Data fusion with tensors

Scalp EEG signal is a direct measure of the brain electric activity reflecting the postsynaptic cortical currents generated by the large pyramidal neurons which are located perpendicularly to the cortical surface. Functional MR imaging (fMRI) is another way of measuring the brain functions reflecting the oxygen metabolism in the brain. Temporal resolution of the BOLD (blood-oxygen-level-dependency) signal measured with fMRI is confined to the time course of the slowly evolving hemodynamic activity (~ 10 s) while exhibiting a high spatial resolution in the order of millimeters. Despite its low spatial resolution, the scalp EEG measures the brain functions with a high temporal resolution of miliseconds. The integration of EEG and fMRI on a common space and time scale by merging the superiorities of different imaging modalities, to reveal the complex dynamics of brain functions and neuronal interactions, is one of the major current problems of neuroimaging. In this study, a novel EEG-fMRI fusion approach based on multilinear methods is developed and applied to neurophysiological data. In this way, different platforms representing the brain activity data will be integrated to the same spatial and temporal scales. Hence, it aims to obtain functional images that enable to understand the brain functions and to analyze processes such as representation of memory, attention and information processing mechanisms better.

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