Background

Within the most commonly used neurofeedback training methods, a threshold has been defined for each EEG feature wherein subjects’ status during training can be assessed according to the given value. In the present study, a neurofeedback training method based on feature-space clustering was proposed in order to assess subjects’ status more accurately.

New method

Neural gas algorithm was employed for feature space clustering. Then, the clusters were labeled as initial clusters (where the EEG features were placed prior to training) and target (where the EEG features should be shifted towards during training) ones. A scoring index was defined whose value was determined according to subjects’ brain activity. This method was simulated in two versions: soft-boundary and hard-boundary based methods.

Results

The results of the present simulation showed that the proposed hard-boundary based version could guide the subjects towards the boundaries of the target clusters and even their status would be stabilized in case of too many changes in subjects’ EEG features. In the proposed soft-boundary based version, in case of too many changes in training features, the subjects would not be encouraged and they could be guided towards the target boundaries.

Conclusion

The proposed hard-boundary based version could be effective in guiding a subject towards being placed within the boundaries of target clusters and even beyond them if no specific limits exited for EEG features. As well, the soft-boundary based version could be useful when controlling EEG features within a limit.

中文翻译：

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一种基于特征空间聚类的神经反馈训练新方法控制目标簇内的脑电特征

背景

在最常用的神经反馈训练方法中，已经为每个 EEG 特征定义了一个阈值，其中可以根据给定的值评估受试者在训练期间的状态。在本研究中，为了更准确地评估受试者的状态，提出了一种基于特征空间聚类的神经反馈训练方法。

新方法

特征空间聚类采用神经气体算法。然后，簇被标记为初始簇（在训练之前放置 EEG 特征的位置）和目标（在训练期间应将 EEG 特征转移到的位置）。定义了一个评分指数，其值根据受试者的大脑活动确定。该方法模拟了两个版本：软边界和基于硬边界的方法。

结果

目前的模拟结果表明，所提出的基于硬边界的版本可以引导受试者朝向目标簇的边界，甚至在受试者脑电图特征变化过多的情况下，他们的状态也会稳定。在提出的基于软边界的版本中，如果训练特征发生太多变化，则不会鼓励受试者，他们可以被引导到目标边界。

结论

如果 EEG 特征没有特定的限制，则所提出的基于硬边界的版本可以有效地引导受试者置于目标簇的边界内，甚至超出目标簇的边界。同样，在限制范围内控制 EEG 特征时，基于软边界的版本可能很有用。