Motor Command Detection for a Repetitive Facilitation Exercise Assistance System
Motor Command Detection for a Repetitive Facilitation Exercise Assistance System
复制标题
重复促进练习辅助系统的运动命令检测
DOI：
10.1109/rcar.2017.8311872
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Masakatsu G. Fujie
中科院分区：
文献类型：
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作者：
Satoshi Miura;Junichi Takazawa;Yo Kobayashi;Masakatsu G. Fujie
本文提出了一种辅助重复促进训练的脑机接口系统的可行性研究。重复促进训练是偏瘫患者的一种有效的康复方法。在重复的促进练习中，治疗师刺激患者瘫痪的部分，同时运动指令沿着神经通路运行。然而，成功的重复促进练习是很难实现的，即使是熟练的从业者也无法检测到患者大脑中何时出现运动指令。我们提出了一种脑机接口系统，用于自动检测运动指令并刺激患者的瘫痪部分。为了从患者的脑电数据中确定运动指令，我们构建了一个支持向量机系统。在本文中，我们验证了使用支持向量机的脑电对运动指令的识别率高于由治疗师进行的重复促进练习的成功率。在实验中，当参与者被提示弯肘时，我们测量了他们的脑电。我们使用交叉验证法对脑电数据进行分析。我们发现，每个参与者的辨别率至少为69%，高于治疗师进行重复促进练习的成功率。实验结果表明，基于支持向量机的脑电信号对检测运动指令具有一定的实用价值。
This paper presents a feasibility study of a brain-machine interface system to assist repetitive facilitation exercise. Repetitive facilitation exercise is an effective rehabilitation method for patients with hemiplegia. In repetitive facilitation exercise, a therapist stimulates the paralyzed part of the patient while motor commands run along the nerve pathway. However, successful repetitive facilitation exercise is difficult to achieve and even a skilled practitioner cannot detect when a motor command occurs in patient's brain. We proposed a brain-machine interface system for automatically detecting motor commands and stimulating the paralyzed part of a patient. To determine motor commands from patient electroencephalogram (EEG) data, we constructed a support vector machine (SVM) system. In this paper, we validated that the discrimination ratio of the motor command by EEG using SVM was higher than the success rate of the repetitive facilitation exercise administered by a therapist. In the experiments, we measured the EEG when the participant bent their elbow when prompted to do so. We analyzed the EEG data using a cross-validation method. We found that the discrimination ratio for each participant was at least 69%, which is above the success rate for repetitive facilitation exercise administered by a therapist. We conclude that the EEG using SVM is useful for detecting motor commands.