[MRG][DOC] Add GFP to the glossary

doc/faq.rst

@@ -238,19 +238,19 @@ Under the assumption that the baseline does not contain a systematic signal
 (time-locked to the event of interest), the whitened baseline signal should be
 follow a multivariate Gaussian distribution, i.e.,
 whitened baseline signals should be between -1.96 and 1.96 at a given time sample.
-Based on the same reasoning, the expected value for the global field power (GFP)
-is 1 (calculation of the GFP should take into account the true degrees of
-freedom, e.g. ``ddof=3`` with 2 active SSP vectors)::
+Based on the same reasoning, the expected value for the :term:`Global Field
+Power (GFP) <GFP>` is 1 (calculation of the :term:`GFP` should take into account the true
+degrees of freedom, e.g. ``ddof=3`` with 2 active SSP vectors)::
 
     >>> evoked = epochs.average() # doctest: +SKIP
     >>> evoked.plot_white(cov) # doctest: +SKIP
 
-This plot displays both, the whitened evoked signals for each channels and
-the whitened GFP. The numbers in the GFP panel represent the estimated rank of
-the data, which amounts to the effective degrees of freedom by which the
-squared sum across sensors is divided when computing the whitened GFP.
-The whitened GFP also helps detecting spurious late evoked components which
-can be the consequence of over- or under-regularization.
+This plot displays both, the whitened evoked signals for each channels and the
+whitened :term:`GFP`. The numbers in the :term:`GFP` panel represent the
+estimated rank of the data, which amounts to the effective degrees of freedom by
+which the squared sum across sensors is divided when computing the whitened
+:term:`GFP`. The whitened :term:`GFP` also helps detecting spurious late evoked
+components which can be the consequence of over- or under-regularization.
 
 Note that if data have been processed using signal space separation (SSS) [2]_,
 gradiometers and magnetometers will be displayed jointly because both are
@@ -266,8 +266,8 @@ For expert use cases or debugging the alternative estimators can also be compare
     >>> evoked = epochs.average() # doctest: +SKIP
     >>> evoked.plot_white(covs) # doctest: +SKIP
 
-This will plot the whitened evoked for the optimal estimator and display the GFPs
-for all estimators as separate lines in the related panel.
+This will plot the whitened evoked for the optimal estimator and display the
+:term:`GFPs <GFP>` for all estimators as separate lines in the related panel.
 
 References
 ----------

doc/glossary.rst

@@ -25,15 +25,9 @@ MNE-Python core terminology and general concepts
         sleep scores, sleep events (spindles, K-complex) etc.
         An :class:`Annotations` object is a container of multiple annotations.
         See :class:`Annotations` page for the API of the corresponding
-        object class and :doc:`auto_tutorials/intro/plot_object_annotations`
+        object class and :ref:`tut-annotations`
         for a tutorial on how to manipulate such objects.
 
-    channels
-        Channels refer to MEG sensors, EEG electrodes or any extra electrode
-        or sensor such as EOG, ECG or sEEG, ECoG etc. Channels have typically
-        a type, such as gradiometer, and a unit, such as Tesla/Meter that
-        is used in the code base, e.g. for plotting.
-
     BEM
         BEM is the acronym for boundary element method or boundary element
         model. Both are related to the forward model computation and more
@@ -44,9 +38,24 @@ MNE-Python core terminology and general concepts
         :func:`mne.bem.make_watershed_bem` or :func:`mne.bem.make_flash_bem`.
         See :ref:`tut-forward` for usage demo.
 
+    channels
+        Channels refer to MEG sensors, EEG electrodes or any extra electrode
+        or sensor such as EOG, ECG or sEEG, ECoG etc. Channels have typically
+        a type, such as gradiometer, and a unit, such as Tesla/Meter that
+        is used in the code base, e.g. for plotting.
+
     dipole
         See :term:`equivalent current dipole`.
 
+    epochs
+        Epochs (sometimes called "trials" in other software packages) are
+        equal-length spans of data extracted from raw continuous data. Usually,
+        epochs are extracted around stimulus events or subject responses,
+        though sometimes sequential or overlapping epochs are extracted (e.g.,
+        for analysis of resting-state activity). See :class:`Epochs` for the
+        API of the corresponding object class, and :ref:`tut-epochs-class` for
+        a narrative overview.
+
     equivalent current dipole
         An equivalent current dipole (ECD) is an approximate representation of
         post-synaptic activity in a small region of cortex. The intracellular
@@ -58,14 +67,12 @@ MNE-Python core terminology and general concepts
         are well-approximated by (i.e., "equivalent" to) fields generated by
         idealized point sources (dipoles) located on the cortical surface.
 
-    epochs
-        Epochs (sometimes called "trials" in other software packages) are
-        equal-length spans of data extracted from raw continuous data. Usually,
-        epochs are extracted around stimulus events or subject responses,
-        though sometimes sequential or overlapping epochs are extracted (e.g.,
-        for analysis of resting-state activity). See :class:`Epochs` for the
-        API of the corresponding object class, and :ref:`tut-epochs-class` for
-        a narrative overview.
+    events
+        Events correspond to specific time points in raw data; e.g.,
+        triggers, experimental condition events, etc. MNE represents events with
+        integers that are stored in numpy arrays of shape (n_events, 3). Such arrays
+        are classically obtained from a trigger channel, also referred to as
+        stim channel.
 
     evoked
         Evoked data are obtained by averaging epochs. Typically, an evoked object
@@ -74,13 +81,6 @@ MNE-Python core terminology and general concepts
         See :class:`EvokedArray` for the API of the corresponding
         object class, and :ref:`tut-evoked-class` for a narrative overview.
 
-    events
-        Events correspond to specific time points in raw data; e.g.,
-        triggers, experimental condition events, etc. MNE represents events with
-        integers that are stored in numpy arrays of shape (n_events, 3). Such arrays
-        are classically obtained from a trigger channel, also referred to as
-        stim channel.
-
     first_samp
         The attribute of raw objects called ``first_samp`` is an integer that
         refers to the number of time samples passed between the onset of the
@@ -101,6 +101,13 @@ MNE-Python core terminology and general concepts
         :term:`boundary element model <BEM>` and
         :class:`mne.bem.ConductorModel`).
 
+    GFP
+        Global Field Power (abbr. ``GFP``) is a measure of the (non-)uniformity
+        of the electromagnetic field at the sensors. It is typically calculated
+        as the standard deviation of the sensor values at each time point; thus
+        it is a one-dimensional time series capturing the spatial variability
+        of the signal across sensor locations.
+
     info
         Also called ``measurement info``, it is a collection of metadata regarding
         a Raw, Epochs or Evoked object; e.g.,
@@ -152,17 +159,8 @@ MNE-Python core terminology and general concepts
         See :class:`~io.RawArray` for the API of the corresponding
         object class, and :ref:`tut-raw-class` for a narrative overview.
 
-    source space
-        A source space (abbr. ``src``) specifies where in the brain one wants
-        to estimate the
-        source amplitudes. It corresponds to locations of a set of
-        candidate equivalent current dipoles (ECD). MNE mostly works
-        with source spaces defined on the cortical surfaces estimated
-        by FreeSurfer from a T1-weighted MRI image. See
-        :ref:`tut-forward` to read on
-        how to compute a forward operator on a source space.
-        See :class:`SourceSpaces` for the API of the corresponding
-        object class.
+    selection (abbr. sel)
+        A set of picks. E.g., all sensors included in a Region of Interest.
 
     source estimates (abbr. ``stc``)
         Source estimates, commonly referred to as STC (Source Time Courses),
@@ -177,8 +175,17 @@ MNE-Python core terminology and general concepts
         :class:`VectorSourceEstimate`, :class:`MixedSourceEstimate`,
         for the API of the corresponding object classes.
 
-    selection (abbr. sel)
-        A set of picks. E.g., all sensors included in a Region of Interest.
+    source space
+        A source space (abbr. ``src``) specifies where in the brain one wants
+        to estimate the
+        source amplitudes. It corresponds to locations of a set of
+        candidate equivalent current dipoles (ECD). MNE mostly works
+        with source spaces defined on the cortical surfaces estimated
+        by FreeSurfer from a T1-weighted MRI image. See
+        :ref:`tut-forward` to read on
+        how to compute a forward operator on a source space.
+        See :class:`SourceSpaces` for the API of the corresponding
+        object class.
 
     stim channel
         A stim channel, a.k.a. trigger channel, is a channel that encodes

examples/time_frequency/plot_time_frequency_global_field_power.py

@@ -6,19 +6,19 @@
 The objective is to show you how to explore spectrally localized
 effects. For this purpose we adapt the method described in [1]_ and use it on
 the somato dataset. The idea is to track the band-limited temporal evolution
-of spatial patterns by using the Global Field Power (GFP).
+of spatial patterns by using the :term:`Global Field Power(GFP) <GFP>`.
 
 We first bandpass filter the signals and then apply a Hilbert transform. To
 reveal oscillatory activity the evoked response is then subtracted from every
 single trial. Finally, we rectify the signals prior to averaging across trials
 by taking the magniude of the Hilbert.
-Then the GFP is computed as described in [2]_, using the sum of the squares
-but without normalization by the rank.
-Baselining is subsequently applied to make the GFPs comparable between
-frequencies.
+Then the :term:`GFP` is computed as described in [2]_, using the sum of the
+squares but without normalization by the rank.
+Baselining is subsequently applied to make the :term:`GFPs <GFP>` comparable
+between frequencies.
 The procedure is then repeated for each frequency band of interest and
-all GFPs are visualized. To estimate uncertainty, non-parametric confidence
-intervals are computed as described in [3]_ across channels.
+all :term:`GFPs <GFP>` are visualized. To estimate uncertainty, non-parametric
+confidence intervals are computed as described in [3]_ across channels.
 
 The advantage of this method over summarizing the Space x Time x Frequency
 output of a Morlet Wavelet in frequency bands is relative speed and, more

examples/visualization/plot_roi_erpimage_by_rt.py

@@ -51,7 +51,7 @@
                     event_id={"square": 2})
 
 ###############################################################################
-# Plot using GFP
+# Plot using :term:`Global Field Power <GFP>`
 
 # Parameters for plotting
 order = rts.argsort()  # sorting from fast to slow trials

tutorials/evoked/plot_object_evoked.py

@@ -66,8 +66,9 @@
 print(data[10])
 
 ###############################################################################
-# In the same vein, we can quickly extract (and, e.g., plot) the GFP as the
-# standard deviation across channels, here shown just for EEG.
+# In the same vein, we can quickly extract (and, e.g., plot) the
+# :term:`Global Field Power(GFP) <GFP>` as the standard deviation across
+# channels, here shown just for EEG.
 
 gfp = evoked.copy().pick_types(eeg=True, meg=False).data.std(axis=0)
 fig, ax = plt.subplots(1)

tutorials/source-modeling/plot_compute_covariance.py

@@ -117,20 +117,21 @@
 # be follow a multivariate Gaussian distribution, i.e.,
 # whitened baseline signals should be between -1.96 and 1.96 at a given time
 # sample.
-# Based on the same reasoning, the expected value for the global field power
-# (GFP) is 1 (calculation of the GFP should take into account the true degrees
-# of freedom, e.g. ``ddof=3`` with 2 active SSP vectors):
+# Based on the same reasoning, the expected value for the :term:`global field
+# power (GFP) <GFP>` is 1 (calculation of the GFP should take into account the
+# true degrees of freedom, e.g. ``ddof=3`` with 2 active SSP vectors):
 
 evoked = epochs.average()
 evoked.plot_white(noise_cov_reg, time_unit='s')
 
 ###############################################################################
 # This plot displays both, the whitened evoked signals for each channels and
-# the whitened GFP. The numbers in the GFP panel represent the estimated rank
-# of the data, which amounts to the effective degrees of freedom by which the
-# squared sum across sensors is divided when computing the whitened GFP.
-# The whitened GFP also helps detecting spurious late evoked components which
-# can be the consequence of over- or under-regularization.
+# the whitened :term:`GFP`. The numbers in the GFP panel represent the
+# estimated rank of the data, which amounts to the effective degrees of freedom
+# by which the squared sum across sensors is divided when computing the
+# whitened :term:`GFP`. The whitened :term:`GFP` also helps detecting spurious
+# late evoked components which can be the consequence of over- or
+# under-regularization.
 #
 # Note that if data have been processed using signal space separation
 # (SSS) [2]_,
@@ -155,7 +156,7 @@
 
 ##############################################################################
 # This will plot the whitened evoked for the optimal estimator and display the
-# GFPs for all estimators as separate lines in the related panel.
+# :term:`GFPs <GFP>` for all estimators as separate lines in the related panel.
 
 
 ##############################################################################