Comparison of EEG source reconstructed functional networks in healthy subjects elicited during visual oddball task

Abstract

In this paper we have reconstructed electroencephalography (EEG) sources using weighted Minimum Norm Estimator (wMNE) for visual oddball experiment to estimate brain functional networks. Secondly we have evaluated the impact of time-frequency decomposition algorithms and scout functions on brain functional networks estimation using phase-locked value (PLV). Lastly, we compared the difference between target stimuli with response (TR) and non-target with no response (NTNR) cases in terms of brain functional connectivity (FC). We acquired the EEG data from 20 healthy participants using 129 channels EEG sensor array for visual oddball experiment. Three scout functions: i) MEAN, ii) MAX and iii) PCA were used to extract the regional time series signals. We transformed the regional time series signals into complex form using two methods: i) Wavelet Transform (WT) and ii) Hilbert Transform (HT). The instantaneous phases were extracted from the complex form of the regional time series signals. The FC was estimated using PLV. The joint capacity of the time-frequency decomposition algorithms/scout functions applied to reconstructed EEG sources was evaluated using two criteria: i) localization index (LI) and ii) R. The difference in FC between TR and NTNR cases was evaluated using these two criteria. Our results show that the WT has higher impact on LI values and it is better than HT in terms of consistency of the results as the standard deviation (SD) of WT is lower. In addition, WT/PCA pair is better than other pairs in terms of consistency as the SD of the pair is lower. This pair is able to estimate the connectivity within parietal region which corresponds to P300 response; although WT/MEAN is also able to do that, However, WT/PCA has lower SD than WT/MEAN. Lastly, the differences in connectivity between TR and NTNR cases over parietal, central, right temporal and limbic regions which correspond to target detection, P300 response and motor response were observed. Therefore, we conclude that the output of the connectivity estimation might be affected by time-frequency decomposition algorithms/scout functions pairs. Among the pairs, WT/PCA yields best results for the visual oddball task. Moreover, TR and NTNR cases are different in terms of estimated functional networks.