From 13dfe161f7946953d132a89ca6badeb45b599025 Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Sat, 4 Mar 2023 12:46:24 +0100 Subject: [PATCH] Fix typos found by codespell --- ...Compiling_EEGLAB_Technical_note_for_developers.md | 8 ++++---- Graveyard/Statistics.md | 6 +++--- Graveyard/common_baseline.md | 2 +- Graveyard/component_dipoles.md | 2 +- code/README.md | 2 +- index.markdown | 2 +- others/EEGLAB_and_high_performance_computing.md | 4 ++-- others/EEGLAB_and_python.md | 2 +- others/EEGLAB_revision_history.md | 2 +- others/EEGLAB_vs_Commercial_EEG_Software.md | 4 ++-- others/TIPS_and_FAQ.md | 12 ++++++------ .../Importing_Continuous_and_Epoched_Data.md | 4 ++-- tutorials/04_Import/Importing_Event_Epoch_Info.md | 2 +- tutorials/06_RejectArtifacts/RunICA.md | 4 ++-- tutorials/06_RejectArtifacts/Scrolling_data.md | 2 +- tutorials/09_source/Custom_head_model.md | 2 +- tutorials/10_Group_analysis/advanced_statistics.md | 6 +++--- tutorials/10_Group_analysis/study_statistics.md | 2 +- .../11_Scripting/Event_Processing_command_line.md | 2 +- tutorials/11_Scripting/Using_EEGLAB_history.md | 4 ++-- tutorials/11_Scripting/automated_pipeline.md | 4 ++-- .../11_Scripting/command_line_study_functions.md | 4 ++-- tutorials/ConceptsGuide/Data_Structures.md | 4 ++-- tutorials/ConceptsGuide/rereferencing_background.md | 2 +- tutorials/misc/EEGLAB_and_MEG_data.md | 6 +++--- tutorials/misc/EEGLAB_and_iEEG_data.md | 2 +- tutorials/misc/Exporting_Data.md | 2 +- tutorials/tutorial_data.md | 2 +- workshops/EEGLAB_2009_Aspet.md | 2 +- workshops/EEGLAB_2009_Australia.md | 2 +- workshops/EEGLAB_2009_Bloomington.md | 2 +- workshops/EEGLAB_2010_Taiwan.md | 2 +- workshops/EEGLAB_2011_Mallorca.md | 2 +- workshops/EEGLAB_2016_at_UCSD.md | 2 +- 34 files changed, 56 insertions(+), 56 deletions(-) diff --git a/Graveyard/Compiling_EEGLAB_Technical_note_for_developers.md b/Graveyard/Compiling_EEGLAB_Technical_note_for_developers.md index 9ababb8e..45fcb10a 100644 --- 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 | \ No newline at end of file +| bids-matlab-tools | Not tested | diff --git a/Graveyard/Statistics.md b/Graveyard/Statistics.md index 8a2d19ba..8af1a603 100644 --- 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) \ No newline at end of file +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 index 43a4da60..71e57a82 100644 --- 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 index bf9a2d17..000499f4 100644 --- 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 index 26898b2d..cf8e7946 100644 --- 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 index 7fadbee7..fee81cff 100644 --- a/index.markdown +++ b/index.markdown @@ -41,7 +41,7 @@ Other type of documentation are listed below. ### Troubleshooting - [Ask eeglablist@sccn.ucsd.edu (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 index d04afb5d..a913554e 100644 --- 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 index a3cdb7cb..32a874e2 100644 --- 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 index 8b358ab5..f55bb98d 100644 --- 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 new plug- in manager (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 index 8b14231a..744ea9e4 100644 --- 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. - + @@ -62,7 +62,7 @@ features in what is currently best in the industry. - + diff --git a/others/TIPS_and_FAQ.md b/others/TIPS_and_FAQ.md index 814e8a35..c5eeebcc 100644 --- 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 index d8ac4da0..6b0b56e0 100644 --- 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 File → Importing data → From ascii/float file or MATLAB array 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 index 802d6d63..271469e0 100644 --- 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 index 0418d0e9..cfc6b0c9 100644 --- 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 Brain, Muscle, Eye, Heart, Line Noise, Channel Noise, and Other. 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 Brain, Muscle, Eye, Heart, Line Noise, Channel Noise, and Other. 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 index 523b1211..5e03e93d 100644 --- 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 -Datasets → Dataset 1: Continous EEG data. +Datasets → Dataset 1: Continuous EEG data. ### 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 index 1a5fa5da..201b7720 100644 --- 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. diff --git a/tutorials/10_Group_analysis/advanced_statistics.md b/tutorials/10_Group_analysis/advanced_statistics.md index be750576..28bb1183 100644 --- a/tutorials/10_Group_analysis/advanced_statistics.md +++ b/tutorials/10_Group_analysis/advanced_statistics.md @@ -13,7 +13,7 @@ Study Statistics and Visualization Options ============================================ {: .no_toc } -Advanced statistics are performed in LIMO (Linear Modeling of EEG data), an EEGLAB plugin, primarily developped by Cyril Pernet in collaboration with Arnaud Delorme. +Advanced statistics are performed in LIMO (Linear Modeling of EEG data), an EEGLAB plugin, primarily developed by Cyril Pernet in collaboration with Arnaud Delorme. The [LIMO toolbox](https://limo-eeg-toolbox.github.io/limo_meeg/) allows you to use general linear modeling approaches on an arbitrarilly large number of categorical and continuous variables. The EEGLAB team have recently developed a more user friendly interface for LIMO, that directly interfaces EEGLAB variables. The documentation about the old version of LIMO is available [here](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki). You may also refer to the @@ -21,8 +21,8 @@ The documentation about the old version of LIMO is available [here](https://gith Difference between optimization methods - Ordinary Least Square (WLS): This is the simplest and fastest method. Same as using the MATLAB glmfit function. -- Weighted Least Square (WLS): The default. This method attributes some weight to individual trials based their outlier likelyhood. -- Iterated Reweighted Least Square (IRLS): The default. This method attributes some weight to individual trials based their outlier likelyhood. +- Weighted Least Square (WLS): The default. This method attributes some weight to individual trials based their outlier likelihood. +- Iterated Reweighted Least Square (IRLS): The default. This method attributes some weight to individual trials based their outlier likelihood. LIMO Faq. ------ diff --git a/tutorials/10_Group_analysis/study_statistics.md b/tutorials/10_Group_analysis/study_statistics.md index 91e50009..42e43fba 100644 --- a/tutorials/10_Group_analysis/study_statistics.md +++ b/tutorials/10_Group_analysis/study_statistics.md @@ -120,7 +120,7 @@ We will study statistics of scalp topographies in the 150 ms to 300 ms range. To ![image not found](/assets/images/studystats18.png) -Press the *STATS* button and slect both independent variables as shown below. Select permutation statistics with no correction for multiple comparisons. Press *Ok*. +Press the *STATS* button and select both independent variables as shown below. Select permutation statistics with no correction for multiple comparisons. Press *Ok*. ![image not found](/assets/images/studystats19.png) diff --git a/tutorials/11_Scripting/Event_Processing_command_line.md b/tutorials/11_Scripting/Event_Processing_command_line.md index 2a1e7ada..f2fe5df6 100644 --- a/tutorials/11_Scripting/Event_Processing_command_line.md +++ b/tutorials/11_Scripting/Event_Processing_command_line.md @@ -46,7 +46,7 @@ EEG = pop_loadset( 'eeglab_data.set', fullfile(eeglab_path, 'sample_data')); % l ``` A -simple script below shift event latencies by 10 samples. Such manipulation are sometimes necessary because of delays introducted by the amplifier or computer collecting behavioral events. Alternatively, you may use the [pop_adjustevents.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop_adjustevents.m) function. The script +simple script below shift event latencies by 10 samples. Such manipulation are sometimes necessary because of delays introduced by the amplifier or computer collecting behavioral events. Alternatively, you may use the [pop_adjustevents.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop_adjustevents.m) function. The script from this section is available [here](http://sccn.ucsd.edu/eeglab/locatefile.php?file=event_processing_single_dataset.m). ``` matlab diff --git a/tutorials/11_Scripting/Using_EEGLAB_history.md b/tutorials/11_Scripting/Using_EEGLAB_history.md index 4b186c9c..71b9a020 100644 --- a/tutorials/11_Scripting/Using_EEGLAB_history.md +++ b/tutorials/11_Scripting/Using_EEGLAB_history.md @@ -112,8 +112,8 @@ The first two lines are comments. They are followed by 6 commands: - The second command loads the tutorial dataset - The third command saves the dataset in EEGLAB memory - The fourth command check the dataset consistency -- The fith command plots the data -- The seventh command refreshes the EEGLAB graphical interface (in case the current dataset was modified) +- The fifth command plots the data +- The sixth command refreshes the EEGLAB graphical interface (in case the current dataset was modified) Note: When the file was saved, an extra command, *\>\> eeglab redraw* was added at the end to ensure that the main graphic interface would be diff --git a/tutorials/11_Scripting/automated_pipeline.md b/tutorials/11_Scripting/automated_pipeline.md index 42b5f737..8a618c29 100644 --- a/tutorials/11_Scripting/automated_pipeline.md +++ b/tutorials/11_Scripting/automated_pipeline.md @@ -66,7 +66,7 @@ ALLEEG = pop_clean_rawdata( ALLEEG,'FlatlineCriterion',5,'ChannelCriterion',0.87 % them again after average reference - STUDY functions handle them automatically) ALLEEG = pop_reref( ALLEEG,[],'interpchan',[]); -% run ICA reducing the dimention by 1 to account for average reference +% run ICA reducing the dimension by 1 to account for average reference plugin_askinstall('picard', 'picard', 1); % install Picard plugin ALLEEG = pop_runica(ALLEEG, 'icatype','picard','concatcond','on','options',{'pca',-1}); @@ -135,7 +135,7 @@ ALLEEG = pop_clean_rawdata( ALLEEG,'FlatlineCriterion',5,'ChannelCriterion',0.87 % them again after average reference - STUDY functions handle them automatically) ALLEEG = pop_reref( ALLEEG,[],'interpchan',[]); -% run ICA reducing the dimention by 1 to account for average reference +% run ICA reducing the dimension by 1 to account for average reference plugin_askinstall('picard', 'picard', 1); % install Picard plugin ALLEEG = pop_runica(ALLEEG, 'icatype','picard','concatcond','on','options',{'pca',-1}); diff --git a/tutorials/11_Scripting/command_line_study_functions.md b/tutorials/11_Scripting/command_line_study_functions.md index 2d31b25f..12f025fd 100644 --- a/tutorials/11_Scripting/command_line_study_functions.md +++ b/tutorials/11_Scripting/command_line_study_functions.md @@ -263,7 +263,7 @@ See the help message of the [statcond.m](http://sccn.ucsd.edu/eeglab/locatefile. ### Saving results for processing in other software packages -Saving any STUDY result for subsequent processing in SPSS, Statistica, Stata, R, SAS, and Excell can easily be done from the command line. Saving a MATLAB array into a text file is simple in MATLAB. Below are five different options. All options are equivalent. Below we are saving the data for condition 1. Some save files with tab-separated values, while others save files with comma-separated values by default. Most functions below have many options. +Saving any STUDY result for subsequent processing in SPSS, Statistica, Stata, R, SAS, and Excel can easily be done from the command line. Saving a MATLAB array into a text file is simple in MATLAB. Below are five different options. All options are equivalent. Below we are saving the data for condition 1. Some save files with tab-separated values, while others save files with comma-separated values by default. Most functions below have many options. ``` matlab array = erpdata{1}; % or array = rand(100,200); @@ -377,7 +377,7 @@ Below is the code used for generating the measures: ### Component pre-clustering and clustering -For clustering ICA components, we usualy first compute all available activity measures. +For clustering ICA components, we usually first compute all available activity measures. To specify clustering on power spectra in the \[3 30\]-Hz frequency range, ERPs in the \[100 600\]-ms time window, dipole location information (weighted by 10), and ERSP information with diff --git a/tutorials/ConceptsGuide/Data_Structures.md b/tutorials/ConceptsGuide/Data_Structures.md index 7b141caa..1dcd574a 100644 --- a/tutorials/ConceptsGuide/Data_Structures.md +++ b/tutorials/ConceptsGuide/Data_Structures.md @@ -512,7 +512,7 @@ of size (channels, (sample_points\*trials)). This format makes handling events from the command-line more convenient. The purpose of the *EEG.urevent* structure is to retain the full record -of experimental events from the original continuous data, as shown inthe image below. Function [eeg_context.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=eeg_context.m) uses *urevents* +of experimental events from the original continuous data, as shown in the image below. Function [eeg_context.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=eeg_context.m) uses *urevents* information to find events defined by their neighboring event context in the experiment (and original data). @@ -916,7 +916,7 @@ The *STUDY.changrp* sub-structure is the equivalent of the the *STUDY.cluster* structure for data channels. There is usually as many element in *STUDY.changrp* as there are data channels. Each element of *STUDY.changrp* contains one data channels and regroup information for -this data channel accross all subjects. For instance, after precomputing +this data channel across all subjects. For instance, after precomputing channel measures, typing *STUDY.changrp(1)* may return ``` matlab diff --git a/tutorials/ConceptsGuide/rereferencing_background.md b/tutorials/ConceptsGuide/rereferencing_background.md index b6a4d407..df578ceb 100644 --- a/tutorials/ConceptsGuide/rereferencing_background.md +++ b/tutorials/ConceptsGuide/rereferencing_background.md @@ -7,7 +7,7 @@ grand_parent: Tutorials categories: concepts --- -Backgound on EEG references +Background on EEG references ===== Refer to the [re-referencing tutorial](/tutorials/05_Preprocess/rereferencing.html) for learning how to reference EEG data in EEGLAB. diff --git a/tutorials/misc/EEGLAB_and_MEG_data.md b/tutorials/misc/EEGLAB_and_MEG_data.md index 88f8ac21..015150d2 100644 --- a/tutorials/misc/EEGLAB_and_MEG_data.md +++ b/tutorials/misc/EEGLAB_and_MEG_data.md @@ -101,11 +101,11 @@ Press 'OK' to close the co-registration graphic interface and then 'OK' again to -When the anatomical MRI is not available, not all is lost. For example, this [publically available dataset](https://openneuro.org/datasets/ds004330/versions/1.0.0) does not contain MRI. What is important is to properly align the template head model with the subject head position in the MEG. This may be done with the fiducials but these are usually not sufficient. Using the fiducials, below we show alignment of the MEG sensors to the template head or to the subject's head extracted from the anatomical MRI. We can see a large difference between the two, even though the template head is stretched to match the subject's fiducial (this was not a rigid transformation). In other words, the fiducials capture the head position, but not the head geometry. If they did, we would expect the template head model to be stretched to match the subject's head. +When the anatomical MRI is not available, not all is lost. For example, this [publicly available dataset](https://openneuro.org/datasets/ds004330/versions/1.0.0) does not contain MRI. What is important is to properly align the template head model with the subject head position in the MEG. This may be done with the fiducials but these are usually not sufficient. Using the fiducials, below we show alignment of the MEG sensors to the template head or to the subject's head extracted from the anatomical MRI. We can see a large difference between the two, even though the template head is stretched to match the subject's fiducial (this was not a rigid transformation). In other words, the fiducials capture the head position, but not the head geometry. If they did, we would expect the template head model to be stretched to match the subject's head. ![Screen Shot 2022-12-23 at 2 26 26 PM](https://user-images.githubusercontent.com/1872705/209410775-d9e51fab-6bff-44fd-a05c-6506e3fcbd18.png) -One way to fix this and use a template head model is to use the location of the EEG channels when they are avaialble. EEG channels are usually scanned in the same coordinate space as the MEG sensors, so aligning and stretching the MEG head model to match the channel coordinates should be able to fix the problem above. A *headshape.pos* file is also sometimes available along with the MEG. It contains data points lying on the head of the subject and may be used to align the MEG sensor space to the anatomical MRI. However, this file may also be used to align and stretch the EEGLAB template MEG boundary element model to match the subject's head. To use this file, create a random data array on the MATLAB command line with the same number of scanned positions *a=rand(150, 1000);* and import it as a MATLAB array. Then, call the channel editor and import the *headshape.pos* file as an *SFP* file. You can then align the scanned position with the BEM head model. Write down the homogenous transformation matrix and reuse it for the MEG model alignment. +One way to fix this and use a template head model is to use the location of the EEG channels when they are available. EEG channels are usually scanned in the same coordinate space as the MEG sensors, so aligning and stretching the MEG head model to match the channel coordinates should be able to fix the problem above. A *headshape.pos* file is also sometimes available along with the MEG. It contains data points lying on the head of the subject and may be used to align the MEG sensor space to the anatomical MRI. However, this file may also be used to align and stretch the EEGLAB template MEG boundary element model to match the subject's head. To use this file, create a random data array on the MATLAB command line with the same number of scanned positions *a=rand(150, 1000);* and import it as a MATLAB array. Then, call the channel editor and import the *headshape.pos* file as an *SFP* file. You can then align the scanned position with the BEM head model. Write down the homogeneous transformation matrix and reuse it for the MEG model alignment. The main reason is that alignment of the head position with fiducials. @@ -126,7 +126,7 @@ MEG pipeline ------------ We have demonstrated MEG pipelines for the PracticalMEEG workshop. The code for the pipeline is available [here](https://github.com/sccn/practical_MEEG). In all of the scripts, including source localization, users can toggle between using the EEG and MEG data. -Other MEG ressources +Other MEG resources -------------------- MEG source localization in EEGLAB leverage Fieldtrip capabilities. Any head model designed in Fieldtrip may be used for source localization in EEGLAB. We invite you to check the other MATLAB-based MEG software tools below, which may be used along with EEGLAB from the MATLAB command line. diff --git a/tutorials/misc/EEGLAB_and_iEEG_data.md b/tutorials/misc/EEGLAB_and_iEEG_data.md index fdd603dd..e309c9a2 100644 --- a/tutorials/misc/EEGLAB_and_iEEG_data.md +++ b/tutorials/misc/EEGLAB_and_iEEG_data.md @@ -27,7 +27,7 @@ Below the channel locations for the same dataset above are shown. ![Screen Shot 2022-09-09 at 3 16 56 PM](https://user-images.githubusercontent.com/1872705/189453262-e942a285-b19f-455e-aad0-b38bbc62d0dd.png) -Even if you are not planning to use EEGLAB to process iEEG data, importing your iEEG data into EEGLAB and resaving it into an EEGLAB dataset may be useful to process it in other softwares. +Even if you are not planning to use EEGLAB to process iEEG data, importing your iEEG data into EEGLAB and resaving it into an EEGLAB dataset may be useful to process it in other software. Other relevant resources for processing iEEG data: - [Fieldtrip sEEG tutorial](https://www.fieldtriptoolbox.org/tutorial/human_ecog/) diff --git a/tutorials/misc/Exporting_Data.md b/tutorials/misc/Exporting_Data.md index 3fbd4da6..79dd01ea 100644 --- a/tutorials/misc/Exporting_Data.md +++ b/tutorials/misc/Exporting_Data.md @@ -146,4 +146,4 @@ function, [eeg_eventtable.m](http://sccn.ucsd.edu/eeglab/locatefile.php?file=pop Exporting output and STUDY results --- -The [scripting tutorial section](/tutorials/11_Scripting/command_line_study_functions.html#saving-results-for-processing-in-other-software-packages) describes solutions for exporting any results to a text or Excel file for subsequent processing in SPSS, Statistica, Stata, R, SAS, and Excell. +The [scripting tutorial section](/tutorials/11_Scripting/command_line_study_functions.html#saving-results-for-processing-in-other-software-packages) describes solutions for exporting any results to a text or Excel file for subsequent processing in SPSS, Statistica, Stata, R, SAS, and Excel. diff --git a/tutorials/tutorial_data.md b/tutorials/tutorial_data.md index 2e1695f2..b438277f 100644 --- a/tutorials/tutorial_data.md +++ b/tutorials/tutorial_data.md @@ -1,7 +1,7 @@ --- layout: default title: Tutorial data -long_title: Tutorial data and publically available EEG data +long_title: Tutorial data and publicly available EEG data parent: Tutorials --- EEG data available for public download diff --git a/workshops/EEGLAB_2009_Aspet.md b/workshops/EEGLAB_2009_Aspet.md index 1ddede70..ce086700 100644 --- a/workshops/EEGLAB_2009_Aspet.md +++ b/workshops/EEGLAB_2009_Aspet.md @@ -25,7 +25,7 @@ Workshop Program (with corresponding PDFs) 16:00 -- train station shuttle pick up -16:30 -- shuttle aiport pick up +16:30 -- shuttle airport pick up diff --git a/workshops/EEGLAB_2009_Australia.md b/workshops/EEGLAB_2009_Australia.md index e236c4e3..39ecabe0 100644 --- a/workshops/EEGLAB_2009_Australia.md +++ b/workshops/EEGLAB_2009_Australia.md @@ -9,7 +9,7 @@ grand_parent: Workshops === Newcastle, Australia Nov 25-Nov 27, 2009 -(Preceeding the [19th Australasian Psychophysiology Society +(Preceding the [19th Australasian Psychophysiology Society Conference](http://www.newcastle.edu.au/conference/asp2009/)) diff --git a/workshops/EEGLAB_2009_Bloomington.md b/workshops/EEGLAB_2009_Bloomington.md index e347f85d..aa2723b4 100644 --- a/workshops/EEGLAB_2009_Bloomington.md +++ b/workshops/EEGLAB_2009_Bloomington.md @@ -15,7 +15,7 @@ Seventh EEGLAB Workshop 7th EEGLAB Workshop Bloomington, IN Apr 20–22, 2009 -(Preceeding the EPIC conference) +(Preceding the EPIC conference) Workshop Program (with corresponding PDFs) diff --git a/workshops/EEGLAB_2010_Taiwan.md b/workshops/EEGLAB_2010_Taiwan.md index 9e31faad..b7d7411b 100644 --- a/workshops/EEGLAB_2010_Taiwan.md +++ b/workshops/EEGLAB_2010_Taiwan.md @@ -281,7 +281,7 @@ Relevant publications using ICA/EEGLAB dynamics](https://sccn.ucsd.edu/githubwiki/files/ticsreview_published.pdf). Trends Cogn Sci. 2004; May; 8(5):204-10. - Jung, TP, Makeig, S, Westerfield, M, Townsend, J, Courchesne, E, - Sejnowski, TJ. [Analysis and visualizaion of single-trial + Sejnowski, TJ. [Analysis and visualization of single-trial event-related potentials](https://sccn.ucsd.edu/githubwiki/files/jung_hbm01.pdf). Human Brain Mapping. 2001; 14(3), 166-185. - Delorme, A., Sejnowski, T., Makeig, S. [Improved rejection of diff --git a/workshops/EEGLAB_2011_Mallorca.md b/workshops/EEGLAB_2011_Mallorca.md index ae7ff758..c8f21b3f 100644 --- a/workshops/EEGLAB_2011_Mallorca.md +++ b/workshops/EEGLAB_2011_Mallorca.md @@ -35,7 +35,7 @@ related) data. 2\. Friday, Sept. 23 through noon on Sunday, Sept. 25, the first -Advanced EEGLAB Workshop introduced and demonstrate dthe use of +Advanced EEGLAB Workshop introduced and demonstrated the use of EEGLAB-linked tools for performing advanced analyses of EEG and related data, with detailed method expositions and practical exercises. diff --git a/workshops/EEGLAB_2016_at_UCSD.md b/workshops/EEGLAB_2016_at_UCSD.md index 5d85d8d8..878b3670 100644 --- a/workshops/EEGLAB_2016_at_UCSD.md +++ b/workshops/EEGLAB_2016_at_UCSD.md @@ -174,7 +174,7 @@ across subjects, sessions, groups, and conditions (Cyril Pernet) 3:30 - 3:45 PM -- Coffee break -3:45 - 5:00 -- D2.A3: LIMO advanved tool theory and practicum (Cyril Pernet) [Download PDF](https://sccn.ucsd.edu/githubwiki/files/pernet_limo_2.pdf) +3:45 - 5:00 -- D2.A3: LIMO advanced tool theory and practicum (Cyril Pernet) [Download PDF](https://sccn.ucsd.edu/githubwiki/files/pernet_limo_2.pdf) Track B (Vizlab) - High-resolution forward head modeling and source localization (Zeynep Akalin Acar) [Download PDF](https://sccn.ucsd.edu/githubwiki/files/nft_presentation16.pdf)
EEGLABLeading EEG commercial softwaresLeading EEG commercial software
Binary file import
Preparation of figures for publicationEEGLAB 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.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. Figures may only be saved using a few formats. Capabilities to build complex figures from within the software is absent.