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Review Article | Open Access

Demystifying signal processing techniques to extract task- related EEG responses for psychologists

CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China
Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
School of Psychology, Jiangxi Normal University, Nanchang 330022, China
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
College of Psychology and Sociology, Shenzhen University, Shenzhen 518060, China
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Abstract

To investigate neural mechanisms of human psychology with electroencephalography (EEG), we typically instruct participants to perform certain tasks with simultaneous recording of their brain activities. The identification of task-related EEG responses requires data analysis techniques that are normally different from methods for analyzing resting-state EEG. This review aims to demystify commonly used signal processing methods for identifying task-related EEG activities for psychologists. To achieve this goal, we first highlight the different preprocessing pipelines between task-related EEG and resting-state EEG. We then discuss the methods to extract and visualize event-related potentials in the time domain and event-related oscillatory responses in the time-frequency domain. Potential applications of advanced techniques such as source analysis and single-trial analysis are briefly discussed. We conclude this review with a short summary of task-related EEG data analysis, recommendations for further study, and caveats we should take heed of.

Keywords

task-related EEGpreprocessingtime domain analysistime-frequency analysis

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Brain Science Advances
Volume 6 Issue 3,
September 2020
Pages 171-188
DOI: 10.26599/BSA.2020.9050018
Cite this article:
Zhang L, Li Z, Zhang F, et al. Demystifying signal processing techniques to extract task- related EEG responses for psychologists. Brain Science Advances, 2020, 6(3): 171-188. https://doi.org/10.26599/BSA.2020.9050018

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Received: 01 June 2020
Revised: 24 June 2020
Accepted: 05 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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