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首页> 外文期刊>Frontiers in Neurorobotics >Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients
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Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients

机译：使用通用线性模型系数的自适应估计，增强功能近红外光谱-脑-计算机接口的分类性能

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摘要

In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain–computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05). These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.
机译：在本文中，一种用于增强功能近红外光谱（fNIRS）信号分类的新方法论可用于两类[运动图像（MI）和静止图像;提出了“精神旋转（MR）和休息”的脑机接口（BCI）。首先，分别从运动皮层和前额叶皮层获取与MI和MR相对应的fNIRS信号，然后进行滤波以去除生理噪声。然后，使用通用线性模型对信号进行建模，并使用最小二乘技术自适应地估计其系数。随后，将估计系数的多个特征组合用于分类。使用支持向量机，对五名受试者而言，心梗与静息的最佳分类准确度分别为79.5％，83.7、82.6、81.4和84.1％，而MR与静息相对的分别为85.5、85.2、87.8、83.7和84.8％ 。将这些结果与使用常规血液动力学响应获得的最佳分类精度进行比较。通过提出的方法，获得的平均分类准确率明显更高（p <0.05）。这些结果证明了开发具有高分类性能的fNIRS-BCI的可行性。

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