updated links to avoid redirections

Author: robertoostenveld

development/project/design.md

@@ -26,7 +26,7 @@ Is there any explanation on the website for more on uvar and wvar? (Seems it is
 - https://www.fieldtriptoolbox.org/tutorial/cluster_permutation_freq
 - https://www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock
 - https://www.fieldtriptoolbox.org/example/source_statistics
-- https://www.fieldtriptoolbox.org/example/apply_clusterrandanalysis_on_tfrs_of_power_that_were_computed_with_besa
+- https://www.fieldtriptoolbox.org/example/stats_besa
 - https://www.fieldtriptoolbox.org/development/statistics
 - https://www.fieldtriptoolbox.org/development/multivariate
 

development/project/documentation_source.md

@@ -79,7 +79,7 @@ none.
 
 ** example scripts:**
 
-[Compute forward simulated data and apply a dipole fit](/example/compute_forward_simulated_data_and_apply_a_dipole_fit)
+[Compute forward simulated data and apply a dipole fit](/example/source/simulateddata_dipolefit)
 
 [Fit a dipole to the tactile ERF after mechanical stimulation](/example/dipolefit_somatosensory_erf)
 

development/project/openmeeg.md

@@ -33,7 +33,7 @@ Integrate the OpenMEEG forward models into FieldTrip as better quality alternati
 ## Steps to be taken (cristiano)
 
 - Document the steps to build a bem model for a generic conductor (done)
-  - See [/example/testing_bem_created_leadfields](/example/testing_bem_created_leadfields)
+  - See [this examples](/example/source/bem_evaluation)
 - Test OpenMEEG binaries under the different OS (Linux32, Linux64, Windows)
   - For Linux: (done)
     - check the environment variables (done)

development/project/tutorial_documentation.md

@@ -43,7 +43,7 @@ Topic
 ## How to build a forward model in FieldTrip
 
 Differences emerge for the various neural recording techniques on how to build a forward model and this is due to technical and physiological reasons. Accordingly a series of models have been devised, which are recording-specific and will be explained here.
-One of the most important differences across methods takes into account the modeling of the volume conductor, which can be considered having different geometries (see also [different conductor models](/example/make_leadfields_using_different_headmodels)). In order to build a forward model though, several steps are needed, among which the construction of conductor geometry, but also the source modeling and the estimation of physical parameters like i.e. conductivity. The next paragraph explains in details the steps to be executed in FieldTrip.
+One of the most important differences across methods takes into account the modeling of the volume conductor, which can be considered having different geometries (see also [different conductor models](/example/headmodel_various)). In order to build a forward model though, several steps are needed, among which the construction of conductor geometry, but also the source modeling and the estimation of physical parameters like i.e. conductivity. The next paragraph explains in details the steps to be executed in FieldTrip.
 
 Check also 'The Forward model and Lead Field matrix' in [HERE](/tutorial/beamformer) (MEG specific way to do that)
 

example.md

@@ -13,13 +13,14 @@ See also the [tutorials](/tutorial) and [frequently asked questions](/faq).
 
 ## Reading and preprocessing data
 
-- [Getting started with reading raw EEG or MEG data](/example/preproc/getting_started_with_reading_raw_eeg_or_meg_data)
+- [Getting started with reading raw EEG or MEG data](/example/preproc/raw_meeg)
 - [Making your own trialfun for conditional trial definition](/example/preproc/trialfun)
-- [Detect the muscle activity in an EMG channel and use that as trial definition](/example/preproc/detect_the_muscle_activity_in_an_emg_channel_and_use_that_as_trial_definition)
+- [Detect the muscle activity in an EMG channel and use that as trial definition](/example/preproc/trialdef_emg)
+- [Determine the filter characteristics](/example/preproc/filter_characteristics)
 - [Independent component analysis (ICA) to remove ECG artifacts](/example/preproc/ica_ecg)
 - [Independent component analysis (ICA) to remove EOG artifacts](/example/preproc/ica_eog)
 - [Combine MEG with Eyelink eyetracker data](/example/preproc/meg_eyelink)
-- [Use denoising source separation (DSS) to remove ECG artifacts](/example/preproc/use_denoising_source_separation_dss_to_remove_ecg_artifacts)
+- [Use denoising source separation (DSS) to remove ECG artifacts](/example/preproc/dss_ecg)
 - [Fixing a missing sensor](/example/preproc/fixing_a_missing_sensor)
 - [Rereference EEG and iEEG data](/example/preproc/rereference)
 
@@ -34,55 +35,54 @@ See also the [tutorials](/tutorial) and [frequently asked questions](/faq).
 ## Spectral analysis
 
 - [Analysis of high-gamma band signals in human ECoG](/example/spectral/ecog_ny)
-- [Analyze Steady-State Visual Evoked Potentials (SSVEPs)](/example/spectral/ssvep)
+- [Analyze steady-state visual evoked potentials (SSVEPs)](/example/spectral/ssvep)
 - [Cross-frequency analysis](/example/spectral/crossfreq)
-- [Determine the filter characteristics](/example/spectral/filter_characteristics)
-- [Effect of Signal-to-Noise Ratio on Coherence](/example/spectral/coherence_snr)
+- [Effect of signal-to-noise ratio on coherence](/example/spectral/coherence_snr)
 - [Effects of tapering for power estimates](/example/spectral/effects_of_tapering)
 - [Fourier analysis of oscillatory power and coherence](/example/spectral/fourier)
 - [Simulate an oscillatory signal with phase resetting](/example/spectral/phase_reset)
-- [Irregular Resampling Auto-Spectral Analysis (IRASA)](/example/spectral/irasa)
+- [Irregular resampling auto-spectral analysis (IRASA)](/example/spectral/irasa)
 - [Fitting oscillations and one-over-F (FOOOF)](/example/spectral/fooof)
-- [Conditional Granger causality in the frequency domain](/example/spectral/connectivity_conditional_granger)
+- [Conditional Granger causality in the frequency domain](/example/spectral/granger_conditional)
 - [Interpolate the time axis of single-trial TFRs](/example/spectral/tfr_interpolatetime)
 
 ## Source reconstruction
 
 - [Align EEG electrode positions to BEM headmodel](/example/source/electrodes2bem)
 - [Check the quality of the anatomical coregistration](/example/source/coregistration_quality_control)
-- [Combined EEG and MEG source reconstruction](/example/source/combined_eeg_and_meg_source_reconstruction)
-- [Common filters in beamforming](/example/source/common_filters_in_beamforming)
+- [Combined EEG and MEG source reconstruction](/example/source/sourcerecon_meeg)
+- [Common filters in beamforming](/example/source/beamformer_commonfilter)
 - [Compute EEG leadfields using a concentric spheres headmodel](/example/source/concentricspheres)
 - [Compute EEG leadfields using a BEM headmodel](/example/source/bem)
 - [Compute EEG leadfields using a FEM headmodel](/example/source/fem)
 - [Create a template source model aligned to MNI space](/example/source/sourcemodel_mnitemplate)
-- [Compute forward simulated data and apply a beamformer scan](/example/source/compute_forward_simulated_data_and_apply_a_beamformer_scan)
-- [Compute forward simulated data and apply a dipole fit](/example/source/compute_forward_simulated_data_and_apply_a_dipole_fit)
-- [Compute forward simulated data using ft_dipolesimulation](/example/source/compute_forward_simulated_data)
+- [Compute forward simulated data and apply a beamformer scan](/example/source/simulateddata_beamformer)
+- [Compute forward simulated data and apply a dipole fit](/example/source/simulateddata_dipolefit)
+- [Compute forward simulated data using ft_dipolesimulation](/example/source/simulateddata)
 - [Compute forward simulated data with the low-level ft_compute_leadfield](/example/source/compute_leadfield)
 - [Create MNI-aligned grids in individual head-space](/example/source/sourcemodel_aligned2mni)
 - [Fit a dipole to the tactile ERF after mechanical stimulation](/example/source/dipolefit_somatosensory_erf)
 - [How to create a head model if you do not have an individual MRI](/example/source/fittemplate)
 - [Localizing the sources underlying the difference in event-related fields](/example/source/difference_erf)
-- [Make MEG leadfields using different headmodels](/example/source/make_leadfields_using_different_headmodels)
-- [Read neuromag .fif mri and create a MNI-aligned single-shell head model](/example/source/neuromag_aligned2mni)
+- [Make MEG leadfields using different headmodels](/example/source/headmodel_various)
+- [Read Neuromag .fif mri and create a MNI-aligned single-shell head model](/example/source/neuromag_aligned2mni)
 - [Symmetric dipole pairs for beamforming](/example/source/symmetry)
-- [Testing BEM created lead fields](/example/source/testing_bem_created_leadfields)
-- [Use your own forward leadfield model in an inverse beamformer computation](/example/source/use_your_own_forward_leadfield_model_in_an_inverse_beamformer_computation)
+- [Testing BEM created lead fields](/example/source/bem_evaluation)
+- [Use your own forward leadfield model in an inverse beamformer computation](/example/source/beamformer_ownforward)
 
 ## Statistical analysis
 
-- [Apply non-parametric statistics with clustering on TFRs of power that were computed with BESA](/example/stats/apply_clusterrandanalysis_on_tfrs_of_power_that_were_computed_with_besa)
+- [Apply non-parametric statistics with clustering on TFRs of power that were computed with BESA](/example/stats/stats_besa)
 - [Computing and reporting the effect size](/example/stats/effectsize)
 - [Defining electrodes as neighbours for cluster-level statistics](/example/stats/neighbours)
 - [Source statistics](/example/stats/source_statistics)
 - [Stratify the distribution of two variables](/example/stats/stratify)
-- [Use simulated ERPs to explore cluster statistics](/example/stats/use_simulated_erps_to_explore_cluster_statistics)
-- [Using GLM to analyze NIRS timeseries data](/example/stats/nirs_glm)
-- [Using General Linear Modeling over trials](/example/stats/glm_trials)
-- [Using General Linear Modeling on time series data](/example/stats/glm_timeseries)
+- [Use simulated ERPs to explore cluster statistics](/example/stats/simulateddata_clusterstats)
+- [Using general linear modeling to analyze NIRS timeseries data](/example/stats/nirs_glm)
+- [Using general linear modeling over trials](/example/stats/glm_trials)
+- [Using general linear modeling on time series data](/example/stats/glm_timeseries)
 - [Using simulations to estimate the sample size for cluster-based permutation test](/example/stats/samplesize)
-- [Using threshold-free cluster enhancement for cluster statistics](/example/stats/threshold_free_cluster_enhancement)
+- [Using threshold-free cluster enhancement for cluster statistics](/example/stats/tfce)
 
 ## Real-time analysis
 
@@ -91,7 +91,7 @@ See also the [tutorials](/tutorial) and [frequently asked questions](/faq).
 - [Example real-time power estimate](/example/realtime/ft_realtime_powerestimate)
 - [Example real-time selective average](/example/realtime/ft_realtime_selectiveaverage)
 - [Example real-time signal viewer](/example/realtime/ft_realtime_signalviewer)
-- [Measuring the timing delay and jitter for a real-time application](/example/realtime/measuring_the_timing_delay_and_jitter_for_a_real-time_application)
+- [Measuring the timing delay and jitter for a real-time application](/example/realtime/realtime_evaluation)
 - [Realtime neurofeedback application based on Hilbert phase estimation](/example/realtime/ft_realtime_hilbert)
 
 ## Plotting and visualization
@@ -117,9 +117,9 @@ See also the [tutorials](/tutorial) and [frequently asked questions](/faq).
 - [Making your analysis pipeline reproducible using reproducescript](/example/other/reproducescript)
 - [Using reproducescript for a group analysis](/example/other/reproducescript_group)
 - [Using reproducescript on a full study](/example/other/reproducescript_andersen)
-- [Correlation analysis of fMRI data](/example/other/correlation_analysis_in_fmri_data)
+- [Correlation analysis of fMRI data](/example/other/fmri_correlationanalysis)
 - [Example analysis pipeline for BioSemi bdf data](/example/other/biosemi)
 - [Find the orientation of planar gradiometer channels](/example/other/planar_orientation)
 - [How to import data from MNE-Python and FreeSurfer](/example/other/import_mne)
 - [How to use ft_checkconfig](/example/other/checkconfig)
-- [Perform modified Multiscale Entropy (mMSE) analysis on EEG/MEG/LFP data](/example/other/entropy_analysis)
+- [Perform modified multiscale entropy (mMSE) analysis on EEG/MEG/LFP data](/example/other/entropy_analysis)

example/other/entropy_analysis.md

@@ -1,12 +1,12 @@
 ---
-title: Perform modified Multiscale Entropy (mMSE) analysis on EEG/MEG/LFP data
+title: Perform modified multiscale entropy (mMSE) analysis on EEG/MEG/LFP data
 category: example
 tags: [entropy]
 redirect_from:
     - /example/entropy_analysis/
 ---
 
-# Perform modified Multiscale Entropy (mMSE) analysis on EEG/MEG/LFP data
+# Perform modified multiscale entropy (mMSE) analysis on EEG/MEG/LFP data
 
 Recently, we have developed a novel algorithm based on multiscale entropy (**[Costa et al. 2002](https://doi.org/10.1103/PhysRevLett.89.068102.m)**) called modified multiscale entropy (mMSE) that directly quantifies the temporal irregularity of time-domain EEG/MEG/LFP signals at longer and shorter timescales. In general, patterns of fluctuations in brain activity that tend to repeat over time are assigned lower entropy, whereas more irregular, non-repeating patterns yield higher entropy. To allow the investigation of dynamic changes in signal irregularity, we developed mMSE as a time-resolved variant, while also permitting assessment of entropy over atypically longer time scales by calculating across discontinuous, concatenated segments (**[Grandy et al.](https://doi.org/10.1038/srep23073.m)**) (see the figure below).
 

example/preproc/dss_ecg.md

@@ -13,10 +13,10 @@ redirect_from:
 
 This script demonstrates how you can use denoising source separation (DSS) for cleaning the ECG/BCG artifacts from your MEEG data. It consists of four steps:
 
-1.  detection of QRS-complexes using the ECG channel, which has been recorded along with the data
-2.  use the identified peaks as prior information to inform the DSS algorithm to unmix the MEEG channel data
-2.  selection of a number of components to remove from MEEG data
-3.  removal of the identified components
+1. detection of QRS-complexes using the ECG channel, which has been recorded along with the data
+2. use the identified peaks as prior information to inform the DSS algorithm to unmix the MEEG channel data
+3. selection of a number of components to remove from MEEG data
+4. removal of the identified components
 
 DSS is a blind source separation technique that is akin to ICA, with the added functionality of that it can use prior information to unmix the signals into sources that have certain characteristics. While in ICA the defining characteristic of the sources is statistical independence, in DSS one can for instance steer the unmixing towards the identification of sources that are timelocked to certain events. This is the exact feature that we are going to exploit in this example, because it shows how to remove the ECG artifact, using information about the timing of the QRS-complexes. What is therefore needed, is a sufficiently clean ECG-like signal to begin with, to allow for the identification of those peaks. This will be done, using the **[ft_artifact_zvalue](/reference/ft_artifact_zvalue)** function. Next, the output of **[ft_artifact_zvalue](/reference/ft_artifact_zvalue)** will be used to call **[ft_componentanalysis](/reference/ft_componentanalysis)** with 'dss' as method.
 

example/source/neuromag_aligned2mni.md

@@ -1,12 +1,12 @@
 ---
-title: Read neuromag .fif mri and create a MNI-aligned single-shell head model
+title: Read Neuromag .fif mri and create a MNI-aligned single-shell head model
 category: example
 tags: [meg, mri, headmodel]
 redirect_from:
     - /example/neuromag_aligned2mni/
 ---
 
-# Read neuromag .fif mri and create a MNI-aligned single-shell head model
+# Read Neuromag .fif mri and create a MNI-aligned single-shell head model
 
 {% include markup/red %}
 The below example code is hopelessly outdated (thus deprecated) and will probably not work anymore. This page is kept in place just for reference. If you ended up on this page because you are curious to learn about the creation of dipole grids from .fif MRI, please look at [this](/example/sourcemodel_aligned2mni) example script.
@@ -56,7 +56,7 @@ This example script is derived from the example script [Create MNI-aligned grids
     [segmentedmri]     = ft_volumesegment(cfg, mri_real)
 
     % check how it looks; does the segmented mri fit into the mri? Probably not
-    % because of neuromag coordinates (x and y are swapped) and a bug in volume
+    % because of Neuromag coordinates (x and y are swapped) and a bug in volume
     % segment.
     cfg                = [];
     test               = segmentedmri;

example/source/sphere_fitting.md

@@ -24,7 +24,7 @@ This example script demonstrates how to fit a sphere to the MEG Polhemus recorde
     save headshape headshape
     save grad grad
 
-    % realign to neuromag coordinate system
+    % realign to Neuromag coordinate system
     lpa    = [  7 104  26];
     nas    = [ 92 210  32];
     rpa    = [176 104  26];

example/spectral/irasa.md

@@ -1,12 +1,12 @@
 ---
-title: Irregular Resampling Auto-Spectral Analysis (IRASA)
+title: Irregular resampling auto-spectral analysis (IRASA)
 category: example
 tags: [irasa]
 redirect_from:
     - /example/irasa/
 ---
 
-# Irregular Resampling Auto-Spectral Analysis (IRASA)
+# Irregular resampling auto-spectral analysis (IRASA)
 
 IRASA allows distinguishing rhythmic activity from concurrent power-spectral 1/f modulations. The technique virtually compresses and expands the time-domain data with a set of non-integer resampling factors prior to Fourier-based spectral decomposition. As a result, rhythmic components in the power-spectrum are redistributed while the arrhythmic 1/f distribution is left intact. Taking the median of the resulting auto-spectral distributions extracts the power-spectral 1/f component, and the subsequent removal of the 1/f component from the original power-spectrum offers a power-spectral estimate of rhythmic content in the recorded signal.
 Below we provide two examples, a simulated dataset and a real human ECoG dataset [(Stolk et al. 2019)](https://elifesciences.org/articles/48065), including how to extract spectral features based on [the IRASA technique (Wen & Liu, 2016)](https://link.springer.com/article/10.1007/s10548-015-0448-0).