Documentation: fixed spelling errors

SPMcentral · web-flow · commit 1b5f3ea71d08 · 2025-03-25T14:21:25.000Z
diff --git a/docs/manual/batch/batch.md b/docs/manual/batch/batch.md
@@ -51,7 +51,7 @@ data-reference="Chap:data:faces">[Chap:data:faces]</a>). To follow this
 tutorial, it is not necessary to download the example dataset, except
 for the last step (entering subject dependent data).
 
-To create a batch which can be re-used for multiple subjects in this
+To create a batch which can be reused for multiple subjects in this
 study, it is necessary to collect/define
 
 - study specific input data (e.g. MRI measurement parameters, time
@@ -120,7 +120,7 @@ necessary:
 
 - Results report (`SPM > Stats > Results Report`)
 
-Note that this examplar analysis pipeline is ancient and the
+Note that this exemplar analysis pipeline is ancient and the
 `SPM > Tools > Old Segment` and
 `SPM > Tools > Old Normalise > Old Normalise: Write` modules could be
 replaced by a single `SPM > Spatial > Normalise: Estimate & Write` one.
diff --git a/docs/reference/MEEG/eeg_DCM.md b/docs/reference/MEEG/eeg_DCM.md
@@ -371,7 +371,7 @@ are not sure. Then click on "Data" and in the box below click on "New:
 Subject". Click on "Subject" and in the box below on "New: Session".
 Click on models and in the selection window that comes up select the DCM
 mat files for all the models (remember the order in which you select the
-files as this is necessary for interpretating the results). Then run the
+files as this is necessary for interpreting the results). Then run the
 model comparison by pressing the green "Run" button. You will see, at
 the top, a bar plot of the log-model evidences for all models. At the
 bottom, you will see the probability, for each model, that it produced
diff --git a/docs/reference/MEEG/eeg_preprocessing.md b/docs/reference/MEEG/eeg_preprocessing.md
@@ -330,7 +330,7 @@ of 'Prepare', see below).
 The "Channel types" submenu allows reviewing and changing the channel
 types. Use the "Review" option to examine the presently set channel
 types. During conversion, SPM will make an informed *guess* at the
-correct channel types but this can sometimes go wrong, especiallly for
+correct channel types but this can sometimes go wrong, especially for
 EEG data. To set a particular channel group to some channel type, select
 this type from the menu. A list of all channels will appear. Select the
 subset whose type you would like to set. `Ctrl` and `Shift` buttons can
@@ -1486,7 +1486,7 @@ which, when run, repeats the analysis. The script can also be obtained
 by directly calling the function `spm_eeg_history`.  
   
 Of course, this script can not only be used to repeat an analysis, but
-the script can also be seen as a template that can be re-used for other
+the script can also be seen as a template that can be reused for other
 analyses. One needs minimal MATLAB knowledge for these changes. For
 example, you can replace the filenames to preprocess a different
 subject. Or you can change parameters and then re-run the analysis. We
diff --git a/docs/tutorials/MEEG/multi/fmri.md b/docs/tutorials/MEEG/multi/fmri.md
@@ -236,7 +236,7 @@ condition (averaged across 9 runs).
 Now we have a new set of 16$\times$<!-- -->3 NIfTI images for each
 subject and each condition, we can put them into the same
 repeated-measures ANOVA that we used to test for differences in power
-across sensors in the time-frequency analysis above, i.e, re-use the
+across sensors in the time-frequency analysis above, i.e, reuse the
 `batch_stats_rmANOVA_job.m` file created above. This can be scripted as:
 
 ```matlab
diff --git a/docs/tutorials/MEEG/multimodal/index.md b/docs/tutorials/MEEG/multimodal/index.md
@@ -1616,7 +1616,7 @@ Press the `Batch` button and then:
   list and set the `Deformation fields` to `Forward`.
 
 - Add a `Spatial` :material-arrow-right-bold: `Normalise` :material-arrow-right-bold: `Normalise: Write` 
-  module, make a `New: Subject`, and for the `Deformation Feild`, select a
+  module, make a `New: Subject`, and for the `Deformation Field`, select a
   `Dependency` of the `Segment: Forward Deformations` (from the prior
   segmentation module). For the "Images to Write", select a `Dependency`
   on the `Coreg: Estimate: Coregistered Images` (which will be all the
diff --git a/docs/tutorials/daiss/beamforming/neuromag/index.md b/docs/tutorials/daiss/beamforming/neuromag/index.md
@@ -451,7 +451,7 @@ The result of this should be a dataset written out labelled `MRapMcbdspmeeg_run_
 
 ### Visualising conditional differences
 
-How you process the end reults after this is down to you, but to quickly visualise the time-series differences between the three conditions, you can load in the dataset and take a subject average.
+How you process the end results after this is down to you, but to quickly visualise the time-series differences between the three conditions, you can load in the dataset and take a subject average.
 
 ```matlab
 
diff --git a/docs/tutorials/daiss/index.md b/docs/tutorials/daiss/index.md
@@ -13,7 +13,7 @@ DAiSS is composed of a series of blocks which allow the users to import their da
 
 1. **Data:** imports preprocessed data from SPM into the DAiSS pipeline, and sets whether we want to work in MNI space or the subjects native space. 
 2. **Sources:** Sets how the source space will be defined, either by using the canonical/individual mesh, a volumetric grid or some other cases. This module also calculates the dipole models for each source.
-3. **Features:** Generates and regularises feature matricies (such as a data covariance matrix) in preparation for source inversion.
+3. **Features:** Generates and regularises feature matrices (such as a data covariance matrix) in preparation for source inversion.
 4. **Inverse:** performs the source inversion (whether that is beamformer or MNE etc) and generates the weights vectors for virtual electrode calculation.
 5. **Output:** generates summary images about features in the data (e.g. power or coherence between brain and EMG recording) or allows the user to specify which regions of interest to export as virtual electrodes.
 6. **Write:** write out NIFTIs/GIFTIs/SPM MEEG datasets.
diff --git a/docs/tutorials/fmri/group/factorial.md b/docs/tutorials/fmri/group/factorial.md
@@ -13,7 +13,7 @@ We will use the contrast corresponding to overall task activation (`con_0009.nii
 5. Now, under `Factors` specify the factors you want to investigate, (1) `Name` :material-arrow-right-bold: `Handedness`, (2) `Name` :material-arrow-right-bold: `Response hand`. Leave the remaining options as default.
 6. Now let's input our data. Under `Specify subjects or all scans & factors` you'll have two ways to do this. You can either specify your subjects and factors on at a time or select all the relevant scans for all subjects in one step and manually specify a corresponding factor matrix. We will choose the latter option, `Specify subjects or all scans & factors` :material-arrow-right-bold: `Specify all`. 
 7. Under `Scans` we will specify all contrast images corresponding to task activation (`con_0009.nii`) for all participants. Using the selection window recursively filter for contrast `con_0009.nii`. To do this, navigate to `derivatives/first-level` via the left-hand side panel. In the filter box, type in `^con_0009.nii` and click the `Rec` button. You should see 40 files selected in the bottom window. Double check that the correct contrast and subjects have been selected. Confirm selection by pressing `Done`. 
-8. Now, we'll specify our factor matrix, which identifies which scans go with which experimental factors. SPM can model the effects of up to three factors, plus participant effects. Therefore the matrix has a maximum of 4 columns (`nscans-by-4`). For this example, we have 40 participants and one scan from each partcipant (`^con_0009.nii`). Our matrix is as follows:
+8. Now, we'll specify our factor matrix, which identifies which scans go with which experimental factors. SPM can model the effects of up to three factors, plus participant effects. Therefore the matrix has a maximum of 4 columns (`nscans-by-4`). For this example, we have 40 participants and one scan from each participant (`^con_0009.nii`). Our matrix is as follows:
     ```
     1	1	1
     2	1	1
diff --git a/docs/wikibooks/Batch.md b/docs/wikibooks/Batch.md
@@ -23,7 +23,7 @@ Any batch script should follow the template:
 
 ## Batch Script for SPM8
 
-SPM12\'s advices also apply to SPM8.
+SPM12\'s advice also apply to SPM8.
 
 The Batch Scripts for SPM5 below can also be used in SPM8.
 
diff --git a/docs/wikibooks/VBM.md b/docs/wikibooks/VBM.md
@@ -265,7 +265,7 @@ segmentation. The seg1 and seg2 images are entered into the program, and
 the result is a brain\_\*.img, which has values of 1 for brain, and 0
 for non-brain. The resulting brain\_\*.img is used to remove a few
 misclassified voxels from the \*\_seg1.img file. This is done using
-ImCalc, selecting the seg1\_, seg2\_, seg3\_, abd brain\_ images,
+ImCalc, selecting the seg1\_, seg2\_, seg3\_, and brain\_ images,
 entering an output filename, and the following expression:
 
 `       `*`i1.*i4./(i1+i2+i3+eps)`*
