How Different EEG References Influence Sensor Level Functional Connectivity Graphs

• Published in: Frontiers in neuroscience Vol. 11; p. 368
• Main Authors: Huang, Yunzhi, Zhang, Junpeng, Cui, Yuan, Yang, Gang, He, Ling, Liu, Qi, Yin, Guangfu
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 05.07.2017; Frontiers Media S.A
• Subjects: Biomedical engineering; Brain research; Ear; EEG; Electrodes; electroencephalograph references; Electroencephalography; Engineering; hamming distance; Neural networks; Neuroscience; relative error; REST; scalp functional connectivity graph; Standardization; Studies; China; REST; electroencephalograph references; relative error; scalp functional connectivity graph; hamming distance
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2017.00368

Hamming Distance is applied to distinguish the difference of functional connectivity networkThe orientations of sources are testified to influence the scalp Functional Connectivity Graph (FCG) from different references significantlyREST, the reference electrode standardization technique, is proved to have an overall stable and excellent performance in variable situations. The choice of an electroencephalograph (EEG) reference is a practical issue for the study of brain functional connectivity. To study how EEG reference influence functional connectivity estimation (FCE), this study compares the differences of FCE resulting from the different references such as REST (the reference electrode standardization technique), average reference (AR), linked mastoids (LM), and left mastoid references (LR). Simulations involve two parts. One is based on 300 dipolar pairs, which are located on the superficial cortex with a radial source direction. The other part is based on 20 dipolar pairs. In each pair, the dipoles have various orientation combinations. The relative error (RE) and Hamming distance (HD) between functional connectivity matrices of ideal recordings and that of recordings obtained with different references, are metrics to compare the differences of the scalp functional connectivity graph (FCG) derived from those two kinds of recordings. Lower RE and HD values imply more similarity between the two FCGs. Using the ideal recording (IR) as a standard, the results show that AR, LM and LR perform well only in specific conditions, i.e., AR performs stable when there is no upward component in sources' orientation. LR achieves desirable results when the sources' locations are away from left ear. LM achieves an indistinct difference with IR, i.e., when the distribution of source locations is symmetric along the line linking the two ears. However, REST not only achieves excellent performance for superficial and radial dipolar sources, but also achieves a stable and robust performance with variable source locations and orientations. Benefitting from the stable and robust performance of REST vs. other reference methods, REST might best recover the real FCG of EEG. Thus, REST based FCG may be a good candidate to compare the FCG of EEG based on different references from different labs.