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. 2018 Mar 2;12:4. doi: 10.3389/fninf.2018.00004

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Copyright © 2018 Liu, Ganzetti, Wenderoth and Mantini.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Workflow for the spatial independent component analysis (sICA)-based network analysis using high-density electroencephalography (hdEEG) data. The main analysis steps include: (1) Signal preprocessing, to attenuate noise and artifacts that are mixed in the EEG data; (2) Volume conduction modeling, to establish how brain sources can generate specific distributions of EEG potentials; (3) Brain activity reconstruction, to estimate -based on the clean EEG data and the volume conduction model- the distribution of active brain sources that most likely generates the EEG potentials; (4) Connectivity analysis, to obtain RSN maps by using sICA on source-space power envelopes.