latest update

Author: dungscout96

code/plugins/reformat_plugin.py

@@ -17,6 +17,7 @@ def reformat_wiki_pages(filepath, filename, parent, output_file, wiki_input_dir=
 layout: default
 parent: {parent}
 grand_parent: Plugins
+render_with_liquid: false
 '''.format(filename=filename, parent=parent)
 
     print(f"Reformatting {filename} of {parent}...")
@@ -45,7 +46,6 @@ def reformat_wiki_pages(filepath, filename, parent, output_file, wiki_input_dir=
             append_text += '''
 title: {title}
 long_title: {title}
-render_with_liquid: false
 '''.format(title=title)
 
             append_text += 'nav_order: {order}\n'.format(order=order+1)
@@ -86,7 +86,7 @@ def reformat_plugin_dir(plugin_input_dir, plugin_name, formatted_name, order, li
         shutil.copytree(os.path.join(plugin_input_dir, 'images'), os.path.join(plugin_output_dir, 'images'), dirs_exist_ok=True)
     # copy all .jpg and .png files from input to output dir
     for file in os.listdir(plugin_input_dir):
-        if file.endswith('.jpg') or file.endswith('.png'):
+        if file.endswith(('.png', '.jpg', '.gif')):
             shutil.copyfile(os.path.join(plugin_input_dir, file), os.path.join(plugin_output_dir, file))
 
     # if plugin is 'imat', copy the Docs directory recursively to the output directory
@@ -99,7 +99,8 @@ def reformat_plugin_dir(plugin_input_dir, plugin_name, formatted_name, order, li
 layout: default
 title: {plugin_name}
 long_title: {plugin_name}
-parent: Plugins'''.format(plugin_name=formatted_name)
+parent: Plugins
+render_with_liquid: false'''.format(plugin_name=formatted_name)
 
     if plugin_type == 'wiki':
         append_text += '\nhas_children: true'
@@ -110,14 +111,14 @@ def reformat_plugin_dir(plugin_input_dir, plugin_name, formatted_name, order, li
             shutil.copytree(os.path.join(wiki_plugin_input_dir, 'images'), os.path.join(plugin_output_dir, 'images'), dirs_exist_ok=True)
         # copy all .jpg and .png files from wiki input to output dir
         for file in os.listdir(wiki_plugin_input_dir):
-            if file.endswith('.jpg') or file.endswith('.png'):
+            if file.endswith('.jpg') or file.endswith(('.png', '.jpg', '.gif')):
                 shutil.copyfile(os.path.join(wiki_plugin_input_dir, file), os.path.join(plugin_output_dir, file))
 
 
         for root, dirs, files in os.walk(wiki_plugin_input_dir):
             for file in files:
                 if file.endswith('.md') and not file.startswith('index') and not file.startswith('Home'):
-                    reformat_wiki_pages(os.path.join(wiki_plugin_input_dir, file), file.strip('.md'), formatted_name, os.path.join(plugin_output_dir, file), wiki_plugin_input_dir)
+                    reformat_wiki_pages(os.path.join(wiki_plugin_input_dir, file), file.replace('.md', ''), formatted_name, os.path.join(plugin_output_dir, file), wiki_plugin_input_dir)
 
 
     with open(index_file) as f:

plugins/ARfitStudio/index.md

@@ -3,7 +3,8 @@ layout: default
 title: ARfitStudio
 long_title: ARfitStudio
 parent: Plugins
-nav_order: 18
+render_with_liquid: false
+nav_order: 20
 ---
 To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/ARfitStudio).
 

plugins/BVA-io/index.md

@@ -0,0 +1,23 @@
+---
+layout: default
+title: BVA-io
+long_title: BVA-io
+parent: Plugins
+render_with_liquid: false
+nav_order: 28
+---
+To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/bva-io).
+
+# BVA-io for Brain Vision Analyzer format
+
+This repository is a plugin for EEGLAB to import/export
+Brain Vision Analyzer EEG data files.
+
+# Version history
+v1.73 - Writing data now in MULTIPLEXED format as a new option; show warning when VMRK file is missing
+
+v1.72 - Avoid crashing when event file is absent
+
+v1.71 - Allow to import .ahdr files
+
+v1.7 - Better handling of VMRK and EEG file non-consistant witht the header VHDR file; allow empty marker info section; allow importing truncated binary file

plugins/BrainBeats/brainbeats_logo.png

@@ -0,0 +1,112 @@
+---
+layout: default
+title: BrainBeats
+long_title: BrainBeats
+parent: Plugins
+render_with_liquid: false
+nav_order: 9
+---
+To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/amisepa/BrainBeats).
+
+<!-- <p align="center"> -->
+# BrainBeats
+<!-- </p> -->
+
+<p align="center" width="100%">
+    <img width="30%" src="https://github.com/amisepa/BrainBeats/blob/main/brainbeats_logo2.png">
+</p>
+
+The BrainBeats toolbox, implemented as an EEGLAB plugin, allows joint processing and analysis of EEG and cardiovascular signals (ECG and PPG) for brain-heart interplay research. Both the general user interface (GUI) and command line are supported (see tutorial). BrainBeats currently supports: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO); 2) Extraction of EEG and HRV features; 3) Extraction of heart artifacts from EEG signals; 4) brain-heart coherence.
+
+ 
+## THREE METHODS AVAILABLE
+
+<p align="center" width="100%">
+    <img width="50%" src="https://github.com/amisepa/BrainBeats/blob/main/figures/diagram.png">
+</p>
+
+1) Process EEG data for heartbeat-evoked potentials (HEP) analysis using ECG or PPG signals. Steps include signal processing of EEG and cardiovascular signals, inserting R-peak markers into the EEG data, segmentation around the R-peaks with optimal window length, time-frequency decomposition.
+
+
+<p align="center">
+    Example of HEP at the subject level, obtained from simultaneous EEG-ECG signals
+</p>
+<p align="center" width="100%">
+    <img width="50%" src="https://github.com/amisepa/BrainBeats/blob/main/figures/fig11.png"> 
+</p>
+
+<p align="center">
+    Example of HEP at the subject level, obtained from simultaneous EEG-PPG signals
+</p>
+<p align="center" width="100%">
+    <img width="50%" src="https://github.com/amisepa/BrainBeats/blob/main/figures/fig17.png">
+</p>
+
+2) Extract EEG and HRV features from continuous data in the time, frequency, and nonlinear domains. 
+    - HRV time domain: SDNN, RMSSD, pNN50.
+    - HRV frequency domain: VLF-power, ULF-power, LF-power, HF-power, LF:HF ratio, Total power. 
+    - HRV nonlinear domain: Poincare, fuzzy entropy, fractal dimension, PRSA. 
+    
+    - EEG frequency domain: average band power (delta, theta, alpha, beta, gamma), individual alpha frequency (IAF), alpha asymmetry.
+    - EEG nonlinear domain: fuzzy entropy, fractal dimension
+
+
+<p align="center">
+    Example of power spectral density (PSD) estimated from HRV and EEG data
+</p>
+<p align="center" width="100%">
+    <img width="50%" src="https://github.com/amisepa/BrainBeats/blob/main/figures/fig21.png"> 
+</p>
+
+<p align="center">
+    Example of EEG features extracted from sample dataset
+</p>
+<p align="center" width="100%">
+    <img width="50%" src="https://github.com/amisepa/BrainBeats/blob/main/figures/fig22.png"> 
+</p>
+
+
+3) Remove heart components from EEG signals using ICA and ICLabel.
+   
+<p align="center">
+    Example of extraction of cardiovascular components from EEG signals
+</p>
+<p align="center" width="100%">
+    <img width="50%" src="https://github.com/amisepa/BrainBeats/blob/main/figures/fig27.png"> 
+</p>
+
+
+4) Compute brain-heart coherence (beta version, please test and give feedback)
+   
+<p align="center">
+    Example of several brain-heart coherence measures computed with BrainBeats from simultaneous EEG and ECG signals
+</p>
+<p align="center" width="100%">
+    <img width="50%" src="https://github.com/amisepa/BrainBeats/blob/main/figures/coherence_allfreqs.png"> 
+</p>
+
+<p align="center">
+    Scalp topography showing scalp regions coherent with ECG signal for each frequency band
+</p>
+<p align="center" width="100%">
+    <img width="50%" src="https://github.com/amisepa/BrainBeats/blob/main/figures/coherence_topo.png"> 
+</p>
+
+## Requirements
+
+- MATLAB installed (https://www.mathworks.com/downloads)
+- EEGLAB installed (https://github.com/sccn/eeglab)
+- Some data containing EEG and cardiovascular signals (ECG or PPG) within the same file (i.e. recorded simultaneously).
+  Or use the tutorial dataset provided in this repository located in the "sample_data" folder.
+
+## Step-by-step tutorial
+
+See our publication for a step-by-step tutorial using the sample dataset: https://www.jove.com/t/65829/brainbeats-as-an-open-source-eeglab-plugin-to-jointly-analyze-eeg
+
+Full-text preprint: https://www.biorxiv.org/content/10.1101/2023.06.01.543272v3.full
+
+## Version history
+
+v1.5 (5/2/2024) - METHOD 4 (brain-heart coherence) added
+
+v1.4 (4/1/2024) - publication JoVE (methods 1, 2, 3)

plugins/BrainBeats/brainbeats_logo2.png

@@ -1,9 +1,11 @@
 ---
 layout: default
-title: EEG-BIDS
-long_title: EEG-BIDS
 parent: EEG-BIDS
 grand_parent: Plugins
+render_with_liquid: false
+
+title: EEG‐BIDS-docs
+long_title: EEG‐BIDS-docs
 ---
 # Documentation and tutorial videos
 

plugins/BrainBeats/index.md

@@ -3,8 +3,9 @@ layout: default
 title: EEG-BIDS
 long_title: EEG-BIDS
 parent: Plugins
+render_with_liquid: false
 has_children: true
-nav_order: 2
+nav_order: 26
 ---
 To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/EEG-BIDS).
 

plugins/EEG-BIDS/EEG‐BIDS-docs.md

@@ -3,6 +3,7 @@ layout: default
 title: ICLabel
 long_title: ICLabel
 parent: Plugins
+render_with_liquid: false
 nav_order: 0
 ---
 To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/ICLabel).

plugins/EEG-BIDS/index.md

@@ -0,0 +1,16 @@
+---
+layout: default
+parent: MFF-matlab-io
+grand_parent: Plugins
+render_with_liquid: false
+
+title: 1.-Plugin-install
+long_title: 1.-Plugin-install
+---
+Select EEGLAB menu item **File > Import data > Using EEGLAB functions and plugins > From EGI/Phillips .mff file** as shown below.
+
+![](EEGLAB_install2.png)
+
+If the MFFmatlabio plugin has not be installed, EEGLAB will prompt you to install it (see also [plugin installation](Plugin-installation)). After this, MFFmatlabio will prompt an interface to select an MFF ressource as shown below.
+
+![](mff_import.png)

plugins/ICLabel/index.md

@@ -0,0 +1,27 @@
+---
+layout: default
+parent: MFF-matlab-io
+grand_parent: Plugins
+render_with_liquid: false
+
+title: 2.-Import-MFF-in-EEGLAB
+long_title: 2.-Import-MFF-in-EEGLAB
+---
+Select EEGLAB menu item **File > Import data > Using EEGLAB functions and plugins > From EGI/Phillips .mff file** as shown below.
+
+![](EEGLAB_install2.png)
+
+If the MFFmatlabio plugin has not be installed, EEGLAB will prompt you to install it (see also [plugin installation](Plugin-installation)). After this, MFFmatlabio will prompt an interface to select an MFF ressource as shown below.
+
+![](mff_import.png)
+
+Select a file and press **Open**. The import function will then prompt you about which MFF field or key should be mapped to EEGLAB data type. The selection will depend on your experiment and how you are collecting information during raw data collection. The field MFF event **Code** usually contains relevant information that may be used for EEGLAB type. Note that a combination of fields or keys may be selected. If you select more than one field, the import function will concatenate the field for each event.
+
+![](mff_import_code_select.png)
+
+Once you press OK, the import function will import the data and show the result in the main EEGLAB graphic interface.
+
+![](mff_import_info.png)
+
+You may then use EEGLAB as you would with any other dataset. Refer to the [EEGLAB tutorial](https://sccn.ucsd.edu/wiki/EEGLAB_Wiki) for more information.
+

plugins/MFF-matlab-io/1.-Plugin-install.md

@@ -0,0 +1,22 @@
+---
+layout: default
+parent: MFF-matlab-io
+grand_parent: Plugins
+render_with_liquid: false
+
+title: 3.-Export-to-Netstation
+long_title: 3.-Export-to-Netstation
+---
+Note that this section assumes that you have an EEGLAB dataset loaded in EEGLAB.
+
+To export data files to MFF format, use menu item **File > Export data > Export Phillips/EGI .mff file**.
+
+![](mff_export.png)
+
+Then enter a file name to export.
+
+![](mff_export_browse.png)
+
+Once the file has been exported, it can imported using Netstation as shown below.
+
+![](netstation.png)

plugins/MFF-matlab-io/2.-Import-MFF-in-EEGLAB.md

@@ -0,0 +1,66 @@
+---
+layout: default
+parent: MFF-matlab-io
+grand_parent: Plugins
+render_with_liquid: false
+
+title: 4.-Exporting-to-BIDS
+long_title: 4.-Exporting-to-BIDS
+---
+In this tutorial, we will see how to export a collection of .mff files to BIDS.
+
+First the [bids-matlab-tools](https://github.com/sccn/bids-matlab-tools) plugin of EEGLAB must be installed. This can be done from the EEGLAB plugin manager (EEGLAB menu item "File > Manage EEGLAB extensions"). 
+
+Once the plugin has been installed, an EEGLAB study must be created. We will be using [this collection](xxxxx) of 20 MFF files (the final BIDS exported version that contain all documentation about this experiment is available on [Openneuro](xxxx)).
+
+After downloading the files above (the version of the files before conversion to BIDS, we need to import them in EEGLAB and create a STUDY).
+
+Use EEGLAB menu item **File > Import data > Using EEGLAB functions and plugins > From EGI/Phillips .mff file**. Select a collection of files as shown below. Note that selecting a collection of file is only possible when they are in the same folder.
+
+![](bids_mff_folder.png)
+
+Because more than one file is being selected, the import function will then ask you if you want to automatically save datasets. Answer "Yes" to that question, otherwise you will not be able to create a valid EEGLAB STUDY using these datasets.
+
+![](bids_mff_save.png)
+
+A second shown below pops up to ask how to map the MFF fields to the EEGLAB type field. See also [plugin import](2.-Import-MFF-in-EEGLAB) for more information.
+
+![](mff_import_code_select.png)
+
+Then the EEGLAB main interface shows the first dataset imported as shown below.
+
+![](bids_multiple_datasets.png)
+
+The second step is to create a STUDY using all imported datasets. For this, select EEGLAB menu item **File > Create STUDY > Using all loaded datasets** as shown below.
+
+![](bids_create_study.png)
+
+If you have not set up memory options to retain at most one dataset in memory, the following warning message may appear. Simply press OK.
+
+![](bids_study_warming.png)
+
+The following interface will pop up. In this interface, you must enter an ID for each participant if it is not already present (if you fail to enter an ID for each participant, the STUDY interface will ask you to do so). When you are done, press **OK**. Note that if your datasets are missing channel labels, EEGLAB may ask you if you automatically want to create channel labels (answer yes to that question).
+
+![](bids_edit_study.png)
+
+Once the EEGLAB STUDY has been created, the EEGLAB interface will be updated as shown below.
+
+![](bids_study.png)
+
+Exporting STUDY to BIDS require that you install the [bids-matlab-tools](https://github.com/sccn/bids-matlab-tools) plugin of EEGLAB. Use the EEGLAB plugin manager (menu item **File > Plugin manager**) to do so. You may then export this STUDY to BIDS using menu item **File > Export > To BIDS folder structure** as shown below.
+
+![](bids_export.png)
+
+The BIDS interface shown below will pop up. The first input box will indicate the name of the folder to export BIDS data to, then the name of the study, reference and link, authors, distribution license information, README and CHANGE comments. You may enter a description of the experiment in the README section, and a list of authors. These are information which will generate warning messages if you do not include them. Press **OK** when done.
+
+![](bids_interface.png)
+
+Once the data has been exported, it can be validated using the online BIDS validator. This validator does not upload the data, simply scan the files and check for problem. This is the [validator home page](https://bids-standard.github.io/bids-validator/) below.
+
+![](bids_validate1.png)
+
+Select the folder where the data was exported and press OK. The validator will check all the data files and indicate if it encounters problems with the data. 
+
+![](bids_validate2.png)
+
+There is much more concerning the generation of BIDS compliant datasets such as individual dataset information, event and channel information that can be fine tuned. These topics are addressed in the [bids-matlab-tools](https://github.com/sccn/bids-matlab-tools) plugin of EEGLAB.