@article{Li2020, 
author = {Zhenjiang Li and Libo Zhang and Fengrui Zhang and Ruolei Gu and Weiwei Peng and Li Hu},
title = {Demystifying signal processing techniques to extract resting- state EEG features for psychologists},
year = {2020},
journal = {Brain Science Advances},
volume = {6},
number = {3},
pages = {189-209},
keywords = {resting-state EEG, preprocessing, spectral analysis, connectivity analysis, microstate analysis},
url = {https://www.sciopen.com/article/10.26599/BSA.2020.9050019},
doi = {10.26599/BSA.2020.9050019},
abstract = {Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non-invasively. Some important mental functions could be encoded by resting-state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting-state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting-state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting-state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft-used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.}
}