脑电神经反馈用于提高工作记忆的研究进展&#x0002A;

周文斌; 南文雅; 伏云发

doi:10.16719/j.cnki.1671-6981.20240301

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心理科学 ›› 2024, Vol. 47 ›› Issue (3) : 514-521. DOI: 10.16719/j.cnki.1671-6981.20240301

基础、实验与工效

脑电神经反馈用于提高工作记忆的研究进展^*

周文斌¹, 南文雅^**1, 伏云发^**2

作者信息 +

EEG Neurofeedback for Working Memory Enhancement: A Literature Review

Zhou Wenbin¹, Nan Wenya¹, Fu Yunfa²

Author information +

文章历史 +

摘要

工作记忆在许多复杂的认知活动中起着重要的作用,如何提高个体的工作记忆能力一直是研究的热点问题。脑电神经反馈利用操作性条件反射原理,将大脑活动实时反馈给个体,使得个体学会自主调节大脑活动,从而改善其认知和行为表现,已被广泛用于提高临床群体和健康群体的工作记忆。然而,由于现有研究在实验设计、训练方案、被试人群、样本量等方面并不相同,结论也并不完全一致,仍然缺少系统的总结和展望。文章重点评述不同的脑电神经反馈方案对工作记忆的作用结果,并指出现存的问题和可能的解决思路,以期为未来研究提供参考。

Abstract

Working memory refers to the ability to maintain and manipulate information over a period of seconds. In daily life, many complex cognitive activities such as learning and decision-making need the participation of working memory. Whether working memory performance can be improved by certain ways of training has been a hot research topic.
Neurofeedback (NF) is a type of biofeedback that uses the principle of operational conditioning to enable individuals to learn regulating their own brain activity. During electroencephalogram (EEG) NF training, the EEG signals are recorded from single or multiple electrodes attached on the scalp and relevant features are extracted and presented to the training individuals in real time by visual, auditory, or combined visual-auditory forms. Thus, participants can be aware of their brain state in real time. When their brain activity meets some predefined rewarded criteria, they will be rewarded by the feedback interface that presents real time feedback feature, such as increasing the sphere size in visual feedback, music quality in auditory feedback, etc. With NF training practice, they will learn how to adjust their brain activities that underlie a specific behavior or pathology.
A large amount of studies have shown that NF training can improve cognitive ability and behavioral performance in both clinical patients and healthy population. Regarding the NF training effectiveness for working memory enhancement, the existing research conclusions are not consistent due to the variations of the experimental design, training protocol, participants’ population, and sample size in the literature. Therefore, this study systematically reviewed previous studies on EEG NF training for working memory performance improvement. It started with the principle and mechanism of NF training and the introduction of the current research progress. Then the article reviewed the experimental results using different NF training protocols including theta enhancement NF, alpha enhancement NF, SMR enhancement NF, beta enhancement NF, gamma enhancement NF and two frequency bands NF protocols. We found that alpha, SMR and theta enhancement NF have shown the benefits on working memory enhancement in most studies. However, a few studies have reported inconsistent results, including the failure to adjust the training EEG feature (i.e. the non-learner problem) and no significant enhancement in working memory compared to the control group.
Future research can be conducted from following three aspects. First, the neural mechanism of EEG NF training effects on working memory has not been clear yet. Previous work only examined the EEG activity during NF training and resting periods. Whether and how NF training influences the brain activity in working memory task and results in working memory performance change remains unknown yet. Future work can utilize a variety of imaging methods such as EEG, functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS) and positron emission tomography (PET) to examine the brain activities during NF training, during resting state and during working memory task. Second, the non-learner problem has been reported in a number of studies. Although a few studies have identified some physiological and psychological predictors for non-learners in some NF protocols, the findings cannot be generalized due to the complexity of EEG activity, the variety of participants’ population and inconsistent experimental design. Future work is suggested to utilize machine learning methods to identify the predictors of non-learners in different NF training protocols in order to understand the reason of non-learner problem, and save time and effort on non-learners. Finally, the optimization of training parameters including training schedule and feedback interface, the adoption of randomized double-blind sham-controlled experimental design, clear reporting the experimental methods and results are desired in future NF studies. This review is expected to provide reference and pave the way for future research.

导出引用

周文斌, 南文雅, 伏云发. 脑电神经反馈用于提高工作记忆的研究进展^*[J]. 心理科学. 2024, 47(3): 514-521 https://doi.org/10.16719/j.cnki.1671-6981.20240301

Zhou Wenbin, Nan Wenya, Fu Yunfa. EEG Neurofeedback for Working Memory Enhancement: A Literature Review[J]. Journal of Psychological Science. 2024, 47(3): 514-521 https://doi.org/10.16719/j.cnki.1671-6981.20240301

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