sccn · arnodelorme · Mar 4, 2023 · Mar 4, 2023
diff --git a/Graveyard/Compiling_EEGLAB_Technical_note_for_developers.md b/Graveyard/Compiling_EEGLAB_Technical_note_for_developers.md
@@ -83,7 +83,7 @@ chmod 777 path2pathdef.m
 
 <!-- -->
 
--   Setup a *crontab* job to automatize the daily compilation. For this,
+-   Setup a *crontab* job to automate the daily compilation. For this,
     create a *crontab* job from the terminal, as indicated here;
 
 ``` powershell
@@ -232,7 +232,7 @@ management software.
 -   Download and install git and git manager software. In the current
     setup, [Git for Windows](https://gitforwindows.org/) has been used.
 -   Setup the EEGLAB git repository in the git bash. For this use, the
-    same code used as in the Mac and Ubuntu compliations -- but from the
+    same code used as in the Mac and Ubuntu compilations -- but from the
     Git Bash terminal.
 
 ``` powershell
@@ -245,7 +245,7 @@ git pull --recurse-submodules
 ```
 
 -   Add EEGLAB to the MATLAB path.
--   To automatize the daily compilation, the *Task Scheduler* in Windows
+-   To automate the daily compilation, the *Task Scheduler* in Windows
     is used. Follow the instructions below to create a new task.
 
 * From the *Start Menu*, open the *Task Scheduler*. if the program is
@@ -386,4 +386,4 @@ next release.
 |-------------------|-------------------------------------------------------------------------------------------------------|
 | Biosig            | Not necessary because included in Fieldtrip                                                           |
 | MFFMatlabIO       | Issue with finding the JAR file at execution time; more debugging necessary before inclusion possible |
-| bids-matlab-tools | Not tested                                                                                            |
+| bids-matlab-tools | Not tested                                                                                            |
diff --git a/Graveyard/Statistics.md b/Graveyard/Statistics.md
@@ -130,7 +130,7 @@ also identical.
 
 Taking GLM betas, t-scores or another single subject measure to the 2nd
 level is also fine (but keep in mind that the H0 and H1 pertain to the
-single-subject statistic, so the inference might be slightly diferent).
+single-subject statistic, so the inference might be slightly different).
 So that is why the statement "you must take logs" does not always hold,
 but for testing the difference in mean power it is indeed a good idea.
 
@@ -141,7 +141,7 @@ is does not really matter too much (in general). There might be cases
 where it does, but a monotonous transformation such as the log does not
 change the inference obn the similarity or difference of the underlying
 mean-power values. Or short: if the power is different, then log-power
-is also different. And if teh power is the same, then log-power is also
+is also different. And if the power is the same, then log-power is also
 the same. The probability of making these statements however is
 different (due to the homogeneity and non-normal variance affecting the
 test sensitivity).
@@ -288,4 +288,4 @@ location](http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&han
 analysis](http://www.ncbi.nlm.nih.gov/pubmed/23123297)
 
 [Wilcox reference
-book](http://www.amazon.com/Introduction-Estimation-Hypothesis-Statistical-Modeling/dp/0127515429/ref=la_B000APCI5U_1_3?ie=UTF8&qid=1353092305&sr=1-3)
+book](http://www.amazon.com/Introduction-Estimation-Hypothesis-Statistical-Modeling/dp/0127515429/ref=la_B000APCI5U_1_3?ie=UTF8&qid=1353092305&sr=1-3)
diff --git a/Graveyard/common_baseline.md b/Graveyard/common_baseline.md
@@ -1,7 +1,7 @@
 ---
 layout: default
 ---
-Common baseline accross ERSP condition
+Common baseline across ERSP condition
 =========
 
 When computing event-related spectral power (ERSP)

diff --git a/Graveyard/component_dipoles.md b/Graveyard/component_dipoles.md
@@ -13,7 +13,7 @@ The main criteria for recognizing brain-related components
 are that they have:
 
 1.  Dipole-like scalp maps,
-2.  Spectral peaks at typical EEG frequence is (i.e., 'EEG-like'
+2.  Spectral peaks at typical EEG frequency is (i.e., 'EEG-like'
     spectra) and,
 3.  Regular ERP-image plots (meaning that the component does not account
     for activity occurring in only a few trials).

diff --git a/code/README.md b/code/README.md
@@ -4,6 +4,6 @@ We use the theme [just-the-docs](https://pmarsceill.github.io/just-the-docs/) fo
 
 As *just-the-docs* is a Jekyll theme, some Jekyll documentation is relevant for the configuration of and some custom features for the wiki. 
 * For seeing how pages are grouped and urls are generated, see: https://jekyllrb.com/docs/pages/
-* Front matter variables is the way we provide metatdata of the page. So far we only need to worry about the variables that control the navigation structure of the page (see navigation structure link above). Just-the-docs will use these variables and automatically generate navigation features. For custom features, we can leverage front matter variables by programmatically accessing them through the [*page* object](https://jekyllrb.com/docs/variables/#page-variables). 
+* Front matter variables is the way we provide metadata of the page. So far we only need to worry about the variables that control the navigation structure of the page (see navigation structure link above). Just-the-docs will use these variables and automatically generate navigation features. For custom features, we can leverage front matter variables by programmatically accessing them through the [*page* object](https://jekyllrb.com/docs/variables/#page-variables). 
 * Coding with Jekyll page involves using Liquid, its language to programmatically and dynamically add content to static page. It's similar to PHP in the sense that you can mix Liquid with HTML in a page, but it's less powerful than PHP and work exclusively with the Jekyll ecosystem. Liquid's [documentation](https://shopify.github.io/liquid/basics/introduction/) is a helpful reference. Most of the time we look up examples provided on the web or Jekyll official documentation page for our specific problem.
 * Some knowledge of [directory structure](https://jekyllrb.com/docs/structure/) and [theme](https://jekyllrb.com/docs/themes/) is helpful when we write custom features for the site, as sometimes we have to override default behavior of the just-the-docs theme.
diff --git a/index.markdown b/index.markdown
@@ -41,7 +41,7 @@ Other type of documentation are listed below.
 ### Troubleshooting
 
 -    [Ask [email protected] (requires subscription here)](/others/EEGLAB_mailing_lists.html)
--    [Use Google - add "eeglablist" or "EEGLAB" to your querry](http://google.com)
+-    [Use Google - add "eeglablist" or "EEGLAB" to your query](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](https://github.com/sccn/eeglab-testcases)

diff --git a/others/EEGLAB_and_high_performance_computing.md b/others/EEGLAB_and_high_performance_computing.md
@@ -24,7 +24,7 @@ We have a funded project to run EEGLAB on the San Diego supercomputer via the Ne
 
 Deep learning and EEGLAB
 ----
-Deep learning is popular and deep learning applied to EEG data is increasing at a rapid pace. We recommend using EEGLAB to prepare data for deep learning and machine learning. EEGLAB data epochs may be concatenated either in MATLAB or Python (EEGLAB datasets may be read in Python using the *loadmat* function of the *scipy* librairy) and used as input for deep learning networks. In MATLAB, assuming multiple datasets are loaded in EEGLAB with the same number of channels and the same number of samples per epochs, type:
+Deep learning is popular and deep learning applied to EEG data is increasing at a rapid pace. We recommend using EEGLAB to prepare data for deep learning and machine learning. EEGLAB data epochs may be concatenated either in MATLAB or Python (EEGLAB datasets may be read in Python using the *loadmat* function of the *scipy* library) and used as input for deep learning networks. In MATLAB, assuming multiple datasets are loaded in EEGLAB with the same number of channels and the same number of samples per epochs, type:
 
 ```matlab
 X = cat(3, ALLEEG.data); % concatenate trials
@@ -250,7 +250,7 @@ in computer science is highly recommended.
 
 -   Reduce step: once all the workers have computed what they had to
     compute (spectral power for example), the reduce step may write it
-    back on S3 Amazon storage (and also do futher processing if
+    back on S3 Amazon storage (and also do further processing if
     necessary such as grouping back channels belonging to the same
     subject).
 

diff --git a/others/EEGLAB_and_python.md b/others/EEGLAB_and_python.md
@@ -150,7 +150,7 @@ plt.plot(EEG.data[0][0]);
 plt.show()
 ```
 
-The SCIPY Python library can import EEGLAB files, when the raw data is embeded in the *.set* file.
+The SCIPY Python library can import EEGLAB files, when the raw data is embedded in the *.set* file.
 
 ``` Python
 import scipy.io as sio

diff --git a/others/EEGLAB_revision_history.md b/others/EEGLAB_revision_history.md
@@ -146,7 +146,7 @@ Previous major EEGLAB versions (e.g., versions 13, 14, etc.) did not use this na
     only possible for ERP and spectrum).
 -   There is a <b>new plug-                                                  in manager</b> (there was a new one in 2019.0
     but it yet a newer one) which automates plug-in release for improved
-    stability. This new manager also has a rating and feedback mecanism.
+    stability. This new manager also has a rating and feedback mechanism.
     The old plug-in manager will be maintained for backward
     compatibility.
 -   We have improved further the compatibility with the LIMO toolbox.

diff --git a/others/EEGLAB_vs_Commercial_EEG_Software.md b/others/EEGLAB_vs_Commercial_EEG_Software.md
@@ -18,7 +18,7 @@ features in what is currently best in the industry.
 <table>
 <th></th>
 <th>EEGLAB</th>
-<th>Leading EEG commercial softwares</th>
+<th>Leading EEG commercial software</th>
 <tbody>
 <tr>
 	<td>Binary file import</td>
@@ -62,7 +62,7 @@ features in what is currently best in the industry.
 </tr>
 <tr>
 	<td>Preparation of figures for publication</td>
-	<td style="background-color:lightgreen">EEGLAB and MATLAB allow creation of complex figures with panels. Most of EEGLAB functions are compatible with panels so users may use EEGLAB function to generate their own paneled results. Formating details of figures may be edited directly under MATLAB from the command line or from the MATLAB GUI. Even complex figures containing bitmaps may be saved as postscript files for further detailed editing. MATLAB also allow saving figures and movies in about 10 different formats.</td>
+	<td style="background-color:lightgreen">EEGLAB and MATLAB allow creation of complex figures with panels. Most of EEGLAB functions are compatible with panels so users may use EEGLAB function to generate their own paneled results. Formatting details of figures may be edited directly under MATLAB from the command line or from the MATLAB GUI. Even complex figures containing bitmaps may be saved as postscript files for further detailed editing. MATLAB also allow saving figures and movies in about 10 different formats.</td>
 	<td style="background-color:#FFAAAA">Figures may only be saved using a few formats. Capabilities to build complex figures from within the software is absent.</td>
 </tr>
 <tr>

diff --git a/others/TIPS_and_FAQ.md b/others/TIPS_and_FAQ.md
@@ -165,9 +165,9 @@ but if
     some other (easier) way, we would appreciate.
 
 
-**Answer:** It actually depends on how you reccorded your Polhemus
+**Answer:** It actually depends on how you recorded your Polhemus
 coordinates. To fix this problem, under EEGLAB, in the channel editing
-window, there is a button "tranform axes". Press this button and enter
+window, there is a button "transform axes". Press this button and enter
 "theta = 90-theta;" and that will do the trick. Note: press the "auto
 shrink" button to visualize all your electrodes. You may also manipulate
 the XYZ coordinates and reconvert them to polar.
@@ -286,7 +286,7 @@ eyeblinks, etc. In my case, there are phases during the experiment,
 where people speak and/or move their eyes. I find it quite hard to
 determine which components are related to these artifacts and I already
 wonder if it is possible et all.
-**Answer:** To determine wich components are related to these artifacts,
+**Answer:** To determine which components are related to these artifacts,
 one approach is to isolate these trials (selecting them) and then use
 menu item "Plot \> Component ERPs \> With component maps" and select the
 time window where these event appear. This function will plot which
@@ -308,7 +308,7 @@ or more). Also their spectrum does not look like the standard EEG
 
 I am currently using ica to correct for artifacts. In the
 past I've visually inspected each single-trial epoch separately,
-indentified those trials with artifact activity, and then trained ICA on
+identified those trials with artifact activity, and then trained ICA on
 each trial separately to identify and remove artifactual components. As,
 you can imagine this process is extremely time consuming. Is it
 effective to train ICA on multiple or all concatenated trials at once,
@@ -473,7 +473,7 @@ Time Frequency
 ### Timef() spectral decompositions: properties and discrepancies
 
 Is it not true that the ERP for a condition can be
-completely reconstructed from the timef() results, incuding ITC? One
+completely reconstructed from the timef() results, including ITC? One
 could write a function that takes the outputs of timef
 ('times','freqs','ersp','itc') and gives as it's output the ERP. Could
 one then usefully manipulate the values in ERSP and ITC and see how the
@@ -576,7 +576,7 @@ Is there a function for computing time-varying coherence between
 Lachaux (from Varela's Lab) has several papers investigating this issue.
 
 
-**Answer:** We also programed multitaper methods for timef(), but
+**Answer:** We also programmed multitaper methods for timef(), but
 removed them from the current more flexible versions (not available yet
 on the Internet (May 23, 2003)). Note that we usually use neither the
 FFT (0) or N-cycle wavelet methods, but a compromise (0.5) setting that

diff --git a/tutorials/04_Import/Importing_Continuous_and_Epoched_Data.md b/tutorials/04_Import/Importing_Continuous_and_Epoched_Data.md
@@ -166,12 +166,12 @@ Finally, you will need to import concatenated data averages into EEGLAB as MATLA
 Select menu item <span style="color: brown">File → Importing data → From
 ascii/float file or MATLAB array</span> as shown in one of the previous sections.
 
-### Other/unsuported data formats
+### Other/unsupported data formats
 
 The EEGLAB discussion list archive also contains messages from users for
 importing specific data formats. You may search the list archive (and
 the rest of the EEGLAB web site) archive using Google and adding the keyword *eeglablist*.
 
 We are eager to add
-other data importing functions to EEGLAB, so please write a plugin and submit it on this [page](http://sccn.ucsd.edu/eeglab/plugin_uploader/upload_form.php). We are also in contact with consultants that can create EEGLAB plugins to import unsuported data format, so feel free to contact us.
+other data importing functions to EEGLAB, so please write a plugin and submit it on this [page](http://sccn.ucsd.edu/eeglab/plugin_uploader/upload_form.php). We are also in contact with consultants that can create EEGLAB plugins to import unsupported data format, so feel free to contact us.
 
diff --git a/tutorials/04_Import/Importing_Event_Epoch_Info.md b/tutorials/04_Import/Importing_Event_Epoch_Info.md
@@ -12,7 +12,7 @@ Importing and managing event and epoch information
 This tutorial describes importing, modifying, selecting, and visualizing EEGLAB events within the EEGLAB graphic interface.
 
 EEGLAB counts records of the time and nature of experimental events to
-analyze the EEG data. This section details how to load in events' information embeded in one of the data channels, stored in a MATLAB
+analyze the EEG data. This section details how to load in events' information embedded in one of the data channels, stored in a MATLAB
 array, or separate ASCII file. Once event information is imported, EEGLAB copies the resulting EEG.event structure to a back-up
 (*ur*) copy, EEG.urevent, and creates links from each event to the
 corresponding urevent. This allows the user to select events based on

diff --git a/tutorials/06_RejectArtifacts/RunICA.md b/tutorials/06_RejectArtifacts/RunICA.md
@@ -205,7 +205,7 @@ Studying and flagging artifactual ICA components
 Learning to recognize types of independent components may require
 experience. A later section on *Automated detection of artifactual ICA components* on this page contains links to an online tutorial for learning to recognize components.
 
-The main criteria to determine if a component is 1) cognitively related 2) a muscle artifac, or 3) some other type of
+The main criteria to determine if a component is 1) cognitively related 2) a muscle artifact, or 3) some other type of
 artifacts are
 - First, the scalp map (as shown above), 
 - Next the component time course, 
@@ -453,7 +453,7 @@ window with an expanded set of component property measures, as well as
 the estimated probabilities of each component being of each type.
 IC components will be plotted along with the category they most likely belong to and the likelihood of belonging to that category. Press *Ok* when done.
 
-Note that the probability that a component belongs to a given categogy is also available on the MATLAB command line. There are six categories of components <i>Brain</i>, <i>Muscle</i>, <i>Eye</i>, <i>Heart</i>, <i>Line Noise</i>, <i>Channel Noise</i>, and <i>Other</i>. By typing the following command on the MATLAB prompt, you can see the probability for each of the first ten components (rows) to belong to one of the component categories (columns):
+Note that the probability that a component belongs to a given category is also available on the MATLAB command line. There are six categories of components <i>Brain</i>, <i>Muscle</i>, <i>Eye</i>, <i>Heart</i>, <i>Line Noise</i>, <i>Channel Noise</i>, and <i>Other</i>. By typing the following command on the MATLAB prompt, you can see the probability for each of the first ten components (rows) to belong to one of the component categories (columns):
 
 ``` matlab
 >>  round(EEG.etc.ic_classification.ICLabel.classifications(1:10,:)*100)

diff --git a/tutorials/06_RejectArtifacts/Scrolling_data.md b/tutorials/06_RejectArtifacts/Scrolling_data.md
@@ -157,7 +157,7 @@ must be performed 'before' separating it into data epochs. To plot the data with
 
 We only performed this rejection for illustrative purposes.
 You may restart EEGLAB or switch back to the original dataset by selecting the main window menu item
-<span style="color: brown">Datasets → Dataset 1: Continous EEG data</span>.
+<span style="color: brown">Datasets → Dataset 1: Continuous EEG data</span>.
 
 ### Strategy for rejecting bad portions of data
 

diff --git a/tutorials/09_source/Custom_head_model.md b/tutorials/09_source/Custom_head_model.md
@@ -107,7 +107,7 @@ EEG = pop_multifit(EEG, 1:10,'threshold', 100, 'dipplot','off');
 pop_dipplot(EEG, [], 'normlen', 'on');
 ```
 
-The first command creates the head model from the MRI, segmenting it using Fieldtrip functions, which itself uses SPM functions. The second command aligns EEG or MEG electrodes with the head model and MRI. This is based on aligning fiducials which are both defined for the MRI and for the sensors. The alignment is performed automatically above, but it is always a good idea to check that the alignnment is correct. You may use the *plotalignment* option of the [pop_dipfit_settings.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop_dipfit_settings.m) to check the alignemnt.
+The first command creates the head model from the MRI, segmenting it using Fieldtrip functions, which itself uses SPM functions. The second command aligns EEG or MEG electrodes with the head model and MRI. This is based on aligning fiducials which are both defined for the MRI and for the sensors. The alignment is performed automatically above, but it is always a good idea to check that the alignnment is correct. You may use the *plotalignment* option of the [pop_dipfit_settings.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop_dipfit_settings.m) to check the alignment.
 
 Now the hard part of aligning all head model and electrodes has been accomplished. Next, we perform dipole search as in regular DIPFIT by calling the [pop_multifit.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop_multifit.m) and plot them using the [pop_dipplot.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop_dipplot.m). The plot below shows one of the component equivalent dipole.