为心理学家揭开提取任务相关脑电图反应的信号处理技术的神秘面纱

Libo Zhang, Zhenjiang Li, Fengrui Zhang, Ruolei Gu, W. Peng, Li Hu
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引用次数: 17

摘要

为了利用脑电图(EEG)研究人类心理的神经机制,我们通常指示参与者执行特定的任务,同时记录他们的大脑活动。任务相关脑电图反应的识别需要数据分析技术,这些技术通常不同于分析静息状态脑电图的方法。本综述旨在为心理学家揭开常用的信号处理方法的神秘面纱,以识别与任务相关的脑电图活动。为了实现这一目标,我们首先强调了任务相关脑电图和静息状态脑电图之间不同的预处理管道。然后,我们讨论了在时域中提取和可视化事件相关电位以及在时频域中提取和可视化事件相关振荡响应的方法。简要讨论了源分析和单次试验分析等先进技术的潜在应用。我们总结了与任务相关的脑电图数据分析、进一步研究的建议以及我们应该注意的注意事项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Demystifying signal processing techniques to extract task-related EEG responses for psychologists
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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