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Review Article | Open Access

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

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.

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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 EEGpreprocessingspectral analysisconnectivity analysismicrostate analysis

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Brain Science Advances
Volume 6 Issue 3,
September 2020
Pages 189-209
DOI: 10.26599/BSA.2020.9050019
Cite this article:
Li Z, Zhang L, Zhang F, et al. Demystifying signal processing techniques to extract resting- state EEG features for psychologists. Brain Science Advances, 2020, 6(3): 189-209. https://doi.org/10.26599/BSA.2020.9050019

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Received: 05 June 2020
Revised: 14 July 2020
Accepted: 23 July 2020
Published: 04 February 2021
© The authors 2020

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY- NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/ en-us/nam/open-access-at-sage).
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