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A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis.

Li Dong | Xiaobo Liu | Lingling Zhao | Yongxiu Lai | Diankun Gong | Tiejun Liu | Dezhong Yao
Frontiers in neuroscience | 2019

In the event-related potential (ERP) of scalp electroencephalography (EEG) studies, the vertex reference (Cz), linked mastoids or ears (LM), and average reference (AVG) are popular reference methods, and the reference electrode standardization technique (REST) is increasingly applied. Because scalp EEG recordings are considered as spatially degraded signals, independent component analysis (ICA) is a widely used data-driven method for obtaining ERPs by decomposing EEG data. However, the accurate estimation of the differences in ERP components extracted by ICA with different references remains unclear. In this study, we first provided formal descriptions of the above reference methods (Cz, LM, AVG, and REST) and ICA decomposition in ERP and then investigated the influences of different reference techniques on simulation and real EEG datasets. The results revealed that (1) the reference method did not change the peak amplitudes and latencies of relative ERPs corresponding to some IC time courses; (2) there were non-negligible effects of different reference methods on both temporal ERPs and spatial topographies of some ICs; and (3) compared to Cz, LM, and AR, considering both the performances of temporal ERPs and spatial topographies, the REST reference had overall superiority. These findings provide a recommended choice of REST for ICA analysis at the trial level and contribute to empirical investigations regarding the use of reference methods in ERP domains with ICA analysis.

Pubmed ID: 31680810

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Neuroscience Information Toolbox (tool)

RRID:SCR_014501

A toolkit for EEG-fMRI multimodal fusion and fMRI data preprocessing and analysis. NIT allows users to perform batch processing of fMRI data analysis and data preprocessing based on SPM8, as well as parallel computing for data preprocessing, nuisance signals removal, and FCD and FOCA calculating. Users can also use NIT to calculate functional connectivity density and four dimensional (spatio-temporal) consistency of local neural activities.

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