Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information
Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information
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DOI：
10.1142/s0129065721500106
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发表时间：
2021-02
影响因子：
8
通讯作者：
Yang Zheng;Xiaogang Hu
中科院分区：
文献类型：
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作者：
Yang Zheng;Xiaogang Hu
可靠的神经机器接口对于人类在不受约束的环境中直观地与先进的机器人手交互至关重要。现有的神经解码方法利用基于离散手势的模式识别或一次用一根手指进行连续力解码。我们开发了一种神经解码技术，可以根据脊髓运动神经元放电信息连续并发地预测不同手指的力量。记录了手指伸肌的高密度皮肤表面肌电图（HD-EMG）信号，而人类参与者以灵巧的方式产生等长屈曲力（即同时使用单个手指或多个手指产生不同的力）。使用盲源分离技术从肌电图信号中提取运动神经元放电信息，并且每个识别的神经元被进一步分类为与给定的手指相关。然后利用各个手指相应的运动神经元池放电频率同时预测各个手指的力。与传统方法相比，我们的技术带来了更好的预测性能，即更高的相关性（[公式：查看文本]与[公式：查看文本]）、较低的预测误差（[公式：查看文本]% MVC 与[公式：查看文本]% MVC）以及手指状态（静止/活动）预测的更高准确度（[公式：查看文本]% 与[公式：查看文本]%）。我们的解码方法证明了对不同手指的运动神经元进行分类的可能性，这显着减轻了相邻手部肌肉肌电图记录的串扰问题，并允许单独和同时解码手指力。结果提供了一个强大的神经机器接口，可以让用户以灵巧的方式直观地控制机器人手。
A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.