A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex
A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex
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DOI：
10.1109/tnsre.2020.3026991
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发表时间：
2020-11-01
影响因子：
4.9
通讯作者：
Tang, Tong Boon
中科院分区：
文献类型：
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作者：
Lim, Lam Ghai;Ung, Wei Chun;Tang, Tong Boon
了解心理负荷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无法揭示实际信息。在这项研究中，一个功能性近红外光谱（fNIRS）设备配备了多通道和多距离测量能力的分析框架，以评估在前额叶皮层（PFC）的心理负荷的发展。除了传统的功能，如血流动力学斜率，我们引入了一个新的功能-深度贡献率，这是脑血流动力学的fNIRS信号的比例。通过一个简单的逻辑运算符检查多组特征，以抑制识别激活通道的误检率。使用激活通道的数量作为线性支持向量机（SVM）的输入，所提出的分析框架的性能进行了评估，在分类三个层次的心理工作负荷。最好的一组功能包括血流动力学斜率和深度贡献率的组合，其中识别的激活通道数量在预测心理工作负荷方面的平均准确度为80.6%，而单一传统功能（准确度：59.8%）。这表明，建议的分析框架的可行性与多个功能作为一种手段，对更准确的评估心理工作负荷的fNIRS为基础的BCI应用程序。
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.