Constructing EEG Large-Scale Cortical Functional Network Connectivity Based on Brain Atlas by S Estimator

Large-scale functional network connectivity (FNC) reveals the neural substrate of the cognitive process on a large-scale level. Electroencephalogram (EEG) is cost effective, portable, and noninvasive and is capable of capturing brain activities at a millisecond scale. Brain atlas derived from the anatomy clearly defines reliable functional subnetworks. In this article, we proposed to construct robust EEG FNC based on brain atlas by combining EEG source imaging with multivariate synchronization analysis to mine the brain’s large-scale information exchange. We evaluated the performances of two typical methods, canonical correlation analysis (CCA) and
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estimator, in quantifying the couplings among subnetworks by both simulation and application to real EEG data set. Simulation demonstrated that, compared to CCA,
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estimator shows high robustness and adaptability to low signal-to-noise ratio (SNR) and short length data. According to the FNC of P300, we further found that the FNC network constructed by
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estimator may be more consistent with the physiological mechanisms of P300 generation, where the
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estimator-based approach emphasizes the important role of the cerebellar network that has been proved to be involved in attention-related cognition tasks. This article provides a new tool to probe information processing during the cognition process at a higher hierarchal level.