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首页> 外文会议>International Conference on Artificial Intelligence and Big Data >Multi-classification of fNIRS Signals in Four body parts Motor Imagery Tasks Measured From Motor Cortex
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Multi-classification of fNIRS Signals in Four body parts Motor Imagery Tasks Measured From Motor Cortex

机译：来自电机皮质测量的四个身体部位电动机图像的FNIR信号的多分类

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Functional near-infrared spectroscopy (fNIRS) which is known as a new brain imaging technology has been widely used in brain-computer interface (BCI) because of its convenience and anti-interference capability. Many studies concentrate on two-category motor imagery (MI) tasks classification (left hand and right hand) or four directions (up, down, left and right) measured from prefrontal cortex. In this study, we have designed the experimental paradigms to collect fNIRS data from 10 subjects when they execute four-category MI tasks (left hand, right hand, feet and tongue) measured from motor cortex. We analyze the fNIRS signals by extracting dynamic features under three different time windows and used machine learning method to build Support vector machine (SVM), K-Nearest Neighbor (KNN) and AdaBoost (Ada) classifiers to classify. The results show that the average accuracy of ten subjects was up to 48.5% and for single subject, the highest accuracy was up to 60.62%. Moreover, average hemodynamics responses of each subject measured from motor cortex that reflect different parts of motor cortex were activated when subjects executed different types of MI tasks. To our knowledge, the accuracies we get are higher than the previous research which involved four-direction MI tasks measured from prefrontal cortex. Our results prove that classification of MI tasks based on motor cortex were more effective than that based on prefrontal cortex, which can promote the development of fNIRS-based BCI.
机译：由于其便利性和抗干扰能力，所谓的新脑成像技术被称为新的脑成像技术的功能近红外光谱（FNIR）已被广泛应用于脑电脑界面（BCI）。许多研究专注于两类电动机图像（MI）任务分类（左手和右手）或从前额叶皮质测量的四个方向（上，向下，左右）。在这项研究中，我们设计了实验范式，以便在从电动机皮层测量的四类MI任务（左手，右手，脚和舌头）时从10个受试者收集FNIRS数据。我们通过在三个不同时间窗口和使用的机器学习方法下提取动态特性来分析FNIRS信号，以构建支持向量机（SVM），K-CORMBED邻居（KNN）和ADABOOST（ADA）分类器来分类。结果表明，十个受试者的平均准确性高达48.5％，最高精度高达60.62％。此外，当受试者执行不同类型的MI任务时，激活了从电动机皮层测量的每个受试者的平均血液动力学响应。为了我们的知识，我们得到的准确性高于前面的研究，涉及从预前期皮质测量的四维MI任务。我们的结果证明，基于电机皮质的MI任务分类比基于前额叶皮层的促进，这可以促进基于FNIRS的BCI的发展。

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