Abstract
EEG and MEG are popular modalities for functional/effective brain connectivity estimation; however they suffer from Volume Conduction (VC) artifact. VC artifact which is an instantaneous linear mixing phenomenon may fake significant electrode couplings that are not due to true brain interactions. An ideal brain connectivity measure must be robust to VC artifact in the sense that it must never yield significant electrode couplings due to VC of independent sources. There are no criteria to compare the robustness of different brain functional/effective connectivity measures to VC artifact in real EEG/MEG datasets. In this paper, we propose a novel measure called Robustness Index (RI) using two surrogate data generation approaches to fill this gap. RI is estimated over both simulated data and real EEG dataset for four functional connectivity measures: the absolute value of Pearson Correlation Coefficient (CC), Mutual Information (MI), magnitude squared Coherence (Coh) and the absolute value of Imaginary part of Coherency (ImC). RI on both datasets has correctly near %100 values for ImC which is theoretically robust to VC artifact. Also, for both datasets, the connectivity measures are ranked by RI as 1-ImC, 2-MI, 3-Coh and 4-CC which is consistent with their robustness levels to VC artifact.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
