This is Python code for Generic Decoding Demo.

All scripts are tested with Python 2.7.13. The following packages are required.

• bdpy
• numpy
• scipy
• pandas
• scikit-learn
• matplotlib (mandatory for creating figures)
• caffe (mandatory if you calculate image and category features by yourself)
• PIL (mandatory if you calculate image and category features by yourself)

All data should be placed in python/data. Data can be obrained from figshare. The data directory should have the following files:

data/ --+-- Subject1.mat (fMRI data, subject 1)
        |
        +-- Subject2.mat (fMRI data, subject 2)
        |
        +-- Subject3.mat (fMRI data, subject 3)
        |
        +-- Subject4.mat (fMRI data, subject 4)
        |
        +-- Subject5.mat (fMRI data, subject 5)
        |
        +-- ImageFeatures.h5 (image features extracted with Matconvnet)

Download links:

• Subject1.mat
• Subject2.mat
• Subject3.mat
• Subject4.mat
• Subject5.mat
• ImageFeatures.h5

• analysis_FeaturePrediction.py: Run image feature prediction for each subject, ROI, and layer (feature).
• analysis_FeaturePredictionMergeResults.py: Merge outputs of analysis_FeaturePrediction.py and calculate feature prediction accuracy.
• analysis_CategoryIdentification.py: Run category identification.
• createfigure.py: Create result figures.
• god_config.py: Define analysis parameters. This file is called in analysis_* scripts.

$ python analysis_FeaturePrediction.py
$ python analysis_FeaturePredictionMergeResults.py
$ python analysis_CategoryIdentification.py
$ python createfigure.py