Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature – deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.

中文翻译：

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具有多个fNIRS功能的统一分析框架，用于前额叶皮层中的心理工作量评估

了解心理工作量的实际水平对于确保基于脑机接口（BCI）的认知训练的有效性非常重要。从大脑区域的有限区域提取信号可能不会揭示实际信息。在这项研究中，配备了具有多通道和多距离测量功能的功能性近红外光谱（fNIRS）设备用于开发分析框架以评估前额叶皮层（PFC）的心理工作量。除了诸如血流动力学斜率之类的常规功能外，我们还引入了一项新功能-深度贡献率，即脑血流动力学与fNIRS信号的比例。一个简单的逻辑运算符检查了多组功能，以抑制识别激活通道时的错误检测率。使用激活通道的数量作为线性支持向量机（SVM）的输入，在对心理工作量的三个级别进行分类时评估了所提出的分析框架的性能。最好的功能集包括血流动力学斜率和深部贡献率的组合，与单个传统功能相比，已识别的激活通道数在预测精神负荷方面的平均准确率达到80.6％（准确度为59.8％）。这表明所提出的具有多种功能的分析框架的可行性，作为在基于fNIRS的BCI应用程序中更准确地评估心理工作量的一种手段。最好的功能集包括血流动力学斜率和深部贡献率的组合，与单个传统功能相比，已识别的激活通道数在预测精神负荷方面的平均准确率达到80.6％（准确度为59.8％）。这表明所提出的具有多种功能的分析框架的可行性，作为在基于fNIRS的BCI应用程序中更准确地评估心理工作量的一种手段。最好的功能集包括血流动力学斜率和深部贡献率的组合，与单个传统功能相比，已识别的激活通道数在预测精神负荷方面的平均准确率达到80.6％（准确度为59.8％）。这表明所提出的具有多种功能的分析框架的可行性，作为在基于fNIRS的BCI应用程序中更准确地评估心理工作量的一种手段。