基于硬边界的神经反馈训练程序:一种改进的固定阈值法,在神经反馈训练中更准确地指导目标区域内的受试者。

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY
Nasrin Sho'ouri
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引用次数: 2

摘要

在神经反馈领域的几乎所有研究中,已经为每个训练特征定义了一个阈值,以便在训练过程中根据给定的值评估受试者的状态。本文提出了一种基于支持向量机(SVM)分类器确定决策边界的基于硬边界的神经反馈训练(HBNFT)方法,该方法根据决策边界明确被试的状态,并在进入目标区域后给予鼓励。在该方法中,同样定义了一个评分指标(SI),根据受试者在训练中的表现确定其值。结果表明,使用分类器和确定决策边界而不是使用阈值可以更成功地准确地将它们引导到目标区域,并且不需要选择确定阈值的基础。此外,与使用可变阈值的方法相比,所提出的方法可能在控制特征和防止极端变化方面更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hard Boundary-Based Neurofeedback Training Procedure: A Modified Fixed Thresholding Method for More Accurate Guidance of Subjects Within Target Areas During Neurofeedback Training.

In nearly all studies within the domain of neurofeedback, a threshold has been defined for each training feature in a way that subjects' status can be evaluated during training according to the given value. In this study, a hard boundary-based neurofeedback training (HBNFT) method based on the determination of decision boundary using support vector machine (SVM) classifier was proposed in which subjects' status were clarified considering a decision boundary and they could also be encouraged once entering a target area. In this method, a scoring index (SI) was similarly defined whose value was determined in accordance with subject performance during training. The results revealed that employing a classifier and determining a decision boundary instead of using a threshold could prove more successful in accurately guiding them towards a target area and also meet no needs to choose a basis for determining a threshold. Moreover, it was likely that the proposed method could be more efficient in controlling features and preventing extreme changes compared to those using variable thresholds.

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来源期刊
Clinical EEG and Neuroscience
Clinical EEG and Neuroscience 医学-临床神经学
CiteScore
5.20
自引率
5.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.
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