@
 Multivariate decomposition for efficient connectivity analysis
 ==============================================================
 
-This example demonstrates how the tools in the decoding module can be used to
-decompose data into the most relevant components of connectivity and used for
-a computationally efficient multivariate analysis of connectivity, such as in
-brain-computer interface (BCI) applications.
+This example demonstrates how the tools in the decoding module can be used to decompose
+data into the most relevant components of connectivity and used for a computationally
+efficient multivariate analysis of connectivity, such as in brain-computer interface
+(BCI) applications.
 """
 
 # Author: Thomas S. Binns <[email protected]>
@@ -21,23 +21,22 @@
 from matplotlib import pyplot as plt
 
 from mne_connectivity import (
+    CoherencyDecomposition,
     make_signals_in_freq_bands,
     seed_target_indices,
     spectral_connectivity_epochs,
 )
-from mne_connectivity.decoding import CoherencyDecomposition
 
 ########################################################################################
 # Background
 # ----------
-#
-# Multivariate forms of signal analysis allow you to simultaneously consider
-# the activity of multiple signals. In the case of connectivity, the
-# interaction between multiple sensors can be analysed at once and the strongest
-# components of this interaction captured in a lower-dimensional set of connectivity
-# spectra. This approach brings not only practical benefits (e.g. easier
-# interpretability of results from the dimensionality reduction), but can also offer
-# methodological improvements (e.g. enhanced signal-to-noise ratio and reduced bias).
+# Multivariate forms of signal analysis allow you to simultaneously consider the
+# activity of multiple signals. In the case of connectivity, the interaction between
+# multiple sensors can be analysed at once and the strongest components of this
+# interaction captured in a lower-dimensional set of connectivity spectra. This approach
+# brings not only practical benefits (e.g. easier interpretability of results from the
+# dimensionality reduction), but can also offer methodological improvements (e.g.
+# enhanced signal-to-noise ratio and reduced bias).
 #
 # Coherency-based methods are popular approaches for analysing connectivity, capturing
 # correlations between signals in the frequency domain. Various coherency-based
@@ -244,8 +243,7 @@
 # connectivity is present. This problem can be mitigated by fitting filters to only
 # those frequencies where you expect connectivity to be present, e.g. as is done with
 # the decomposition class.
-
-########################################################################################
+#
 # In addition to assessing the validity of the approach, we can also look at the time
 # taken to run the analysis. Doing so, we see that the decomposition class is much
 # faster than the ``spectral_connectivity_...()`` functions, thanks to the fact that the
@@ -550,6 +548,20 @@
 # Ultimately, there are distinct advantages and disadvantages to both approaches, and
 # one may be more suitable than the other depending on your use case.
 
+########################################################################################
+# Visualising spatial contributions to connectivity
+# -------------------------------------------------
+# In addition to the lower-dimensional representation of connectivity, we can also
+# extract information about the spatial distributions of connectivity over channels.
+# This information is captured in the spatial patterns, derived from the spatial
+# filters :footcite:`HaufeEtAl2014`.
+#
+# The patterns (and filters) can be visualised as topomaps using the
+# :meth:`~mne_connectivity.decoding.CoherencyDecomposition.plot_patterns` and
+# :meth:`~mne_connectivity.decoding.CoherencyDecomposition.plot_filters` methods of the
+# :class:`~mne_connectivity.decoding.CoherencyDecomposition` class, discussed in more
+# detail in :doc:`cohy_decomposition_plotting`.
+
 ########################################################################################
 # References
 # ----------

examples/decoding/cohy_decomposition_plotting.py

@@ -0,0 +1,172 @@
+"""
+==============================================================
+Visualising spatial contributions to multivariate connectivity
+==============================================================
+
+This example demonstrates how the spatial filters and patterns of connectivity obtained
+from the decomposition tools in the decoding module can be visualised and interpreted.
+"""
+
+# Author: Thomas S. Binns <[email protected]>
+# License: BSD (3-clause)
+
+# %%
+
+import mne
+from mne import make_fixed_length_epochs
+from mne.datasets.fieldtrip_cmc import data_path
+
+from mne_connectivity import CoherencyDecomposition
+
+########################################################################################
+# Background
+# ----------
+# Multivariate forms of signal analysis allow you to simultaneously consider the
+# activity of multiple signals. In the case of connectivity, the interaction between
+# multiple sensors can be analysed at once and the strongest components of this
+# interaction captured in a lower-dimensional set of connectivity spectra. This approach
+# brings not only practical benefits (e.g. easier interpretability of results from the
+# dimensionality reduction), but can also offer methodological improvements (e.g.
+# enhanced signal-to-noise ratio and reduced bias).
+#
+# Coherency-based methods are popular approaches for analysing connectivity, capturing
+# correlations between signals in the frequency domain. Various coherency-based
+# multivariate methods exist, including: canonical coherency (CaCoh; multivariate
+# measure of coherency/coherence) :footcite:`VidaurreEtAl2019`; and maximised imaginary
+# coherency (MIC; multivariate measure of the imaginary part of coherency)
+# :footcite:`EwaldEtAl2012`.
+#
+# These methods are described in detail in the following examples:
+#  - comparison of coherency-based methods - :doc:`../compare_coherency_methods`
+#  - CaCoh - :doc:`../cacoh`
+#  - MIC - :doc:`../mic_mim`
+#
+# The CaCoh and MIC methods work by finding spatial filters that decompose the data into
+# components of connectivity, and applying them to the data. Connectivity can then be
+# computed on this transformed data (see :doc:`cohy_decomposition` for more
+# information).
+#
+# However, in addition to the connectivity scores, useful insights about the data can be
+# gained by visualising the topographies of the spatial filters and their corresponding
+# spatial patterns. These provide important information about the spatial distributions
+# of connectivity information, and represent two complementary aspects:
+#
+# - The filters represent how the connectivity sources are extracted from the channel
+#   data, akin to an inverse model.
+# - The patterns represent how the channel data is formed by the connectivity sources,
+#   akin to a forward model.
+#
+# This distinction is discussed further in Haufe *et al.* (2014)
+# :footcite:`HaufeEtAl2014`, but in short: **the patterns should be used to interpret
+# the contribution of distinct brain regions/sensors to a given component of
+# connectivity**. Accordingly, keep in mind that the filters and patterns are not a
+# replacement for source reconstruction, as without this the patterns will still only
+# tell you about the spatial contributions of sensors, not underlying brain regions,
+# to connectivity.
+
+########################################################################################
+# Generating the filters and patterns
+# -----------------------------------
+# We will first load some example MEG data collected during a hand movement task, which
+# we will generate the spatial filters and patterns for (see
+# `here <https://www.fieldtriptoolbox.org/tutorial/coherence/>`_ for more information on
+# the data). We divide the data into continuous epochs.
+
+# %%
+
+# Load example MEG data
+raw = mne.io.read_raw_ctf(data_path() / "SubjectCMC.ds")
+raw.pick("mag")
+raw.crop(50.0, 110.0).load_data()
+raw.notch_filter(50)
+raw.resample(100)
+
+# Create epochs
+epochs = make_fixed_length_epochs(raw, duration=2.0).load_data()
+
+########################################################################################
+# We designate the left hemisphere sensors as the seeds and the right hemisphere sensors
+# as the targets. Since this is sensor-space data, we will use the MIC method to analyse
+# connectivity, given its resilience to zero time-lag interactions (see
+# :doc:`../compare_coherency_methods` for more information).
+
+# %%
+
+# Left hemisphere sensors
+seeds = [idx for idx, ch_info in enumerate(epochs.info["chs"]) if ch_info["loc"][0] < 0]
+
+# Right hemisphere sensors
+targets = [
+    idx for idx, ch_info in enumerate(epochs.info["chs"]) if ch_info["loc"][0] > 0
+]
+
+# Define indices
+indices = (seeds, targets)
+
+########################################################################################
+# To fit the filters (and in turn compute the corresponding patterns), we instantiate
+# the :class:`~mne_connectivity.decoding.CoherencyDecomposition` object and call the
+# :meth:`~mne_connectivity.decoding.CoherencyDecomposition.fit` method. We also define
+# our connectivity frequency band of interest to be 20-30 Hz. See
+# :doc:`cohy_decomposition` for more information.
+
+# %%
+
+# Instantiate decomposition object
+mic = CoherencyDecomposition(
+    info=epochs.info,
+    method="mic",
+    indices=indices,
+    mode="multitaper",
+    fmin=20,
+    fmax=30,
+    rank=(3, 3),
+)
+
+# Fit filters & generate patterns
+mic.fit(epochs.get_data())
+
+########################################################################################
+# Visualising the patterns
+# ------------------------
+# Visualising the patterns as topomaps can be done using the
+# :meth:`~mne_connectivity.decoding.CoherencyDecomposition.plot_patterns` method.
+#
+# When interpreting patterns, note that the absolute value reflects the strength of the
+# contribution to connectivity, and that the sign differences can be used to visualise
+# the orientation of the underlying dipole sources. The spatial patterns are **not**
+# bound between :math:`[-1, 1]`.
+#
+# Plotting the patterns for 20-30 Hz connectivity below, we find the strongest
+# connectivity between the left and right hemispheres comes from centromedial left and
+# frontolateral right sensors, based on the areas with the largest absolute values. As
+# these patterns come from decomposition on sensor-space data, we make no assumptions
+# about the underlying brain regions involved in this connectivity.
+
+# %%
+
+# Plot patterns
+mic.plot_patterns(info=epochs.info, sensors="m.", size=2)
+
+########################################################################################
+# Visualising the filters
+# -----------------------
+# We can also visualise the filters as topomaps using the
+# :meth:`~mne_connectivity.decoding.CoherencyDecomposition.plot_filters` method.
+#
+# Here we see that the filters show a similar topography to the patterns. However, this
+# is not always the case, and you should never confuse the information represented by
+# the filters (i.e. an inverse model) and patterns (i.e. a forward model), which can
+# lead to very incorrect interpretations of the data :footcite:`HaufeEtAl2014`.
+
+# %%
+
+# Plot filters
+mic.plot_filters(info=epochs.info, sensors="m.", size=2)
+
+########################################################################################
+# References
+# ----------
+# .. footbibliography::
+
+# %%

mne_connectivity/__init__.py

@@ -24,6 +24,7 @@
     TemporalConnectivity,
 )
 from .datasets import make_signals_in_freq_bands
+from .decoding import CoherencyDecomposition
 from .effective import phase_slope_index
 from .envelope import envelope_correlation, symmetric_orth
 from .io import read_connectivity