Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array.
Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array.
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DOI：
10.1152/jn.00220.2021
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发表时间：
2021-12-01
影响因子：
2.5
通讯作者：
Weber, Douglas J
中科院分区：
文献类型：
--
作者：
Ting, Jordyn E;Del Vecchio, Alessandro;Weber, Douglas J
运动神经元传递有关运动意图的信息，这些信息可以被提取并解释为控制辅助装置。然而，大多数测量单个神经元放电活动的方法依赖于植入的微电极。尽管皮质内脑机接口（bci）已被证明是安全有效的，但手术的要求对广泛使用构成了障碍，可以通过使用非侵入性接口来减轻这种障碍。本研究的目的是评估从可穿戴传感器获取运动控制信号的可行性，该传感器可以检测慢性颈脊髓损伤（SCI）后瘫痪肌肉的剩余运动单位活动。尽管没有产生可观察到的手部运动，但在单个手指的尝试运动和明显的手腕和肘部运动中，可以观察到损伤水平以下的运动单位的自愿招募。运动单元亚组在屈曲或伸展阶段是协同的。通过肌电图（EMG）功率[均方根（RMS）]或运动单元放电率对个位数运动意图进行离线分类，两种情况下的中位数分类准确率均为75%。利用二值分类器对虚拟手进行了在线仿真控制，验证了实时提取和解码运动单元的可行性。在线分解算法在1.2 ms内提取运动单元，放电率预测正确数字运动的准确率为88±24%。这项研究首次展示了一种可穿戴接口，用于记录和解码运动完全性脊髓损伤患者损伤水平以下运动单元的放电率。使用可穿戴电极阵列和机器学习方法记录和解码运动性完全四肢瘫痪患者瘫痪肌肉的肌电信号和运动单元放电。即使在没有可见运动的情况下，肌电活动和运动单元放电率也具有任务特异性，从而能够对尝试的个位数运动进行准确分类。这种可穿戴系统有可能使四肢瘫痪的人通过运动意图来控制辅助设备。
Motor neurons convey information about motor intent that can be extracted and interpreted to control assistive devices. However, most methods for measuring the firing activity of single neurons rely on implanted microelectrodes. Although intracortical brain-computer interfaces (BCIs) have been shown to be safe and effective, the requirement for surgery poses a barrier to widespread use that can be mitigated by instead using noninvasive interfaces. The objective of this study was to evaluate the feasibility of deriving motor control signals from a wearable sensor that can detect residual motor unit activity in paralyzed muscles after chronic cervical spinal cord injury (SCI). Despite generating no observable hand movement, volitional recruitment of motor units below the level of injury was observed across attempted movements of individual fingers and overt wrist and elbow movements. Subgroups of motor units were coactive during flexion or extension phases of the task. Single digit movement intentions were classified offline from the electromyogram (EMG) power [root-mean-square (RMS)] or motor unit firing rates with median classification accuracies >75% in both cases. Simulated online control of a virtual hand was performed with a binary classifier to test feasibility of real-time extraction and decoding of motor units. The online decomposition algorithm extracted motor units in 1.2 ms, and the firing rates predicted the correct digit motion 88±24% of the time. This study provides the first demonstration of a wearable interface for recording and decoding firing rates of motor units below the level of injury in a person with motor complete SCI.NEW & NOTEWORTHY A wearable electrode array and machine learning methods were used to record and decode myoelectric signals and motor unit firing in paralyzed muscles of a person with motor complete tetraplegia. The myoelectric activity and motor unit firing rates were task specific, even in the absence of visible motion, enabling accurate classification of attempted single-digit movements. This wearable system has the potential to enable people with tetraplegia to control assistive devices through movement intent.