fixing typos

Author: arnodelorme

plugins/SIFT/Chapter-7.-Statistics-in-SIFT.md

@@ -163,9 +163,9 @@ The connectivity matrix in the **dDTF08** substructure, for example, is of size
 
 ### 7.3.2.1. Comparing post-stimulus connectivity to baseline
 
-Bootstraping to compute confidence intervals remains relatively simple. You may randomly select data epochs and rerun the connectivity analysis multiple times.
+Bootstrapping to compute confidence intervals remains relatively simple. You may randomly select data epochs and rerun the connectivity analysis multiple times.
 
-**Important note:** You can look for effect using the simple thresholding method presented in the visualization section. However, computing significance of connectivity measures can take hours. This is also why it is only presented as a script. Be patient.
+**Important note:** You can look for effects using the simple thresholding method presented in the visualization section. However, computing significance of connectivity measures can take hours. This is also why it is only presented as a script. Be patient.
 
 If you have followed the tutorial, you need not prepare the data, but if you have not, the following script will apply the analyses performed in the previous sections of the tutorial (you still need to import the data with EEGLAB and perform EEGLAB-based preprocessing presented in section 5.2).
 

plugins/imat/index.md

@@ -11,7 +11,7 @@ To view the plugin source code, please visit the plugin's [GitHub repository](ht
 # IMAT - Independent Modulator Analysis Toolbox
 
 ## What is IMA?
-Independent Modulator Analysis (IMA) is a method for decomposing spectral fluctuations of temporally independent EEG sources into 'spatio-spectrally' distinct spectral modulator processes. Such processes might might derive from and isolate coordinated multiplicative scaling effects of functionally near-independent modulatory factors, for example the effects of modulations roduced in cortico-subcortical or sensory-cortical loops, or by signalling from brainstem-centered import recognition systems using dopamine, serotonin, noradrenaline, etc. (see schematic figure below from [Onton & Makeig, 2009](https://www.frontiersin.org/articles/10.3389/neuro.09.061.2009/full)). Rather than attempting to decompose the mean power spectrum for a component process to identify narrow-band processes superimposed on a 1/f baseline spectum, IMAT identifies characteristic frequency bands in which spectral power *varies* across time. This allows IMA to find *both* narrow and wide band modes. Also, the identified modes need not be singular. For example, IMA will separate the joint activity of an alpha or mu rhythm and its harmonics from endogenous beta band fluctuations occupying overlapping frequency ranges. IMA is applied to independent component (IC) source processes in the data which can be localized in the brain or to a specific scalp muscle, etc. IMA thereby identifies IC subsets that are co-modulated in a specified IM frequency band; these might be thought of as co-modulation networks with a common influence and susceptibility.
+Independent Modulator Analysis (IMA) is a method for decomposing spectral fluctuations of temporally independent EEG sources into 'spatio-spectrally' distinct spectral modulator processes. Such processes might derive from and isolate coordinated multiplicative scaling effects of functionally near-independent modulatory factors, for example the effects of modulations produced in cortico-subcortical or sensory-cortical loops, or by signaling from brainstem-centered import recognition systems using dopamine, serotonin, noradrenaline, etc. (see schematic figure below from [Onton & Makeig, 2009](https://www.frontiersin.org/articles/10.3389/neuro.09.061.2009/full)). Rather than attempting to decompose the mean power spectrum for a component process to identify narrow-band processes superimposed on a 1/f baseline spectrum, IMAT identifies characteristic frequency bands in which spectral power *varies* across time. This allows IMA to find *both* narrow and wide band modes. Also, the identified modes need not be singular. For example, IMA will separate the joint activity of an alpha or mu rhythm and its harmonics from endogenous beta band fluctuations occupying overlapping frequency ranges. IMA is applied to independent component (IC) source processes in the data which can be localized in the brain or to a specific scalp muscle, etc. IMA thereby identifies IC subsets that are co-modulated in a specified IM frequency band; these might be thought of as co-modulation networks with a common influence and susceptibility.
 
 <img src="./Docs/figs/IndependentModulators.png" width="400">  
 
@@ -194,7 +194,7 @@ In the resulting window (above right) we can specify:
 On the command line enter:  
 *pop_plotspecenv(EEG,'comps', [1 2 5], 'factors', [1 2 3 6], 'frqlim', [6 120], 'plotenv', 'full');*
 
-Here is an example of plotting IMs **Full envelope** of inflence on the IC power spectra. The IC mean log power spectrum is shown as a black trace. Outer light grey limits represent the 1st and 99th percentiles of IC spectral variation associated with the IM. Dark grey areas represent the 1st and 99th percentiles of the PCA-reduced spectral data used in the IMA analysis.
+Here is an example of plotting IMs **Full envelope** of influence on the IC power spectra. The IC mean log power spectrum is shown as a black trace. Outer light grey limits represent the 1st and 99th percentiles of IC spectral variation associated with the IM. Dark grey areas represent the 1st and 99th percentiles of the PCA-reduced spectral data used in the IMA analysis.
 
 <img src="./Docs/figs/plotenv_EC.png" width="600">
 

tutorials/ConceptsGuide/Data_Structures.md

@@ -67,7 +67,7 @@ line output:
                group: ''
            condition: ''
              session: []
-            comments: [9x769 charater]
+            comments: [9x769 character]
               nbchan: 32
               trials: 80
                 pnts: 384
@@ -1116,9 +1116,9 @@ For example:
 ```
 
 The data measures used in the clustering were the component spectra in a
-given frequency range ('' ‘freqrange' \[3 25\]*), the spectra were
-reduced to 10 principal dimensions (* 'npca' \[10\]*), normalized (*
-'norm' \[1\]*), and each given a weight of 1 (* 'weight' \[1\]'). When
+given frequency range ('' 'freqrange' [3 25]*), the spectra were
+reduced to 10 principal dimensions (* 'npca' [10]*), normalized (*
+'norm' [1]*), and each given a weight of 1 (* 'weight' [1]'). When
 more than one method is used for clustering, then *preclustparams* will
 contain several cell arrays.
 The *preclust.preclustdata* field contains the data given to the

tutorials/ConceptsGuide/Setting_up_your_EEG_lab.md

@@ -115,17 +115,17 @@ Recording EEG data in a Faraday cage will lead to better signal quality and no l
 
 * The subject and the experimenter should be isolated in a separate room. If they communicate with an intercom, make sure the intercom is far away from the subject. Do not use an intercom that relies on the electrical circuit to transmit the signal.
 
-* Remove metal objects touching the subject (or very close). For example, the subject should not be sitting on a metal chair. If the subject needs to respond to stimuli, make sure the button press or mouse pap is nonmetallic.
+* Remove metal objects touching the subject (or very close). For example, the subject should not be sitting on a metal chair. If the subject needs to respond to stimuli, make sure the button press or mouse pad is nonmetallic.
 
 * Remove any non-shielded battery or backup power. Non-shielded batteries can create very large noise artifacts, especially when plugged into the wall. Laptops, if held far away from the subject, are probably OK. Laptops should not be plugged into the wall and run on battery if present with the subject in the recording room.
 
 * It is probably a good idea for the subject to remove anything that could be used as an antenna on his body (e.g., a metal watch). 
 
-* Compute screens and light are the major sources of 50 Hz noise in the EEG. These should be placed as far as possible from the subject. Reduction of the 50 Hz noise is very important, as in practice, the lower the 50 noise is, the lower the noise is at other frequencies. One technique we have used is to have one electrode on a pole and go around the room to try to detect the source of noise (outlet, screen, etc.). EMF detectors can probably also be used. 
+* Computer screens and light are the major sources of 50 Hz noise in the EEG. These should be placed as far as possible from the subject. Reduction of the 50 Hz noise is very important, as in practice, the lower the 50 Hz noise is, the lower the noise is at other frequencies. One technique we have used is to have one electrode on a pole and go around the room to try to detect the source of noise (outlet, screen, etc.). EMF detectors can probably also be used. 
 
 ## Applying gel
 
-Using the syringe filled with gel, push down on the electrode well (so the gel doesn’t spread to other parts of the cap), and part the participant’s hair with the needle until you reach the skin. Then squeeze a small amount of gel into each well. Press firmly but not so firmly that the subject experiences pain. DO NOT PUT TOO MUCH GEL IN, otherwise, the gel will spread between electrode wells across the scalp, merging multiple distinct EEG signals into one.
+Using the syringe filled with gel, push down on the electrode well (so the gel doesn't spread to other parts of the cap), and part the participant's hair with the needle until you reach the skin. Then squeeze a small amount of gel into each well. Press firmly but not so firmly that the subject experiences pain. DO NOT PUT TOO MUCH GEL IN, otherwise, the gel will spread between electrode wells across the scalp, merging multiple distinct EEG signals into one.
 
 Slightly irritating the scalp of the subject by asking them to brush their hair for 5 minutes may decrease electrode impedance and increase signal quality. 
 
@@ -137,7 +137,7 @@ Decreasing electrode impedance and maximizing data quality (even for high-impeda
 
 Scanning electrode position is easy (a smartphone and an app can construct detailed 3-D models) and can improve source location (even in the absence of the subject MRI). It should be done systematically even if you are not sure you are going to use that information (see this [page](https://github.com/sccn/get_chanlocs/wiki) for more information). 
 
-## EEG synchronisation
+## EEG synchronization
 
 Synchronizing EEG with experimental events is critical and needs to be performed with millisecond precision (in psychophysics, a 10-millisecond difference in reaction time is considered large). Here are a few tips.