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matlab_SPoC

This repository contains Matlab code for

• Source Power Correlation analysis (SPoC, Dähne et al. 2014a)
• multimodal Source Power Correlation analysis (mSPoC, Dähne et al., 2013)
• canonical Source Power Correlation analysis (cSPoC, Dähne et al., 2014b)
• Spatio-Spectral Decomposition (SSD) for dimensionality reduction (Nikulin et al., 2011, Haufe et al. 2014b)

Download the latest release from here: https://github.com/svendaehne/matlab_SPoC/releases/latest

Important Notes:

1. Please make sure the util folder (and all of its subfolders) are on the Matlab path. Otherwise the optimization required for (m/c)SPoC will not work! Run the startup_spoc.m script to add folders to the path.
2. Please read the documentation of the matlab functions ssd.m, spoc.m, mspoc.m, cspoc.m and run / look at the respective examples. I have tried to explain everything that you need to know to use the functions. If there is unclarity, please let me know and I will try to improve the documentation.
3. It is highly recommened to use dimensionality reduction via SSD before applying (m/c)SPoC. Dimensionality reduction greatly increases the computational speed and improves the quality of the results! Below you find a snippet of matlab code that shows an example of how to use SSD for preprocessing.
4. EEGLAB plugins are on the way!

References

S. Dähne, F. C. Meinecke, S. Haufe, J. Höhne, M. Tangermann, K. R. Müller, V. V. Nikulin, "SPoC: a novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters", NeuroImage, 86:111-122, 2014

S. Dähne, F. Biessman, F. C. Meinecke, J. Mehnert, S. Fazli, K. R. Müller, "Integration of Multivariate Data Streams With Bandpower Signals", IEEE Transactions on Multimedia, 15(5):1001-1013, 2013

S. Dähne, V. V. Nikulin, D. Ramirez, P. J. Schreier, K. R. Müller, S. Haufe, "Finding brain oscillations with power dependencies in neuroimaging data", NeuroImage, 96:334-348, 2014

S. Haufe, F. Meinecke, K. Görgen, S. Dähne, J. Haynes, B. Blankertz, F. Biessmann, "On the interpretation of weight vectors of linear models in multivariate neuroimaging", NeuroImage, 87:96-110, 2014

S. Haufe, S. Dähne, V. V. Nikulin, "Dimensionality reduction for the analysis of brain oscillations", NeuroImage, 101:583-597, 2014

V. Nikulin, G. Nolte, G. Curio, "A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition" , NeuroImage, 55(4):1528-35, 2011 .