@article{DaeNikRamSchMueHau2014,
    author = {D\"ahne, Sven and Nikulin, Vadim V. and Ram{\'i}rez, David and Schreier, Peter J. and M\"uller, Klaus-Robert and Haufe, Stefan},
    journal = "NeuroImage",
    Title = "Finding brain oscillations with power dependencies in neuroimaging data",
    year = "2014",
    volume = "96",
    pages = "334-348",
    doi = "10.1016/j.neuroimage.2014.03.075",
    url = "http://www.sciencedirect.com/science/article/pii/S1053811914002365",
    pdf = "http://doc.ml.tu-berlin.de/bbci/publications/DaeNikRamSchMueHau2014.pdf",
    abstract = "Phase synchronization among neuronal oscillations within the same frequency band has been hypothesized to be a major mechanism for communication between different brain areas. On the other hand, cross-frequency communications are more flexible allowing interactions between oscillations with different frequencies. Among such cross-frequency interactions amplitude-to-amplitude interactions are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetencephalography ({EEG}/{MEG}) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low. In addition to using cSPoC for the analysis of cross-frequency interactions in the same subject, we show that it can also be utilized for studying amplitude dynamics of neuronal oscillations across subjects. We assess the performance of cSPoC in simulations as well as in three distinctively different analysis scenarios of real EEG data, each involving several subjects. In the simulations, cSPoC outperforms unsupervised state-of-art approaches. In the analysis of real EEG recordings, we demonstrate excellent unsupervised discovery of meaningful power-to-power couplings, within as well as across subjects and frequency bands."
}