Frequently Asked Questions
bidsmreye can not find subject / task
Check the following things:
Have you passed the correct path to your BIDS dataset?
This is one the most common errors, that can very easily happen when working with a containerized version of bidsmreye
Is your dataset a valid preprocessed fMRI BIDS dataset?
Try to rerun your command with the
--reset-databaseoption for force bidsmreye to reindex your input dataset.
How I should structure my input data?
bidsmreye requires a BIDS preprocessed dataset as input.
Two bids apps are available to generate those types of preprocessed data:
bidsmreye requires your input fmri data:
to be minimally preprocessed
with filenames and structure that conforms to a BIDS derivative dataset.
More specifically the dataset should look like this:
dataset_description.json
sub-{sub}
[ses-{session}]
func (func_dir)
sub-{sub}[_ses-{session}]_task-{task}[_acq-{acq}][_ce-{ce}][_dir-{dir}][_rec-{rec}][_run-{run_index}]_space-{space}[_res-{res}]_desc-preproc_bold.nii[.gz]
[participants.tsv]
[README]
[CHANGES]
[LICENSE]
Filename entities, files or directories between square brackets (for example,
[_ses-<label>]) are OPTIONAL. Note that for bidsmreye to work, thespaceentity is required.[.gz]means that both the unzipped and gzipped versions of the extension are valid.
Moreover the dataset_description.json file specify that the input dataset is a derivative dataset:
{
"Name": "my_dataset",
"BIDSVersion": "1.8.0",
"DatasetType": "derivative",
}
Is the “prepare” only suitable for the datasets that have eye-tracking info?
No the prepare action is necessary for all datasets,
whether they have eye-tracking info or not.
This action:
registers the data to MNI if this is not the case already
registers the data the the deepmreye template
extracts data from the eyes mask
In future versions of bidsmreye, this action should also be able to combine the extracted data with the eye gaze position coming from preprocessed eyetracking data.
Should I use the automatically generated methods section?
See the notes in this Nipreps page
Also there is this blogpost from the Stanford team behind fmriprep.
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