Background

A major difficulty for the asynchronous brain-computer interface (BCI) lies in the accurate recognition of the control and idle states. Although subject’s attention level was found to be different in these states, the validity of recognizing them using attention features has not been studied.

New methods

This paper proposed a novel Individualized Frequency Band based Optimized Complex Network (IFB-OCN) method to enhance the performance of discriminating the control and idle states. The IFB-OCN method extracted the attention features from a single FPz channel, selected the first three individualized frequency bands with the highest accuracies, and integrated the features of these bands for classification.

Results

The performance was evaluated using a steady-state visual evoked potential (SSVEP)-based BCI task. In the offline evaluation, the IFB-OCN method achieved the highest average accuracy of 93.5 % with the data length of 4 s, and achieved the highest information transfer rate (ITR) of 47.3 bits/min with the data length of 0.5 s. In the simulated online evaluation, the IFB-OCN method obtained a true positive rate (TPR) of 89.8 % and a true negative rate (TNR) of 86.2 %.

Comparison with existing methods

The proposed IFB-OCN method recognized the control and idle states using a single FPz channel rather than the occipital channels, and outperformed the existing algorithms in the accuracy of detecting the attention level.

Conclusions

These results demonstrate that the proposed IFB-OCN method is efficient in recognizing the idle state and has a great potential for enhancing the asynchronous BCIs.

中文翻译：

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基于用于异步脑机接口的新型IFB-OCN方法的空闲状态识别。

背景

异步脑机接口（BCI）的主要困难在于对控制状态和空闲状态的准确识别。尽管在这些状态下发现受试者的注意力水平有所不同，但尚未研究使用注意力特征识别它们的有效性。

新方法

本文提出了一种新颖的基于个性化频带的优化复杂网络（IFB-OCN）方法，以提高区分控制状态和空闲状态的性能。IFB-OCN方法从单个FPz通道中提取注意力特征，选择精度最高的前三个个性化频段，并整合这些频段的特征进行分类。

结果

使用基于稳态视觉诱发电位（SSVEP）的BCI任务评估性能。在离线评估中，IFB-OCN方法在4 s的数据长度下实现了93.5％的最高平均准确度，在0.5 s的数据长度下实现了47.3位/分钟的最高信息传输率（ITR）。在模拟的在线评估中，IFB-OCN方法获得的真阳性率（TPR）为89.8％，真阴性率（TNR）为86.2％。

与现有方法的比较

提出的IFB-OCN方法使用单个FPz通道而不是枕骨通道来识别控制状态和空闲状态，并且在检测注意力水平的准确性方面优于现有算法。

结论

这些结果表明，所提出的IFB-OCN方法在识别空闲状态方面是有效的，并且具有增强异步BCI的巨大潜力。