This tutorial accompanies the paper titled "Multivariate pattern analysis for MEG: a comparison of dissimilarity measures", which is available here (preprint).
Citation: Guggenmos, M., Sterzer, P., & Cichy, R. M. (2018). Multivariate pattern analysis for MEG: a comparison of dissimilarity measures. NeuroImage. DOI: 10.1016/j.neuroimage.2018.02.044
This tutorial is based on IPython/Jupyter Notebook files, which are linked below. In addition, the tutorial can be downloaded as a zip file, which includes the notebook files, additional code files and the example dataset used for this tutorial. To reduce computational costs, the dataset is for one participant only and includes only 9 of 92 experimental conditions.
Content of the zip file:
| File | Description |
|---|---|
| cv.py | containing code for pseudo-trials/permutations/cross-validation |
| dissimilarity.py | containing a number of custom dissimilarity measures |
| weird.py | weighted robust distance classifier (WeiRD), see also here |
| python_decoding.ipynb | Notebook on Decoding |
| python_reliability.ipynb | Notebook on RDMs and Reliability |
| python_distance.ipynb | Notebook on Distance measures and cross-validation |
| data01_sess1.npy | data for subject 1, session 1 |
| data01_sess2.npy | data for subject 1, session 2 |
| labels01_sess1.npy | trial labels for subject 1, session 1 |
| labels01_sess2.npy | trial labels for subject 1, session 2 |
In addition, the tutorial requires 4 established scientific python packages: numpy, scipy, scikit-learn, matplotlib
This tutorial is based on IPython/Jupyter Notebook files, which are linked below. In addition, the tutorial can be downloaded as a zip file, which includes the notebook files, additional code files and the example dataset used for this tutorial. To reduce computational costs, the dataset is for one participant only and includes only 9 of 92 experimental conditions.
Content of the zip file:
| File | Description |
|---|---|
| cov1para.m | Shrinkage code (Ledoit & Wolf, 2004) for covariances |
| weirdtrain.m & weirdpredict.m | Weighted Robust Distance (WeiRD) classifier |
| gnbtrain.m & gnbpredict.m | Gaussian Naive Bayes (GNB) classifier |
| matlab_decoding.ipynb | Notebook on Decoding |
| matlab_reliability.ipynb | Notebook on RDMs and Reliability |
| matlab_distance.ipynb | Notebook on Distance measures and cross-validation |
| data01_sess1.mat | data for subject 1, session 1 |
| data01_sess2.mat | data for subject 1, session 2 |
| labels01_sess1.mat | trial labels for subject 1, session 1 |
| labels01_sess2.mat | trial labels for subject 1, session 2 |
In addition, the tutorial assumes a working LIBSVM installation for Matlab.