fixing all broken links

Author: arnodelorme

index.markdown

@@ -15,7 +15,7 @@ Over the last 17 or more years, EEGLAB ([Delorme & Makeig, 2004](/others/EEGLAB_
 -    [EEGLAB hardware and software
     recommendations](/others/EEGLAB_hardware_and_software_recommendations.html)
 -   [Download EEGLAB as a ZIP file](/others/How_to_download_EEGLAB.html)
--    [Download EEGLAB from GIT](/How_to_download_EEGLAB "wikilink")
+-    [Download EEGLAB from GIT](https://github.com/sccn/eeglab)
 -    [Download the compiled version of EEGLAB](/others/Compiled_EEGLAB.html)
 -    [EEGLAB extensions/plugins](/others/EEGLAB_Extensions.html)
 -    [EEGLAB revision history](/others/EEGLAB_revision_history.html)
@@ -40,9 +40,9 @@ Other type of documentation are listed below.
 -    [Use Google - add "eeglablist" or "EEGLAB" to your querry](http://google.com)
 -    [Bugs and Suggestions](/others/EEGLAB_Bugs.html)
 -    [Post a bug issues on Github](https://github.com/sccn/eeglab/issues)
--    [Download EEGLAB test cases](/EEGLAB_test_cases "wikilink")
--    [Other TIPS and FAQ](/FAQ/TIPS_and_FAQ)
--    [EEGLAB filtering FAQ](/FAQ/Firfilt_FAQ)
+-    [Download EEGLAB test cases](https://github.com/sccn/eeglab-testcases)
+-    [Other TIPS and FAQ](/others/TIPS_and_FAQ)
+-    [EEGLAB filtering FAQ](/others/Firfilt_FAQ)
 
 <h3><a href="/tutorials"><span style="color: black;">EEGLAB Tutorial</span></a></h3>
 {%- assign children_list = site.pages | where: "parent", "Tutorials" -%}

others/EEGLAB_Extensions.md

@@ -71,7 +71,7 @@ To contribute a new plugin
 --------------------------------------
 
 See the simple instructions under [How to contribute to
-EEGLAB](/tutorials/misc/Contributing_to_EEGLAB.html) to create EEGLAB compatible code.
+EEGLAB](/tutorials/contribute/) to create EEGLAB compatible code.
 
 Then, you may add your extension to the list above so that EEGLAB users can
 download it automatically from within EEGLAB. To do this, use [this
@@ -250,7 +250,7 @@ We only include the most popular ones below.
 
 - **[PACTools](https://github.com/sccn/PACTools)**: The Event-Related PACTools is an EEGLAB plugin to compute phase-amplitude coupling in single-subject data. In addition to traditional methods to compute PAC, the plugin includes the Instantaneous and Event-Related implementation of the Mutual Information Phase-Amplitude Coupling Method (MIPAC).
 
--   **[PACT](http://sccn.ucsd.edu/wiki/PACT):** PACT is an EEGLAB extension for computing cross-frequency
+-   **[PACT](https://github.com/sccn/PACT):** PACT is an EEGLAB extension for computing cross-frequency
     phase-amplitude coupling.
 
 - **[erpsource](https://github.com/sccn/erpsource)**: Source localization of ERPs using eLoreta.

others/Firfilt_FAQ.md

@@ -174,16 +174,13 @@ How can we address these problems?
     recommend you use 'Windowed sinc FIR filter' to design the filter so
     that it has the stopband at DC. (CAUTION: 'Windowed sinc FIR filter'
     uses **cutoff frequency** and not **passband edge** i.e. cutoff
-    frequency of 1 Hz is equivalent to passband edge at 2 Hz; See
-    [Q3](http://sccn.ucsd.edu/wiki/Firfilt_FAQ#Q._What_is_the_difference_between_the_.E2.80.9CBasic_FIR_filter_.28new.29.E2.80.9D_and_the_.E2.80.9CWindowed_sinc_FIR_filter.E2.80.9D.3F))
+    frequency of 1 Hz is equivalent to passband edge at 2 Hz
 -   When treating the line noise, use CleanLine() instead of notch
     filter because the former is phase-invariant.
 -   When downsampling data (which is useful for multivariate Granger
     causality analysis), use mild anti-aliasing filter and do not let
     the stopband below the Nyquist frequency. In practice, use the
-    following example. In this example, you are downsampling your data to 200Hz, with the cutoff
-frequency being 160Hz (i.e. 200Hz\*0.8) and the transient bandwidth 80Hz
-(i.e. 200Hz\*0.4).
+    following example. In this example, you are downsampling your data to 200Hz, with the cutoff frequency being 160Hz (i.e. 200Hz\*0.8) and the transient bandwidth 80Hz (i.e. 200Hz\*0.4).
 
     ```matlab
     EEG = pop_resample(EEG, 200, 0.8, 0.4);

others/How_to_download_EEGLAB.md

@@ -49,8 +49,8 @@ select *git pull*.
 ### Contributing code to EEGLAB
 
 To contribute code to EEGLAB, fork the code and create a pull request, as
-indicated on this [page](/others/Fork_the_EEGLAB_repository.html). This other
-[page](/tutorials/misc/Contributing_to_EEGLAB.html) contains other
+indicated on this [page](/tutorials/contribute/Contributing_to_EEGLAB.html#forking-the-eeglab-repository). This other
+[page](/tutorials/contribute) contains additional
 information on how to contribute to EEGLAB.
 
 ### Prior repository systems

others/Running_EEGLAB_on_Octave.md

@@ -116,8 +116,8 @@ to be more Octave compatible.
 If you modify an interactive EEGLAB
 function for that purpose and want others to benefit from your changes,
 fork the code and create a pull request as explained on [this
-page](/others/Fork_the_EEGLAB_repository.html). [This other 
-page](/tutorials/misc/Contributing_to_EEGLAB.html) contains other
+page](/tutorials/contribute/Contributing_to_EEGLAB.html#forking-the-eeglab-repository). [This other 
+page](/tutorials/contribute/) contains additional
 information on how to contribute to EEGLAB.
 
 A simple example of running an EEGLAB script in Octave

tutorials/04_Import/Importing_Continuous_and_Epoched_Data.md

@@ -9,7 +9,7 @@ Importing continuous and epoched data
 =======
 {: .no_toc }
 
-Refer to the [quickstart guide](/Tutorials/quickstart.html) to load an EEG data file, and scroll data. This section of the tutorial deals with importing raw data files in different formats, some of them only available through EEGLAB plugins. 
+Refer to the [quickstart guide](/tutorials/02_Quickstart/quickstart.html) to load an EEG data file, and scroll data. This section of the tutorial deals with importing raw data files in different formats, some of them only available through EEGLAB plugins. 
 
 <details open markdown="block">
   <summary>
@@ -88,7 +88,7 @@ toolbox command line function [shortread.m](http://sccn.ucsd.edu/eeglab/locatefi
 to read data from a (16-bit) short-integer file. The resulting MATLAB
 array may then be imported into EEGLAB, as shown above.
 
-Once the data is imported, refer to the [quickstart guide](/Tutorials/quickstart.html) to scroll the data.
+Once the data is imported, refer to the [quickstart guide](/tutorials/02_Quickstart/quickstart.html) to scroll the data.
 
 ### Importing a file containing a MATLAB structure
 

tutorials/05_Preprocess/rereferencing.md

@@ -7,7 +7,7 @@ grand_parent: Tutorials
 ---
 Re-referencing the data
 ======
-For detailed background theory on re-referencing EEG data, please refer to the [Appendix](/tutorials/IV.Appendix/rereferencing_background.html). We describe below how to specify the reference electrode(s) in EEGLAB and to (optionally) re-reference the data.
+For detailed background theory on re-referencing EEG data, please refer to the [Appendix](/tutorials/ConceptsGuide/rereferencing_background.html). We describe below how to specify the reference electrode(s) in EEGLAB and to (optionally) re-reference the data.
 
 Load the sample EEGLAB dataset
 -------------------------

tutorials/06_RejectArtifacts/RunICA.md

@@ -11,7 +11,7 @@ Independent Component Analysis for artifact removal
 {: .no_toc }
 
 Independent Component Analysis (ICA) may be used to remove/subtract artifacts embedded in the data (muscle, eye blinks, or eye movements) <i>without</i> removing the affected data portions. ICA may also be used to find brain sources, and we will come back to this topic in subsequent sections of the tutorial. For more theory and background 
-information on ICA you can also refer to the [Appendix](/tutorials/IV.Appendix/ICA_background.html).
+information on ICA you can also refer to the [Appendix](/tutorials/ConceptsGuide/ICA_background.html).
 
 <details open markdown="block">
   <summary>
@@ -67,7 +67,7 @@ To use the FastICA algorithm, one must install the [FastICA toolbox](http://www.
 
 You may install the [Amica](https://github.com/japalmer29/amica) and [postAmicaUtility](https://github.com/sccn/postAmicaUtility) plugins to use Amica in EEGLAB. By contrast with other ICA algorithms, Amica does not use the standard ICA interface and creates its own sets of menus.
 
-Refer to the [Appendix](/tutorials/IV.Appendix/ICA_background.html#note-on-ica-algorithms) for further information on 
+Refer to the [ICA concept guide](/tutorials/ConceptsGuide/ICA_background.html#note-on-ica-algorithms) for further information on 
 the different ICA algorithms.
 
 *Important note* 
@@ -80,7 +80,7 @@ then a considerable amount of data may be required to find N components.
 When insufficient data are available, then use the 'pca' option to find fewer than N components may be the only good
 option. In general, it is important to give ICA as
 much data as possible for successful training. 
-Refer to [this Appendix section](/tutorials/IV.Appendix/ICA_background.html#how-many-data-points-do-i-need-to-run-an-ica) for more details.
+Refer to [the ICA concept guide](/tutorials/ConceptsGuide/ICA_background.html#how-many-data-points-do-i-need-to-run-an-ica) for more details.
 
 ### Selecting channel types 
 

tutorials/06_RejectArtifacts/cleanrawdata.md

@@ -41,4 +41,4 @@ The result of rejecting data on the tutorial dataset are shown below. We can see
 
 Automated artifact rejection using other methods
 -------------------
-The are several EEGLAB plugins and legacy EEGLAB menus to reject bad data and bad channels. Use menu item <span style="color: brown">File → Preference</span> and check the checkbox to *If set, show all menu items from previous EEGLAB versions*. Restart EEGLAB for this change to take effect. A collection of menu items for rejecting bad portions of data become available. These involve <span style="color: brown">Tools → Automatic channel rejection</span> (see the help message of the [pop_rejchan.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop_rejchan.m) function) and <span style="color: brown">Tools → Automatic continuous rejection</span> (see help of the [pop_rejcont.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop_rejcont.m) function), which were the default methods used in previous versions of EEGLAB. A collection of menus to reject bad data epochs is also described in [this section of the tutorial](/Tutorials/Rejecting_Artifacts_Legacy_Menus.html).
+The are several EEGLAB plugins and legacy EEGLAB menus to reject bad data and bad channels. Use menu item <span style="color: brown">File → Preference</span> and check the checkbox to *If set, show all menu items from previous EEGLAB versions*. Restart EEGLAB for this change to take effect. A collection of menu items for rejecting bad portions of data become available. These involve <span style="color: brown">Tools → Automatic channel rejection</span> (see the help message of the [pop_rejchan.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop_rejchan.m) function) and <span style="color: brown">Tools → Automatic continuous rejection</span> (see help of the [pop_rejcont.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop_rejcont.m) function), which were the default methods used in previous versions of EEGLAB. A collection of menus to reject bad data epochs is also described in [this section of the tutorial](/tutorials/misc/Rejecting_Artifacts_Legacy_Menus.html).

tutorials/10_Group_analysis/working_with_study_designs.md

@@ -113,14 +113,14 @@ This selection is equivalent to selecting the three conditions in the previous d
 The list of independent variables is automatically generated
 based on the STUDY definition information and also based on events from
 each of the datasets. Every single event field (as visible in the <span style="color: brown">Edit → Event values</span>) is automatically made visible. Note that only information about the time-locking event is shown, and other events within data epochs are ignored. However, EEGLAB populates empty fields within data epochs with information from other events within the same epochs. For example, events might have a field *correct* belonging to *reaction time* events (not the time-locking event) containing true or false. All events have the same fields so other events will also have a *correct* event field, which will be empty since it is not defined for these events. If this is the case, then the value (true or false) is automatically copied to all events within a given epoch, and may be selected as an independent variable in the GUI above. For details on what information is
-being extracted from datasets, refer to the [STUDY design structure](/tutorials/multi-subject/EEGLAB-STUDY-data-structure.html)
+being extracted from datasets, refer to the [STUDY design structure](/tutorials/ConceptsGuide/Data_Structures.html#the-studydesign-sub-structure)
 tutorial. 
 
 You may also read the [event scripting tutorial](/tutorials/11_Scripting/Event_Processing_command_line.html#adding-event-information-for-group-analysis) for defining new independent variables based on event context.
 
 ### Plotting ERPs for two designs
 
-We describe in detail data plotting in the group analysis data [visualisation tutorial](/tutorials/10_Group_analysis/study_data_visualization_tools.html). However, we will plot and compare the ERPs for these two designs. 
+We describe in detail data plotting in the group analysis data [visualization tutorial](/tutorials/10_Group_analysis/study_data_visualization_tools.html). However, we will plot and compare the ERPs for these two designs. 
 
 First, we need to precompute measures. Select menu item <span style="color: brown">Study → Precompute channel measures</span>, click the *ERP* checkbox, and press *Ok* (interface not shown). Then select menu item <span style="color: brown">Study → Plot channel measures</span>. The following interface pops up. Select electrode *Oz*.
 
@@ -158,7 +158,7 @@ Here we have one panel per memory load. To interpret this plot, we would need to
 
 This simple example shows that the range of possibilities for STUDY
 designs is large. More details about STUDY.design structure is available
-in the [STUDY structure](/tutorials/multi-subject/EEGLAB-STUDY-data-structure.html)
+in the [STUDY structure](/tutorials/ConceptsGuide/Data_Structures.html#the-study-structure)
 part of the tutorial.
 
 For more complex designs, one must use the LIMO EEGLAB plugin. Refer to the [LIMO plugin documentation](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki) for more information.