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Copy file name to clipboardExpand all lines: others/EEGLAB_Extensions.md
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--------------------------------------
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See the simple instructions under [How to contribute to
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EEGLAB](/tutorials/misc/Contributing_to_EEGLAB.html) to create EEGLAB compatible code.
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EEGLAB](/tutorials/contribute/) to create EEGLAB compatible code.
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Then, you may add your extension to the list above so that EEGLAB users can
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download it automatically from within EEGLAB. To do this, use [this
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-**[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).
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-**[PACT](http://sccn.ucsd.edu/wiki/PACT):** PACT is an EEGLAB extension for computing cross-frequency
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-**[PACT](https://github.com/sccn/PACT):** PACT is an EEGLAB extension for computing cross-frequency
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phase-amplitude coupling.
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-**[erpsource](https://github.com/sccn/erpsource)**: Source localization of ERPs using eLoreta.
frequency of 1 Hz is equivalent to passband edge at 2 Hz
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- When treating the line noise, use CleanLine() instead of notch
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filter because the former is phase-invariant.
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- When downsampling data (which is useful for multivariate Granger
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causality analysis), use mild anti-aliasing filter and do not let
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the stopband below the Nyquist frequency. In practice, use the
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following example. In this example, you are downsampling your data to 200Hz, with the cutoff
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frequency being 160Hz (i.e. 200Hz\*0.8) and the transient bandwidth 80Hz
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(i.e. 200Hz\*0.4).
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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).
Copy file name to clipboardExpand all lines: tutorials/04_Import/Importing_Continuous_and_Epoched_Data.md
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=======
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{: .no_toc }
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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.
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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.
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<detailsopenmarkdown="block">
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<summary>
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to read data from a (16-bit) short-integer file. The resulting MATLAB
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array may then be imported into EEGLAB, as shown above.
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Once the data is imported, refer to the [quickstart guide](/Tutorials/quickstart.html) to scroll the data.
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Once the data is imported, refer to the [quickstart guide](/tutorials/02_Quickstart/quickstart.html) to scroll the data.
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### Importing a file containing a MATLAB structure
Copy file name to clipboardExpand all lines: tutorials/05_Preprocess/rereferencing.md
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---
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Re-referencing the data
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======
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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.
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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.
Copy file name to clipboardExpand all lines: tutorials/06_RejectArtifacts/RunICA.md
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{: .no_toc }
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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
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information on ICA you can also refer to the [Appendix](/tutorials/IV.Appendix/ICA_background.html).
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information on ICA you can also refer to the [Appendix](/tutorials/ConceptsGuide/ICA_background.html).
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<detailsopenmarkdown="block">
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<summary>
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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.
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Refer to the [Appendix](/tutorials/IV.Appendix/ICA_background.html#note-on-ica-algorithms) for further information on
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Refer to the [ICA concept guide](/tutorials/ConceptsGuide/ICA_background.html#note-on-ica-algorithms) for further information on
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the different ICA algorithms.
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*Important note*
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When insufficient data are available, then use the 'pca' option to find fewer than N components may be the only good
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option. In general, it is important to give ICA as
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much data as possible for successful training.
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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.
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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.
Copy file name to clipboardExpand all lines: tutorials/06_RejectArtifacts/cleanrawdata.md
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Automated artifact rejection using other methods
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-------------------
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The are several EEGLAB plugins and legacy EEGLAB menus to reject bad data and bad channels. Use menu item <spanstyle="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 <spanstyle="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 <spanstyle="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).
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The are several EEGLAB plugins and legacy EEGLAB menus to reject bad data and bad channels. Use menu item <spanstyle="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 <spanstyle="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 <spanstyle="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).
Copy file name to clipboardExpand all lines: tutorials/10_Group_analysis/working_with_study_designs.md
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The list of independent variables is automatically generated
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based on the STUDY definition information and also based on events from
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each of the datasets. Every single event field (as visible in the <spanstyle="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
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being extracted from datasets, refer to the [STUDY design structure](/tutorials/multi-subject/EEGLAB-STUDY-data-structure.html)
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being extracted from datasets, refer to the [STUDY design structure](/tutorials/ConceptsGuide/Data_Structures.html#the-studydesign-sub-structure)
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tutorial.
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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.
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### Plotting ERPs for two designs
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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.
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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.
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First, we need to precompute measures. Select menu item <spanstyle="color: brown">Study → Precompute channel measures</span>, click the *ERP* checkbox, and press *Ok* (interface not shown). Then select menu item <spanstyle="color: brown">Study → Plot channel measures</span>. The following interface pops up. Select electrode *Oz*.
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This simple example shows that the range of possibilities for STUDY
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designs is large. More details about STUDY.design structure is available
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in the [STUDY structure](/tutorials/multi-subject/EEGLAB-STUDY-data-structure.html)
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in the [STUDY structure](/tutorials/ConceptsGuide/Data_Structures.html#the-study-structure)
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part of the tutorial.
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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.
Copy file name to clipboardExpand all lines: tutorials/ConceptsGuide/Data_Structures.md
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urevent. Hard 'boundary' events are important for functions such as [eeg_context.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=eeg_context.m) that are concerned with temporal relationships
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among events in the original experiment (i.e., among urevents).
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### The 'epoch' structure
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### EEG.epoch
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The *EEG.epoch* structure is empty in continuous datasets but is
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automatically filled during epoch extraction. It is computed from the *EEG.event* structure by the function [eeg_checkset.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=eeg_checkset.m) (with
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EEG = pop_loadset('myfile.set')
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```
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The *STUDY* structure
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The STUDY structure
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---------
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This section gives details of EEGLAB structures necessary for writing
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custom MATLAB scripts, functions, and plugins that operate on EEGLAB
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*ParentCluster*. This cluster does not contain those components whose
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equivalent dipole model exhibit a high percent variance from the
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component's scalp map. These components have been excluded from
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clustering (see [component clustering](/tutorial/10_Group_analysis/component_clustering_tools.html)
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clustering (see [component clustering](/tutorials/10_Group_analysis/component_clustering_tools.html)
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tutorial for more information on how to exclude components from clustering).
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Typing *STUDY.cluster* at the MATLAB command line returns
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### *STUDY* data files
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### STUDY data files
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When pre-computing measures for a specific *STUDY* design, some files are
Copy file name to clipboardExpand all lines: tutorials/contribute/Contributing_to_EEGLAB.md
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for Neural Computation at the University of California San Diego (UCSD). Core EEGLAB maintenance and development is supported by the US National Institute of Neurological Disorders and Stroke (NINDS).
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The EEGLAB core code cannot be modified by third parties directly. Instead,
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users submits [pull requests](/others/Fork_the_EEGLAB_repository.html). EEGLAB core developers then
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users submits [pull requests](/tutorials/contribute/Contributing_to_EEGLAB.html#forking-the-eeglab-repository). EEGLAB core developers then
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review the code and add it to the repository.
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For third-party developers, we welcome collaborations with users and
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The main EEGLAB repository is not the place to add new functions. If
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you want to add new functionality, write an EEGLAB
Copy file name to clipboardExpand all lines: tutorials/contribute/design_plugin.md
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Creating EEGLAB plugins and extension
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========================
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{: .no_toc }
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This section about creating plugins and extension is fairly long. However, creating an EEGLAB plugin is very simple. If you are in a rush, jump to the [example section](/contribute/design_plugin.html#eegplugin_-function-examples) and copy and paste code.
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This section about creating plugins and extension is fairly long. However, creating an EEGLAB plugin is very simple. If you are in a rush, jump to the [example section](/tutorials/contribute/design_plugin.html#eegplugin_-function-examples) and copy and paste code.
This other [section](/tutorials/RejectArtifacts/) of the tutorial contains methods that are currently recommended for rejecting artifacts.
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This other [section](/tutorials/06_RejectArtifacts/) of the tutorial contains methods that are currently recommended for rejecting artifacts.
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<spanstyle="color: red">The current section refers to obsolete methods that are no longer recommended for artifact rejections, given better methods are now available. In EEGLAB 2019.1 and later version, make sure to select the option <i>Show all menus from previous versions of EEGLAB</i> in EEGLAB menu item <spanstyle="color: brown">File > Preference</span> to be able to use the tools on this page.</span>
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