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Lower limb Movements' Classifications using Hemodynamic Response:fNIRS Study

机译:使用血液动力学反应的肢体运动的分类:FNIRS研究

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Functional near-infrared spectroscopy (fNIRS) has become a viable approach for brain function investigation and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and resistance to electromagnetic noise. In this work, a hemodynamic response based on fNIRS signals was utilized to classify the right and left ankle joint movements. To achieve this objective, 32 optodes (emitters and detectors) were used to measure the hemodynamic responses in the motor cortex area during the motor execution task of the ankle joint movements. Two-channel sets were formed one including the channels directly related to the movement task, and another including all of the proposed channels. The results of this study reveal that the scheme based only on the selected channels outperformed the scheme that uses all channels. The classification accuracies were 91.38 % and 89.86 % respectively. These results demonstrated that fNIRS signal classification can be enhanced by eliminating the redundant channels.
机译:功能近红外光谱(FNIR)已成为脑功能调查的可行方法,是脑机接口(BMI)的有趣方式,由于其可移植性和对电磁噪声的抵抗力。在这项工作中,利用基于FNIR信号的血流动力学响应来分类右侧和左脚踝关节运动。为了实现这一目标,使用32个光电(发射器和探测器)来测量踝关节运动的电动机执行任务期间电动机皮层区域中的血流动力学响应。形成一个包括与运动任务直接相关的通道的双通道组,另一个包括所有提出的通道。本研究的结果表明,仅基于所选通道的方案优于使用所有通道的方案。分类准确性分别为91.38%和89.86%。这些结果表明,通过消除冗余通道,可以增强FNIRS信号分类。

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