A Predictive Modeling Approach to Analyze Data in EEG–fMRI Experiments

Abstract

In this paper, a novel technique based on blind source extraction (BSE) using linear prediction is proposed to extract rolandic beta rhythm from electroencephalogram (EEG) recorded in a simultaneous EEG–fMRI experiment. We call this method CLP–BSE standing for constrained-linear-prediction BSE. Extracting event-related oscillations is a crucial task due to nonphase-locked nature and inter-trial variability of this event. The main objective of this work is to extract rolandic beta rhythm to measure event-related synchronization (ERS) with acceptable signal-to-noise ratio (SNR). The extracted rhythm is utilized for constructing a regressor to analyze functional magnetic resonance imaging (fMRI). The proposed method is a semi-blind technique which uses a spatio-temporal constraint for beta rhythm extraction. This constraint is derived from recorded EEG signals based on the prior knowledge about the frequency and location of the source of interest. The main reason of employing linear prediction as an effective algorithm to extract the EEG rhythm is the ability of extracting sources which have specific temporal structure. Performance of the proposed method is evaluated using both synthetic and real EEG data. The obtained results show that the proposed technique is able to extract ERS effectively. The maximum percentage of ERS obtained by filtering is 152% while the obtained ERS by CLP–BSE is 214%. In another experiment, the extracted event-related oscillations in beta band are used to make the necessary regressor for fMRI analysis. The results of EEG–fMRI coregistration confirm that there are correlation between the extracted rolandic beta rhythm and simultaneously recorded fMRI. This conclude that, the results of EEG–fMRI combination support the reliability of CLP–BSE output.