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On optimal spatial filtering for the detection of phase coupling in multivariate neural recordings

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Nikulin,  Vadim
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Neurology, Charité University Medicine Berlin, Germany;
Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia;

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Citation

Waterstraat, G., Curio, G., & Nikulin, V. (2017). On optimal spatial filtering for the detection of phase coupling in multivariate neural recordings. NeuroImage, 157, 331-340. doi:10.1016/j.neuroimage.2017.06.025.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002D-88F7-5
Abstract
Neuronal oscillations synchronize processing in the brain over large spatiotemporal scales and thereby facilitate integration of individual functional modules. Up to now, the relation between the phases of neuronal oscillations and behavior or perception has mainly been analyzed in sensor space of multivariate EEG/MEG recordings. However, sensor-space analysis distorts the topographies of the underlying neuronal sources and suffers from low signal-to-noise ratio. Instead, we propose an optimized source reconstruction approach (Phase Coupling Optimization, PCO).