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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.


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Demystifying signal processing techniques to extract resting- state EEG features for psychologists

Show Author's information Zhenjiang Li1,§Libo Zhang2,3,§Fengrui Zhang2,3Ruolei Gu3,4Weiwei Peng5Li Hu2,3( )
School of Psychology, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China
Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
College of Psychology and Sociology, Shenzhen University, Shenzhen 518060, China

§ These authors contributed equally to this work.

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.

Keywords:

resting-state EEG, preprocessing, spectral analysis, connectivity analysis, microstate analysis
Received: 05 June 2020 Revised: 14 July 2020 Accepted: 23 July 2020 Published: 04 February 2021 Issue date: September 2020
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Publication history

Received: 05 June 2020
Revised: 14 July 2020
Accepted: 23 July 2020
Published: 04 February 2021
Issue date: September 2020

Copyright

© The authors 2020

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 31822025, No. 31671141). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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