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Journal Article

Integration of multivariate data streams with bandpower signals


Mehnert,  Jan
Berlin Neuroimaging Center, Charité University Medicine Berlin, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Machine Learning, TU Berlin, Germany;
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea;

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Dähne, S., Bießmann, F., Meinecke, F. C., Mehnert, J., Fazli, S., & Müller, K.-R. (2013). Integration of multivariate data streams with bandpower signals. IEEE Transactions on Multimedia, 15(5), 1001-1013. doi:10.1109/TMM.2013.2250267.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0015-173A-1
The urge to further our understanding of multimodal neural data has recently become an important topic due to the ever increasing availability of simultaneously recorded data from different neural imaging modalities. In case where EEG is one of the modalities, it is of interest to relate a nonlinear function of the raw EEG time-domain signal, say, EEG band power, to another modality such as the hemodynamic response, as measured with NIRS or fMRI. In this work we tackle exactly this problem defining a novel algorithm that we denote multimodal source power correlation analysis (mSPoC). The validity and high performance of the mSPoC framework is demonstrated for simulated and real-world multimodal data.