Evaluation of multi-dimensional decomposition models using synthetic moving EEG potentials

Abstract

To identify the scalp projections of the underlying sources of neural activity based on recorded electroencephalographic (EEG) signals, the multi-dimensional decomposition models Parallel Factor Analysis (PARAFAC) and Parallel Factor Analysis 2 (PARAFAC2) have recently attained interest. We evaluate the models based on synthetic EEG data, because this allows an objective assessment by comparing the estimated projections to the parameters of the sources. We simulate EEG data using the EEG forward solution and focus on dynamic sources that change their spatial projection over time. Recently, this type of signal has been identified as the dominant type of signal, e. g. in measurements of visual evoked potentials. Further, we develop a method to objectively evaluate the decomposition models. We show that the decomposition models reconstruct the scalp projections successfully from data with low signal-to-noise ratio (SNR). They perform best if the number of calculated components (model order) equals the number of sources.