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Brain Science Advances 2020, 6(3): 171-188 https://doi.org/10.26599/BSA.2020.9050018
Review Article | Open Access | Issue | Published: 04 February 2021

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

Show Author's Information Libo Zhang1,2,  Zhenjiang Li3,  Fengrui Zhang1,2,  Ruolei Gu2,4,  Weiwei Peng5,  Li Hu1,2( )
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
Keywords:
task-related EEG, preprocessing, time domain analysis, time-frequency analysis
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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Abstract Full text About this article

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.


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About this article

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

Show Author's information Libo Zhang1,2, Zhenjiang Li3, Fengrui Zhang1,2, Ruolei Gu2,4, Weiwei Peng5, Li Hu1,2( )
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

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 EEG, preprocessing, time domain analysis, time-frequency analysis

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Publication history

Received: 01 June 2020
Revised: 24 June 2020
Accepted: 05 July 2020
Published: 04 February 2021
Issue date: September 2020

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© The authors 2020

Acknowledgements

This work was supported by the National Natural Science Foundation of China (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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