Temporally resolved multi-way component analysis of dynamic sources in event-related EEG data using parafac2

The identification of signal components in electroencephalographic (EEG) data is a major task in neuroscience. The interest to this area has regained new interest due to the possibilities of multidimensional signal processing. In this contribution we analyze event-related multi-channel EEG recordings on the basis of the time-varying spectrum for each channel. To identify the signal components it is a common approach to use parallel factor (PARAFAC) analysis. However, the PARAFAC model cannot cope with components appearing time-shifted over the different channels. Furthermore, it is not possible to track PARAFAC components over time. We show how to overcome these problems by using the PARAFAC2 decomposition, which renders it an attractive approach for processing EEG data with highly dynamic (moving) sources. Additionally, we introduce the concept of PARAFAC2 component amplitudes, which resolve the scaling ambiguity in the PARAFAC2 model and can be used to judge the relevance of the components.

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