Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study
Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study
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DOI：
10.1155/2020/9152369
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发表时间：
2020-05-22
影响因子：
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通讯作者：
Khan, Muhammad Jawad
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文献类型：
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作者：
Khan, Rayyan Azam;Naseer, Noman;Khan, Muhammad Jawad
功能性近红外光谱（fNIRS）是最新的无创脑功能测量技术之一，已用于脑机接口（BCI）的目的。在本文中，我们比较和分析了六种最常用的滤波技术（即高斯、巴特沃斯、卡尔曼、血流动力学响应滤波器（hrf）、维纳和有限脉冲响应）对 fNIRS-BCI 分类精度的影响。为了得出针对特定皮质区域的特定皮质任务的最佳过滤器，我们根据三个主要皮质区域划分了实验任务：前额皮质、运动皮质和视觉皮质。针对前额叶和运动执行任务进行了三项不同的实验，而一项针对视觉刺激进行了实验。前额叶执行的任务包括休息 (R) 与心算 (MA)、R 与物体旋转 (OB) 以及 OB 与 MA。同样，对于电机执行，R 与左手指敲击 (LFT)、R 与右手指敲击 (RFT) 以及 LFT 与 RFT。同样，对于视觉皮层，R 与视觉刺激 (VS) 任务。这些实验针对五名受试者进行了十次试验。为了保持提取数据的一致性，使用氧合血红蛋白评估了六个统计特征，即斜率、平均值、峰值、峰度、偏度和方差。这六个特征的组合用于通过非线性支持向量机（SVM）对数据进行分类。对于 R 与 MA、R 与 OB、R 与 RFT、R 与 VS 以及 OB 与 MA 的维纳滤波器，使用 hrf 和高斯从 SVM 获得的分类精度显着更高。同样，对于 R 与 LFT 以及 LFT 与 RFT，发现 hrf 显着
Functional near-infrared spectroscopy (fNIRS) is one of the latest noninvasive brain function measuring technique that has been used for the purpose of brain-computer interfacing (BCI). In this paper, we compare and analyze the effect of six most commonly used filtering techniques (i.e., Gaussian, Butterworth, Kalman, hemodynamic response filter (hrf), Wiener, and finite impulse response) on classification accuracies of fNIRS-BCI. To conclude with the best optimal filter for a specific cortical task owing to a specific cortical region, we divided our experimental tasks according to the three main cortical regions: prefrontal, motor, and visual cortex. Three different experiments were performed for prefrontal and motor execution tasks while one for visual stimuli. The tasks performed for prefrontal include rest (R) vs mental arithmetic (MA), R vs object rotation (OB), and OB vs MA. Similarly, for motor execution, R vs left finger tapping (LFT), R vs right finger tapping (RFT), and LFT vs RFT. Likewise, for the visual cortex, R vs visual stimuli (VS) task. These experiments were performed for ten trials with five subjects. For consistency among extracted data, six statistical features were evaluated using oxygenated hemoglobin, namely, slope, mean, peak, kurtosis, skewness, and variance. Combination of these six features was used to classify data by the nonlinear support vector machine (SVM). The classification accuracies obtained from SVM by using hrf and Gaussian were significantly higher for R vs MA, R vs OB, R vs RFT, and R vs VS and Wiener filter for OB vs MA. Similarly, for R vs LFT and LFT vs RFT, hrf was found to be significantp