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基于模糊相似性度量的软边界神经反馈训练:一种在神经反馈训练期间学习如何控制脑电信号特征的方法。

Soft boundary-based neurofeedback training based on fuzzy similarity measures: A method for learning how to control EEG Signal features during neurofeedback training.

作者信息

Sho'ouri Nasrin

机构信息

Faculty of Technology and Engineering, Central Tehran branch, Islamic Azad University, Tehran, Iran.

出版信息

J Neurosci Methods. 2020 Sep 1;343:108805. doi: 10.1016/j.jneumeth.2020.108805. Epub 2020 Jun 13.

DOI:10.1016/j.jneumeth.2020.108805
PMID:32544535
Abstract

BACKGROUND

Most commonly used neurofeedback training (NFT) methods are able to assist subjects towards an increase/decrease in EEG features. So, it is possible that the enhancement/inhabitation in a subject's EEG features exceed normal limits if the process of changes in brain activity in the subject is very successful. This issue may also bring about a reduction in the effectiveness of NFT.

NEW METHOD

A soft boundary-based NFT method was proposed for learning how to control the EEG features during training. According to this method, an initial group was defined within which the training features of subjects' EEG signals were placed prior to training and a target group was considered referring to what the features of the EEG signals should be shifted towards during training. In the course of training, the fuzzy similarity of EEG features of subject towards the target group center was measured and the subject's score was increased if their fuzzy similarity was higher than a threshold. Within this method, an adaptive scoring index (the scores assigned to subjects for each achievement) was defined whose value was determined according to brain activity of the subject.

RESULTS

Increase/decrease in large amounts in the training features of subject's EEG could lead to a descending trend in the scores received using the proposed method.

COMPARISON WITH EXISTING METHODS

The proposed method may assist subjects to control their EEG signal features within the target group range.

CONCLUSION

The proposed method may be able to prevent the side effects of neurofeedback.

摘要

背景

最常用的神经反馈训练(NFT)方法能够帮助受试者增加/减少脑电图特征。因此,如果受试者大脑活动的变化过程非常成功,其脑电图特征的增强/抑制可能会超出正常范围。这个问题也可能导致NFT有效性的降低。

新方法

提出了一种基于软边界的NFT方法,用于学习如何在训练过程中控制脑电图特征。根据该方法,定义了一个初始组,在训练前将受试者脑电图信号的训练特征置于该组内,并考虑一个目标组,该目标组指的是训练期间脑电图信号特征应朝着的方向。在训练过程中,测量受试者脑电图特征与目标组中心的模糊相似度,如果其模糊相似度高于阈值,则提高受试者的分数。在该方法中,定义了一个自适应评分指数(为受试者的每项成就分配的分数),其值根据受试者的大脑活动确定。

结果

受试者脑电图训练特征的大量增加/减少可能导致使用所提出方法获得的分数呈下降趋势。

与现有方法的比较

所提出的方法可以帮助受试者在目标组范围内控制其脑电图信号特征。

结论

所提出的方法可能能够预防神经反馈的副作用。

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