A Novel Criterion for Ranking the Robustness of EEG/MEG Sensor-Space Connectivity Estimators against Volume Conduction Artifact

Khadem, Ali; Hossein-Zadeh, Gholam Ali

doi:10.22041/ijbme.2014.13050

Document Type : Full Research Paper

Authors

¹ PHD Candidate of Biomedical Engineering, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran

² Associate Professor of Biomedical Engineering, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran - School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM)

10.22041/ijbme.2014.13050

Abstract

In EEG/MEG datasets, the Volume Conduction (VC) artifact appears as instantaneous linear mixing of brain source activities on the channel measurements. A desired characteristic of an ideal EEG/MEG connectivity estimator (on sensor-space) is its robustness to VC artifact. This means that the VC of independent brain sources must never lead to detection of significant connectivity among EEG/MEG channels. There has been no criterion in the literature so far that can compare the robustness levels of different (sensor-space) connectivity estimators against VC artifact. In this paper, a criterion called Robustness Index (RI) is proposed to compare the robustness levels of connectivity estimators to channel couplings which are modeled by instantaneous linear mixing of quasi-independent components. Since the VC effects have instantaneous linear mixing nature, we expect RI to rank the connectivity estimators according to their robustness levels to VC artifact. RI is used to rank seven functional connectivity estimators: the absolute value of Pearson Correlation Coefficient (CC), Mutual Information (MI), Magnitude Squared Coherence (Coh), (1:1) Phase Locking Value ((1:1)PLV), the absolute value of Imaginary part of Coherency (ImC), Phase Lag Index (PLI) and Weighted Phase Lag Index (WPLI). The results for simulated data and a real EEG dataset show the connectivity estimators that are theoretically robust to VC artifact (ImC, PLI and WPLI) yield RI values near %100 and have the highest ranks, as expected. Also, for the simulated models in which the true VC effects and brain sources are known, ranking the connectivity estimators by RI is consistent with their robustness levels against VC artifact. This supports the possibility of using RI as a tool for ranking the robustness levels of connectivity estimators against VC artifact for real EEG/MEG datasets.

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

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Iranian Journal of Biomedical Engineering

A Novel Criterion for Ranking the Robustness of EEG/MEG Sensor-Space Connectivity Estimators against Volume Conduction Artifact

References

References

Volume 8, Issue 1 - Serial Number 1
March 2014
Pages 1-17

A Novel Criterion for Ranking the Robustness of EEG/MEG Sensor-Space Connectivity Estimators against Volume Conduction Artifact

References

References

Volume 8, Issue 1 - Serial Number 1March 2014Pages 1-17

Volume 8, Issue 1 - Serial Number 1
March 2014
Pages 1-17