ensure consistent page titles, see #810

example.md

@@ -118,4 +118,4 @@ See also the [tutorials](/tutorial) and [frequently asked questions](/faq).
 - [Find the orientation of planar gradiometer channels](/example/planar_orientation)
 - [How to import data from MNE-Python and FreeSurfer](/example/import_mne)
 - [How to use ft_checkconfig](/example/checkconfig)
-- [Performing modified Multiscale Entropy (mMSE) analysis](/example/entropy_analysis)
+- [Perform modified Multiscale Entropy (mMSE) analysis on EEG/MEG/LFP data](/example/entropy_analysis)

example/compute_leadfield.md

@@ -3,7 +3,7 @@ title: Compute forward simulated data with the low-level ft_compute_leadfield
 tags: [example, eeg, source, headmodel, dipole, simulation]
 ---
 
-# Example use of the ft_compute_leadfield function
+# Compute forward simulated data with the low-level ft_compute_leadfield
 
 Rather than using the high-level **[ft_dipolesimulation](/reference/ft_dipolesimulation)**, this uses the low-level **[ft_compute_leadfield](/reference/forward/ft_compute_leadfield)**. Note that this makes you responsible of more bookkeeping.
 

example/entropy_analysis.md

@@ -1,5 +1,5 @@
 ---
-title: Performing modified Multiscale Entropy (mMSE) analysis on EEG/MEG/LFP data
+title: Perform modified Multiscale Entropy (mMSE) analysis on EEG/MEG/LFP data
 tags: [example, entropy]
 ---
 

example/fooof.md

@@ -1,5 +1,5 @@
 ---
-title: Fitting oscillations and one-over-F (FOOOF).
+title: Fitting oscillations and one-over-F (FOOOF)
 tags: [example, fooof]
 ---
 

faq/how_can_i_determine_the_anatomical_label_of_a_source.md

@@ -3,7 +3,9 @@ title: How can I determine the anatomical label of a source or electrode?
 tags: [faq, source]
 ---
 
-# How can I determine the anatomical label of a source?
+# How can I determine the anatomical label of a source or electrode?
+
+## Determine the label of a source that you localized
 
 FieldTrip supports the use of an anatomical atlas to look up the anatomical label of a source that you have localized. Vice versa you can also first look up the location of an anatomical region and subsequently use that in source analysis, e.g., as region of interest for beamforming or as starting point for dipole fitting.
 
@@ -21,10 +23,10 @@ Atlases can be used in several FieldTrip functions. For instance in the **[ft_so
 
 The most important function for using an atlas is **[ft_volumelookup](/reference/ft_volumelookup)**. It can be used in two approaches.
 
-1.  Given the anatomical or functional label, it looks up the locations and creates a mask (as a binary volume) based on the label, or creates a sphere or box around a point of interest.
-2.  Given a binary volume that indicates a region of interest, it looks up the corresponding anatomical or functional labels from a given atlas.
+1. Given the anatomical or functional label, it looks up the locations and creates a mask (as a binary volume) based on the label, or creates a sphere or box around a point of interest.
+2. Given a binary volume that indicates a region of interest, it looks up the corresponding anatomical or functional labels from a given atlas.
 
-# How can I determine the anatomical or functional label of an (intracranial) electrode?
+## Determine the label of an (intracranial) electrode
 
 In the context of intracranial EEG recordings, FieldTrip supports looking up the anatomical or functional labels corresponding to electrodes in a number of atlases, including the AFNI Talairach Tournoux atlas, the AAL atlas, the BrainWeb data set, the JuBrain cytoarchitectonic atlas, the VTPM atlas, the Brainnetome atlas, and the Yeo atlases, in addition to the subject-tailored Desikan-Killiany and Destrieux atlases produced by FreeSurfer (for details of each atlas, see the [template atlas documentation](/template/atlas)). Given that no two electrodes end up in the exact same location across subjects due to inter-individual variability in electrode coverage and brain anatomy, atlases are particularly useful for the systematic combination of neural activity from different subjects in a so-called region of interest (ROI) analysis. With exception of the above FreeSurfer-based atlases, the atlases are in MNI coordinate space and require the electrodes to be spatially normalized (Steps 26 through 27 of the [human iEEG tutorial](/tutorial/human_ecog)). First, import an atlas of interest, e.g., the AAL atlas, into the MATLAB workspace.
 

faq/how_can_i_test_whether_a_behavioral_measure_is_phasic.md

@@ -3,15 +3,14 @@ title: How can I test whether a behavioral measure is phasic?
 tags: [faq, statistics, freq]
 ---
 
-# How can I test whether a behavioral measure (or some other dependent variable is phasic?
+# How can I test whether a behavioral measure is phasic?
 
 Some experimental hypotheses address the question: *Is my measure-of-interest systematically modulated by the phase of an underlying process?*
 
 For instance, you may wish to quantify the effect of the phase of a band-limited neuronal oscillation on behavioral accuracy or reaction time. Alternatively, you may wish to quantify the effect of pre-stimulus phase on the amplitude of a stimulus-evoked transient in the neuronal signal. One way to evaluate this is to fit a sine/cosine function to the dependent variable, which (according to the hypothesis) modulates as a function of phase. Subsequently, you test the probability of observing the outcome of this fit (typically expressed as the amplitude, or modulation depth) under some null hypothesis.
 
 Here, we demonstrate how this can be achieved in a generic way, using a binning approach. Conceptually, the dependent data consists of a set of observations (typically trials), consisting of categorical variables (e.g., hit/miss) or of continuous variables (e.g., reaction time, signal amplitude). For each of these observations, there is a corresponding 'phase' of the underlying independent variable of interest. Using least-squares regression, it is possible to estimate the amplitude (and phase) of the best fitting cosine-wave to the data. A binning approach can be used to improve the sensitivity, and to better appreciate the underlying structure in the data (particularly for categorical data). The code snippet below demonstrates this approach, and provides a statfun that efficiently computes the cosinefit for multiple signals at once.
 
-
     function [s, s_unbinned, x_binned, y_binned] = demo_phasicfit
 
     nchan = 200;

tutorial/minimumnormestimate.md

@@ -34,7 +34,7 @@ The forward model requires three geometric object
 The sourcemodel and headmodel are ideally generated from a subject-specific MRI image. The description of the sensor-array typically is represented in the data (MEG), or needs to be constructed, for example with a Polhemus device (EEG). The construction of the head- and sourcemodels that are needed for the remainder of this tutorial is described in the following tutorial
 
 - [Creating a volume conduction model of the head for source reconstruction of MEG data](/tutorial/headmodel_meg)
-- [Creating a sourcemodel for source reconstruction of MEG or EEG data](/tutorial/sourcemodel)
+- [Creating a source model for source reconstruction of MEG or EEG data](/tutorial/sourcemodel)
 
 Once we have the headmodel and sourcemodel, we perform the following step
 

tutorial/sourcemodel.md

@@ -1,5 +1,5 @@
 ---
-title: Creating a sourcemodel for source reconstruction of MEG or EEG data
+title: Creating a source model for source reconstruction of MEG or EEG data
 tags: [tutorial, source, meg, sourcemodel, mri, plotting, meg-language]
 ---