Multi-class Classification of Motor Execution Tasks using fNIRS
Multi-class Classification of Motor Execution Tasks using fNIRS
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DOI：
10.1109/spmb47826.2019.9037856
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
F. Shamsi;L. Najafizadeh
中科院分区：
文献类型：
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作者：
F. Shamsi;L. Najafizadeh
本文研究了多类运动执行任务的分类问题，通过功能近红外光谱（fNIRS）获得的信号。fNIRS数据是从五名健康受试者中获取的，同时执行四种类型的运动执行任务以及非运动任务（总共五类）。基于在[0 - 2]、[1 - 3]和[2 - 4]秒间隔内计算的氧合血红蛋白（[ΔHbO]）信号浓度变化的平均值提取各种特征集。一个多类支持向量机分类器与二次多项式核（QSVM）是用来分类运动和非运动类（共5类）使用的数据从三个时间间隔。分类结果表明，使用[2 - 4]秒间隔的数据获得的平均准确率高于其他两个（78.55%）。此外，仅从运动皮层与从大脑的多个区域获得的数据的分类结果之间的比较完成。我们的研究结果表明，通过使用来自大脑不同区域的fNIRS数据，与仅使用来自运动区域的数据的情况相比，分类精度提高了10 - 12%。
This paper investigates the problem of classification of multi-class movement execution tasks from signals obtained via functional near infrared spectroscopy (fNIRS). fNIRS data is acquired from five healthy subjects while performing four types of motor execution tasks as well as a non-movement task (five classes in total). Various feature sets are extracted based on the mean of changes in the concentration of oxygenated hemoglobin ([ΔHbO]) signals computed across the [0 – 2], [1 – 3], and [2 – 4] sec intervals. A multi-class support vector machine classifier with a quadratic polynomial kernel (QSVM) is utilized to classify movement and non-movement classes (total of 5 classes) using the data from the three time intervals. Classification results revealed that the average accuracy obtained for data using [2 – 4] sec interval is higher than the other two (78.55%). In addition, a comparison between the classification results of the data obtained from only the motor cortex vs from multiple regions of the brain is done. Our results demonstrate that by using fNIRS data from different regions of the brain, the classification accuracy is improved by 10 – 12% as compared to the case when the data is used only from the motor region.