Here we provide the code that was used for the analysis of the open source
studyforrest data set.
DATA SET
The data set can be downloaded from
here. After the data set has been
downloaded we can preprocess the functional data and automatically detect eye
movements.
NOTE: the data set structure has changed since it was originally published and
it might be required to change some paths in the preprocessing pipeline.
The preprocessing can be performed with PreprocessRegex Matlab function. ex.
PreprocessRegex('/tmp/studyforrest/sub-*/')
For the eye movement detection we use the sp_tool which is available
here. Because the sp_tool uses
the ARFF file format as input we converted the eye tracking data to the ARFF format with
the Studyforrest2ArffRegex.m function. After conversion we called the sp_tool with
the provided default_parameters_studyforrest.conf.json configuration file as
follows:
./run_detection.py --config-file default_parameters_studyforrest.conf.json
NOTE: some paths in the configuration file have to be changed in order
to point to the correct directories.
The steps for the conversion of the gaze files and the detection of the eye
movements can be omitted because we provide them in this repository.
Finally in this repository we provide the mean motion of each frame of the
studyforrest videos as computed from the EpicFlow algorithm in the frame_motion directory.
fMRI ANALYSIS
For the fMRI analysis we have two directories (sp_saccade_analysis,
sp_saccade_motion_analysis) where we have different number of regressors
modelling the respective aspects of the experiment.
NOTE: before progressing further with the analysis you should download some Matlab
utilities that handle ARFF files from
here and add them to
your Matlab search path with the pathtool or addpath commands.
In order to run the analysis we start by computing the regressors of the 1st
level GLM by running the ComputeRegressorsAll.m Matlab function. The call to
this function should work straight out of the box without any tweaking. Then we
call the bash script 1stLevelAnalysis.sh after changing the used paths in
order to point to the correct directories. Basically the script goes through
the subdirectories that exist for each subject and runs the 1st level analysis
independently for each subject. Then we call the contrastAnalysis.sh script,
after changing the paths to point to the correct directories, which computes
the contrasts of interest. Finally in the 2nd level analysis we computed the
mean group effect by using a simple t-test across all subjects excluding subjects
05 and 20 due to substantial losses in eye tracking samples.
OPTIC FLOW EXTRACTION
For optic flow extraction we used the Minors of the Structure Tensor [1] and
the EpicFlow [2] algorithms. The earlier returns the flow in AVI formatted
videos and it does not have a open-source implementation. Therefore we provide
its output in the minors_flow folder. The latter is open source and it can be
downloaded from here. Because EpicFlow
reports 2 float values for each pixel, the resulting files for the studyforrest data
set were disproportionately large (155GB). For this reason we provide the script that
we used (videoFlow.sh), which can be run directly from the directory where
the EpicFlow and its dependencies were installed/extracted.
REFERENCES
[1] Barth, E. (2000). The minors of the structure tensor. In Mustererkennung
2000 (pp. 221-228). Springer, Berlin, Heidelberg.
[2] Revaud, J., Weinzaepfel, P., Harchaoui, Z., & Schmid, C. (2015). Epicflow:
Edge-preserving interpolation of correspondences for optical flow. In
Proceedings of the IEEE conference on computer vision and pattern recognition
(pp. 1164-1172).
