Faculty of Technology and Engineering, 201585Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Clin EEG Neurosci. 2023 May;54(3):228-237. doi: 10.1177/15500594221100159. Epub 2022 Jun 9.
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.
在神经反馈领域的几乎所有研究中，针对每个训练特征都定义了一个阈值，以便可以根据给定的值在训练期间评估受试者的状态。在这项研究中，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，其中根据决策边界明确了受试者的状态，并且一旦进入目标区域，他们也可以受到鼓励。在这种方法中，还定义了一个评分指数 (SI)，其值是根据训练期间受试者的表现确定的。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引导他们进入目标区域，并且也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，该方法在控制特征和防止极端变化方面可能更有效。
