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| FIX doesn’t find the classification file with the list of components to be removed, so the error could be either in the features extraction or in the classification. To see which is the problem have a look at the following log files: <subject.ica>/fix/logMatlab.txt (this should show errors in Matlab part, i.e. features extraction) <subject.ica>/.fix.log <subject.ica>/.fix_2b_predict.log (those are log file in general for the whole routine) You’ll probably find errors related to Matlab or R, so you might need to check your settings.sh file following the setup instructions described in the FIX README file |
FIX doesn’t find the classification file with the list of components to be removed, so the error could be either in the features extraction or in the classification. To see which is the problem have a look at the following log files: <subject.ica>/fix/logMatlab.txt (this should show errors in Matlab part, i.e. features extraction) <subject.ica>/.fix.log <subject.ica>/.fix_2b_predict.log (those are log file in general for the whole routine) You’ll probably find errors related to Matlab or R, so you might need to check your settings.sh file following the setup instructions described in the FIX README file |
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| FIX is more likely to work better with the training dataset that is most similar to your data, both in terms of acquisition parameters (TR and resolution) and preprocessing steps applied. Regarding the threshold to use, you can start with the “default” 20 and increase or decrease it according to FIX performance (i.e. visual check of the components' classification contained in the file fix4melview_TRAIN_thr.txt). For example, if it is very important to you that almost no good components are removed, and hence you would prefer to leave in the data a larger number of bad components, then use a low threshold. If you want to remove more noise, use a higher threshold. |
FIX is more likely to work better with the training dataset that is most similar to your data, both in terms of acquisition parameters (TR and resolution) and preprocessing steps applied. Regarding the threshold to use, you can start with the “default” 20 and increase or decrease it according to FIX performance (i.e. visual check of the components' classification contained in the file fix4melview_TRAIN_thr.txt). For example, if it is very important to you that almost no good components are removed, and hence you would prefer to leave in the data a larger number of bad components, then use a low threshold. If you want to remove more noise, use a higher threshold. |
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| FIX output is basically the (automated) equivalent of the output of fsl_regfilt, so you don’t need to run both: | FIX output is basically the (automated) equivalent of the output of fsl_regfilt, so you don’t need to run both: |
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| FIX automatically classifies the artefactual components and regress their contribution out of the data —> cleaned data | FIX automatically classifies the artefactual components and regress their contribution out of the data —> cleaned data |
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| To check that FIX is removing the artifactual components correctly (i.e. it is doing what you would do before running fsl_regfilt) you can check the classification done by FIX in the fix4melview….txt file and adjust the training dataset and threshold you are using as appropriate. | To check that FIX is removing the artifactual components correctly (i.e. it is doing what you would do before running fsl_regfilt) you can check the classification done by FIX in the fix4melview….txt file and adjust the training dataset and threshold you are using as appropriate. |
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| Yes, although you will probably need to create a study-specific training dataset | Yes, although you will probably need to create a study-specific training dataset |
Contents
- Introduction
- User Guide
- FAQ
Contents
- 1. When I run fix, I obtain the following output: “No valid labelling file specified” What does it mean?
- 2. How do I choose the best training dataset (among the existing ones) and/or threshold for my data?
- 3. What is the difference between fsl_regfilt and FIX?
- 4. Can I use FIX to clean task fMRI data?
1. When I run fix, I obtain the following output: “No valid labelling file specified” What does it mean?
- FIX doesn’t find the classification file with the list of components to be removed, so the error could be either in the features extraction or in the classification. To see which is the problem have a look at the following log files:
<subject.ica>/fix/logMatlab.txt (this should show errors in Matlab part, i.e. features extraction) <subject.ica>/.fix.log <subject.ica>/.fix_2b_predict.log (those are log file in general for the whole routine) You’ll probably find errors related to Matlab or R, so you might need to check your settings.sh file following the setup instructions described in the FIX README file
2. How do I choose the best training dataset (among the existing ones) and/or threshold for my data?
- FIX is more likely to work better with the training dataset that is most similar to your data, both in terms of acquisition parameters (TR and resolution) and preprocessing steps applied. Regarding the threshold to use, you can start with the “default” 20 and increase or decrease it according to FIX performance (i.e. visual check of the components' classification contained in the file fix4melview_TRAIN_thr.txt). For example, if it is very important to you that almost no good components are removed, and hence you would prefer to leave in the data a larger number of bad components, then use a low threshold. If you want to remove more noise, use a higher threshold.
3. What is the difference between fsl_regfilt and FIX?
- FIX output is basically the (automated) equivalent of the output of fsl_regfilt, so you don’t need to run both:
with fsl_regfilt you need to do a manual classification of unwanted components and run fsl_regfilt —> cleaned data
FIX automatically classifies the artefactual components and regress their contribution out of the data —> cleaned data
(the equivalent to fsl_regfilt would be using FIX with the –A option – see user guide)
- To check that FIX is removing the artifactual components correctly (i.e. it is doing what you would do before running fsl_regfilt) you can check the classification done by FIX in the fix4melview….txt file and adjust the training dataset and threshold you are using as appropriate.
4. Can I use FIX to clean task fMRI data?
- Yes, although you will probably need to create a study-specific training dataset