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Demystifying signal processing techniques to extract task-related EEG responses for psychologists
Author(s) -
Libo Zhang,
Zhenjiang Li,
Fengrui Zhang,
Ruolei Gu,
Weiwei Peng,
Li Hu
Publication year - 2020
Publication title -
brain science advances
Language(s) - English
Resource type - Journals
ISSN - 2096-5958
DOI - 10.26599/bsa.2020.9050018
Subject(s) - electroencephalography , task (project management) , preprocessor , computer science , eeg fmri , identification (biology) , artificial intelligence , resting state fmri , pattern recognition (psychology) , speech recognition , psychology , cognitive psychology , neuroscience , engineering , botany , systems engineering , biology
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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