Merge pull request #825 from schoffelen/fname_short

shortening filenames of example pages

example.md

@@ -13,13 +13,13 @@ See also the [tutorials](/tutorial) and [frequently asked questions](/faq).
 
 ### Reading and preprocessing data
 
-- [Getting started with reading raw EEG or MEG data](/example/getting_started_with_reading_raw_eeg_or_meg_data)
+- [Getting started with reading raw EEG or MEG data](/example/raw_meeg)
 - [Making your own trialfun for conditional trial definition](/example/trialfun)
-- [Detect the muscle activity in an EMG channel and use that as trial definition](/example/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/trialdef_emg)
 - [Independent component analysis (ICA) to remove ECG artifacts](/example/ica_ecg)
 - [Independent component analysis (ICA) to remove EOG artifacts](/example/ica_eog)
 - [Combine MEG with Eyelink eyetracker data](/example/meg_eyelink)
-- [Use denoising source separation (DSS) to remove ECG artifacts](/example/use_denoising_source_separation_dss_to_remove_ecg_artifacts)
+- [Use denoising source separation (DSS) to remove ECG artifacts](/example/dss_ecg)
 - [Fixing a missing sensor](/example/fixing_a_missing_sensor)
 - [Re-reference EEG and iEEG data](/example/rereference)
 
@@ -42,43 +42,43 @@ See also the [tutorials](/tutorial) and [frequently asked questions](/faq).
 - [Simulate an oscillatory signal with phase resetting](/example/phase_reset)
 - [Irregular Resampling Auto-Spectral Analysis (IRASA)](/example/irasa)
 - [Fitting oscillations and one-over-F (FOOOF)](/example/fooof)
-- [Conditional Granger causality in the frequency domain](/example/connectivity_conditional_granger)
+- [Conditional Granger causality in the frequency domain](/example/granger_conditional)
 
 ### Source reconstruction
 
 - [Align EEG electrode positions to BEM headmodel](/example/electrodes2bem)
 - [Check the quality of the anatomical coregistration](/example/coregistration_quality_control)
-- [Combined EEG and MEG source reconstruction](/example/combined_eeg_and_meg_source_reconstruction)
-- [Common filters in beamforming](/example/common_filters_in_beamforming)
+- [Combined EEG and MEG source reconstruction](/example/sourcerecon_meeg)
+- [Common filters in beamforming](/example/beamformer_commonfilter)
 - [Compute EEG leadfields using a FEM headmodel](/example/fem)
-- [Compute forward simulated data and apply a beamformer scan](/example/compute_forward_simulated_data_and_apply_a_beamformer_scan)
-- [Compute forward simulated data and apply a dipole fit](/example/compute_forward_simulated_data_and_apply_a_dipole_fit)
-- [Compute forward simulated data using ft_dipolesimulation](/example/compute_forward_simulated_data)
+- [Compute forward simulated data and apply a beamformer scan](/example/simulateddata_beamformer)
+- [Compute forward simulated data and apply a dipole fit](/example/simulateddata_dipolefit)
+- [Compute forward simulated data using ft_dipolesimulation](/example/simulateddata)
 - [Compute forward simulated data with the low-level ft_compute_leadfield](/example/compute_leadfield)
 - [Create MNI-aligned grids in individual head-space](/example/sourcemodel_aligned2mni)
-- [Determine the filter characteristics](/example/determine_the_filter_characteristics)
+- [Determine the filter characteristics](/example/filter_characteristics)
 - [Fit a dipole to the tactile ERF after mechanical stimulation](/example/dipolefit_somatosensory_erf)
 - [How to create a head model if you do not have an individual MRI](/example/fittemplate)
 - [Localizing the sources underlying the difference in event-related fields](/example/difference_erf)
-- [Make MEG leadfields using different headmodels](/example/make_leadfields_using_different_headmodels)
+- [Make MEG leadfields using different headmodels](/example/headmodel_various)
 - [Read neuromag .fif mri and create a MNI-aligned single-shell head model](/example/neuromag_aligned2mni)
 - [Symmetric dipole pairs for beamforming](/example/symmetry)
-- [Testing BEM created lead fields](/example/testing_bem_created_leadfields)
-- [Use your own forward leadfield model in an inverse beamformer computation](/example/use_your_own_forward_leadfield_model_in_an_inverse_beamformer_computation)
+- [Testing BEM created lead fields](/example/bem_evaluation)
+- [Use your own forward leadfield model in an inverse beamformer computation](/example/beamformer_ownforward)
 
 ### Statistical analysis
 
-- [Apply non-parametric statistics with clustering on TFRs of power that were computed with BESA](/example/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_besa)
 - [Computing and reporting the effect size](/example/effectsize)
 - [Defining electrodes as neighbours for cluster-level statistics](/example/neighbours)
 - [Source statistics](/example/source_statistics)
 - [Stratify the distribution of two variables](/example/stratify)
-- [Use simulated ERPs to explore cluster statistics](/example/use_simulated_erps_to_explore_cluster_statistics)
+- [Use simulated ERPs to explore cluster statistics](/example/simulateddata_clusterstats)
 - [Using GLM to analyze NIRS timeseries data](/example/nirs_glm)
 - [Using General Linear Modeling over trials](/example/glm_trials)
 - [Using General Linear Modeling on time series data](/example/glm_timeseries)
 - [Using simulations to estimate the sample size for cluster-based permutation test](/example/samplesize)
-- [Using threshold-free cluster enhancement for cluster statistics](/example/threshold_free_cluster_enhancement)
+- [Using threshold-free cluster enhancement for cluster statistics](/example/tfce)
 
 ### Real-time analysis
 
@@ -87,7 +87,7 @@ See also the [tutorials](/tutorial) and [frequently asked questions](/faq).
 - [Example real-time power estimate](/example/ft_realtime_powerestimate)
 - [Example real-time selective average](/example/ft_realtime_selectiveaverage)
 - [Example real-time signal viewer](/example/ft_realtime_signalviewer)
-- [Measuring the timing delay and jitter for a real-time application](/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application)
+- [Measuring the timing delay and jitter for a real-time application](/example/realtime_evaluation)
 - [Realtime neurofeedback application based on Hilbert phase estimation](/example/ft_realtime_hilbert)
 
 ## Plotting and visualization
@@ -113,7 +113,7 @@ See also the [tutorials](/tutorial) and [frequently asked questions](/faq).
 - [Making your analysis pipeline reproducible using reproducescript](/example/reproducescript)
 - [Using reproducescript for a group analysis](/example/reproducescript_group)
 - [Using reproducescript on a full study](/example/reproducescript_andersen)
-- [Correlation analysis of fMRI data](/example/correlation_analysis_in_fmri_data)
+- [Correlation analysis of fMRI data](/example/fmri_correlationanalysis)
 - [Example analysis pipeline for Biosemi bdf data](/example/biosemi)
 - [Find the orientation of planar gradiometer channels](/example/planar_orientation)
 - [How to import data from MNE-Python and FreeSurfer](/example/import_mne)

example/common_filters_in_beamforming.md → example/beamformer_commonfilter.md

@@ -2,6 +2,8 @@
 title: Common filters in beamforming
 category: example
 tags: [meg, freq, source, fixme]
+redirect_from:
+    - /example/common_filters_in_beamforming/
 ---
 
 # Common filters in beamforming

example/use_your_own_forward_leadfield_model_in_an_inverse_beamformer_computation.md → example/beamformer_ownforward.md

@@ -2,6 +2,8 @@
 title: Use your own forward leadfield model in an inverse beamformer computation
 category: example
 tags: [eeg, source]
+redirect_from:
+    - /example/use_your_own_forward_leadfield_model_in_an_inverse_beamformer_computation/
 ---
 
 # Use your own forward leadfield model in an inverse beamformer computation

example/testing_bem_created_leadfields.md → example/bem_evaluation.md

@@ -2,6 +2,8 @@
 title: Testing BEM created EEG lead fields
 category: example
 tags: [eeg, mri, headmodel, source, simulation]
+redirect_from:
+    - /example/testing_bem_created_leadfields/
 ---
 
 # Testing BEM created EEG lead fields
@@ -44,7 +46,7 @@ For the simplest case, the BEM and the theoretical solutions for EEG lead field
     cfg.elec = elec;
     sourcemodel = ft_prepare_sourcemodel(cfg);
 
-{% include image src="/assets/img/example/testing_bem_created_leadfields/bemtesting1.png" width="400" %}
+{% include image src="/assets/img/example/bem_evaluation/bemtesting1.png" width="400" %}
 _Figure; BEM model consisting of single sphere, including electrodes_
 
 ## Building the geometrical head model with BEM
@@ -94,6 +96,6 @@ The dipole positions are defined in the variable `sourcemodel`, the headmodels a
 
 ## Result
 
-{% include image src="/assets/img/example/testing_bem_created_leadfields/bemtesting2.png" %}
+{% include image src="/assets/img/example/bem_evaluation/bemtesting2.png" %}
 
 The pinky arrow describes the correlation curves of meshes with increasing number of triangles. The last mesh (2000 vertices) has a flat correlation curve at value y=1.

example/use_denoising_source_separation_dss_to_remove_ecg_artifacts.md → example/dss_ecg.md

@@ -2,6 +2,8 @@
 title: Use denoising source separation (DSS) to remove ECG artifacts
 category: example
 tags: [artifact, preprocessing, ica, meg-removal]
+redirect_from:
+    - /example/use_denoising_source_separation_dss_to_remove_ecg_artifacts/
 ---
 
 # Use denoising source separation (DSS) to remove ECG artifacts

example/determine_the_filter_characteristics.md → example/filter_characteristics.md

@@ -2,6 +2,8 @@
 title: Determine the filter characteristics
 category: example
 tags: [preprocessing, filter]
+redirect_from:
+    - /example/determine_the_filter_characteristics/
 ---
 
 # Determine the filter characteristics
@@ -27,7 +29,7 @@ The following script demonstrates how you can determine the filter characteristi
 
     print -dpng fig1.png
 
-{% include image src="/assets/img/example/determine_the_filter_characteristics/fig1.png" width="600" %}
+{% include image src="/assets/img/example/filter_characteristics/fig1.png" width="600" %}
 
       str = 'compare different filter orders (Butterworth)';
     clear f
@@ -44,7 +46,7 @@ The following script demonstrates how you can determine the filter characteristi
 
     print -dpng fig2.png
 
-{% include image src="/assets/img/example/determine_the_filter_characteristics/fig2.png" width="600" %}
+{% include image src="/assets/img/example/filter_characteristics/fig2.png" width="600" %}
 
     str = 'compare Butterworth and FIR';
     clear f
@@ -61,7 +63,7 @@ The following script demonstrates how you can determine the filter characteristi
 
     print -dpng fig3.png
 
-{% include image src="/assets/img/example/determine_the_filter_characteristics/fig3.png" width="600" %}
+{% include image src="/assets/img/example/filter_characteristics/fig3.png" width="600" %}
 
     str = 'compare filter direction';
     clear f
@@ -78,6 +80,6 @@ The following script demonstrates how you can determine the filter characteristi
 
     print -dpng fig4.png
 
-{% include image src="/assets/img/example/determine_the_filter_characteristics/fig4.png" width="600" %}
+{% include image src="/assets/img/example/filter_characteristics/fig4.png" width="600" %}
 
 Note that the two-pass filter characteristic drops off twice as fast as the forward and reverse filter, even though the specified filter order is the same.

example/correlation_analysis_in_fmri_data.md → example/fmri_correlationanalysis.md

@@ -2,6 +2,8 @@
 title: Correlation analysis of fMRI data
 category: example
 tags: [fmri, raw, freq, coherence]
+redirect_from:
+    - /example/correlation_analysis_in_fmri_data/
 ---
 
 # Correlation analysis of fMRI data

example/connectivity_conditional_granger.md → example/granger_conditional.md

@@ -2,6 +2,8 @@
 title: Conditional Granger causality in the frequency domain
 category: example
 tags: [freq, connectivity, granger]
+redirect_from:
+    - /example/connectivity_conditional_granger/
 ---
 
 # Conditional Granger causality in the frequency domain

example/make_leadfields_using_different_headmodels.md → example/headmodel_various.md

@@ -2,6 +2,8 @@
 title: Make MEG leadfields using different headmodels
 category: example
 tags: [meg, headmodel, source]
+redirect_from:
+    - /example/make_leadfields_using_different_headmodels/
 ---
 
 # Make MEG leadfields using different headmodels

example/headmovement_meg.md

@@ -3,7 +3,8 @@ title: How to incorporate head movements in MEG analysis
 category: example
 tags: [artifact, meg, glm, regression, confound]
 redirect_from:
-   - /example/how_to_incorporate_head_movements_in_meg_analysis/
+     - /example/how_to_incorporate_head_movements_in_meg_analysis/
+     - /example/regressing_out_headposition_confounds_in_a_ctf275_dataset/
 ---
 
 # How to incorporate head movements in MEG analysis

example/getting_started_with_reading_raw_eeg_or_meg_data.md → example/raw_meeg.md

@@ -2,6 +2,8 @@
 title: Getting started with reading raw EEG or MEG data
 category: example
 tags: [eeg, meg, raw, preprocessing, trialdef]
+redirect_from:
+    - /example/getting_started_with_reading_raw_eeg_or_meg_data/
 ---
 
 # Getting started with reading raw EEG or MEG data

example/measuring_the_timing_delay_and_jitter_for_a_real-time_application.md → example/realtime_evaluation.md

@@ -2,6 +2,8 @@
 title: Measuring the timing delay and jitter for a real-time application
 category: example
 tags: [realtime]
+redirect_from:
+    - /example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/
 ---
 
 # Measuring the timing delay and jitter for a real-time application
@@ -183,7 +185,7 @@ We can just plot the trigger channel:
 
 This then looks a bit like this figure.
 
-{% include image src="/assets/img/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/sent_and_received_triggers_head_localization_off.jpg" %}
+{% include image src="/assets/img/example/realtime_evaluation/sent_and_received_triggers_head_localization_off.jpg" %}
 
 i.e. a train of couplets comprising a 4 followed by a 16. We can now extract the incoming and detected events;
 
@@ -211,11 +213,11 @@ i.e. a train of couplets comprising a 4 followed by a 16. We can now extract the
 
 The data I obtained (at a sampling rate of 1200) after sending about 3000 triggers looks like this:
 
-{% include image src="/assets/img/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/picture_3.png" %}
+{% include image src="/assets/img/example/realtime_evaluation/picture_3.png" %}
 
 This is rather consistent with a uniform distribution between 100-250ms
 
-{% include image src="/assets/img/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/picture_2.png" %}
+{% include image src="/assets/img/example/realtime_evaluation/picture_2.png" %}
 
 ## Timing of a closed system using the FT buffer to do the online streaming
 
@@ -451,17 +453,17 @@ Below follow the results of the testing in the DCCN for continuous head localiza
 
 NOTE: this is a configuration previously considered as buggy, which is now working
 
-{% include image src="/assets/img/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/delay_hist._1200hz.jpg" %}
+{% include image src="/assets/img/example/realtime_evaluation/delay_hist._1200hz.jpg" %}
 
 We now also plot the sample number of the echo against the sample number of the trigger that preceded i
 
-{% include image src="/assets/img/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/trigger_smp_vs_echo_smp.jpg" %}
+{% include image src="/assets/img/example/realtime_evaluation/trigger_smp_vs_echo_smp.jpg" %}
 
 This shows no samples missing and no accumulative delays
 
 ### CHL off, Fs=1200, Nchans=311
 
-{% include image src="/assets/img/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/delay_hist._1200hz_hl_off.jpg" %}
+{% include image src="/assets/img/example/realtime_evaluation/delay_hist._1200hz_hl_off.jpg" %}
 
 We note that the delays are smaller when the continuous HL is off. This is probably to do with an additional data granularity related to the time required to fit a dipole while doing continuous localization- more details on this will follow soon...
 
@@ -471,7 +473,7 @@ Here we increase the sampling rate to Fs=4000Hz
 
 ### CHL on, Fs=4KHz, Nchans=341
 
-{% include image src="/assets/img/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/delay_hist_hl_on_fs_4khz.jpg" %}
+{% include image src="/assets/img/example/realtime_evaluation/delay_hist_hl_on_fs_4khz.jpg" %}
 
 Comparing Figure 3 to Figure 1a, we see that the delays have decreased.
 
@@ -481,11 +483,11 @@ We now use the 2nd option for detecting events: using ft_read_event. Note that t
 
 The events were detected with `ft_read_event`.
 
-{% include image src="/assets/img/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/trigger_smp_vs_echo_smp_4khz_read_ev.jpg" %}
+{% include image src="/assets/img/example/realtime_evaluation/trigger_smp_vs_echo_smp_4khz_read_ev.jpg" %}
 
 This shows no events missing and no accumulative delays.The delay distribution is in Figure 5.
 
-{% include image src="/assets/img/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/delay_hist_hl_on_fs_4khz_detection_read_ev.jpg" %}
+{% include image src="/assets/img/example/realtime_evaluation/delay_hist_hl_on_fs_4khz_detection_read_ev.jpg" %}
 
 Although, here we only have 200 delays (compared to 2000 before), we see that the detection of triggers with read*event is not faster than with the online flank detection, although we might be able to squeeze out a bit more performance (reduce latency) once we use a clever scheme for only reading \_new* events. This also depends on whether **acq2ftx** first writes the events or the samples to the buffer.
 
@@ -601,4 +603,4 @@ The code below will give you a sense for the distribution of time delays associa
 
 A typical distribution of access times is below:
 
-{% include image src="/assets/img/example/measuring_the_timing_delay_and_jitter_for_a_real-time_application/delay_read_header_acq_buffer.jpg" %}
+{% include image src="/assets/img/example/realtime_evaluation/delay_read_header_acq_buffer.jpg" %}

example/compute_forward_simulated_data.md → example/simulateddata.md

@@ -2,6 +2,8 @@
 title: Compute forward simulated data using ft_dipolesimulation
 category: example
 tags: [eeg, source, headmodel, dipole, simulation]
+redirect_from:
+    - /example/compute_forward_simulated_data/
 ---
 
 # Compute forward simulated data using ft_dipolesimulation

example/compute_forward_simulated_data_and_apply_a_beamformer_scan.md → example/simulateddata_beamformer.md

@@ -2,6 +2,8 @@
 title: Compute forward simulated data and apply a beamformer scan
 category: example
 tags: [meg, source, dipole]
+redirect_from:
+    - /example/compute_forward_simulated_data_and_apply_a_beamformer_scan/
 ---
 
 # Compute forward simulated data and apply a beamformer scan
@@ -63,4 +65,4 @@ This example script shows you how to create some simulated channel-level MEG dat
     cfg.funcolorlim = [1.4 1.5];  % the voxel in the center of the volume conductor messes up the autoscaling
     ft_sourceplot(cfg, source_nai);
 
-{% include image src="/assets/img/example/compute_forward_simulated_data_and_apply_a_beamformer_scan/example_beamforming.png" %}
+{% include image src="/assets/img/example/simulateddata_beamformer/example_beamforming.png" %}

example/use_simulated_erps_to_explore_cluster_statistics.md → example/simulateddata_clusterstats.md

@@ -2,6 +2,8 @@
 title: Use simulated ERPs to explore cluster statistics
 category: example
 tags: [statistics, cluster, simulation]
+redirect_from:
+    - /example/use_simulated_erps_to_explore_cluster_statistics/
 ---
 
 # Use simulated ERPs to explore cluster statistics

example/writing_simulated_data_to_a_ctf_dataset.md → example/simulateddata_ctf.md

@@ -2,6 +2,8 @@
 title: Can I create an artificial CTF dataset using MATLAB?
 category: example
 tags: [dataformat, ctf, meg]
+redirect_from:
+    - /example/writing_simulated_data_to_a_ctf_dataset/
 ---
 
 {% include markup/red %}

example/compute_forward_simulated_data_and_apply_a_dipole_fit.md → example/simulateddata_dipolefit.md

@@ -2,6 +2,8 @@
 title: Compute forward simulated data and apply a dipole fit
 category: example
 tags: [eeg, source, dipole]
+redirect_from:
+    - /example/compute_forward_simulated_data_and_apply_a_dipole_fit/
 ---
 
 # Compute forward simulated data and apply a dipole fit

example/sourcemodel_aligned2mni_atlas.md

@@ -53,7 +53,7 @@ Now we determine all indices of the binary mask to be considered as inside the h
     figure;
     ft_plot_mesh(template_grid.pos(template_grid.inside,:));
 
-{% include image src="/assets/img/example/create_single-subject_grids_in_individual_head_space_that_are_all_aligned_in_brain_atlas_based_mni_space/atlasbasedmnigrid.png" width="600" %}
+{% include image src="/assets/img/example/sourcemodel_aligned2mni_atlas/atlasbasedmnigrid.png" width="600" %}
 
 Load the subject-specific MRI from [here](https://download.fieldtriptoolbox.org/tutorial/salzburg/mri.mat) and inverse-warp the subject specific grid to the template grid.
 

example/combined_eeg_and_meg_source_reconstruction.md → example/sourcerecon_meeg.md

@@ -2,6 +2,8 @@
 title: Combined EEG and MEG source reconstruction
 category: example
 tags: [eeg, meg, headmodel, source]
+redirect_from:
+    - /example/combined_eeg_and_meg_source_reconstruction/
 ---
 
 # Combined EEG and MEG source reconstruction