IEEE Trans Neural Syst Rehabil Eng. 2018 Jan;26(1):244-251. doi: 10.1109/TNSRE.2017.2766360.
We prove the feasibility of decomposing high density surface EMG signals from forearm muscles in non-isometric wrist motor tasks of normally limbed and limb-deficient individuals with the perspective of using the decoded neural information for prosthesis control. For this purpose, we recorded surface EMG signals during motions of three degrees of freedom of the wrist in seven normally limbed subjects and two patients with limb deficiency. The signals were decomposed into individual motor unit activity with a convolutive blind source separation algorithm. On average, for each subject, 16 ± 7 motor units were identified per motor task. The discharge timings of these motor units were estimated with an accuracy > 85%. Moreover, the activity of 6 ± 5 motor units per motor task was consistently detected in all repetitions of the same task. The joint angle at which motor units were first identified was 62.5 ± 26.4% of the range of motion, indicating a prevalence in the identification of high threshold motor units. These findings prove the feasibility of accurate identification of the neural drive to muscles in contractions relevant for myoelectric control, allowing the development of a new generation of myocontrol methods based on motor unit spike trains.
我们证明了在非等长腕部运动任务中，从正常肢体和肢体残缺个体的前臂肌肉中分解高密度表面肌电信号的可行性，其目的是利用解码后的神经信息来控制假肢。为此，我们在 7 名正常肢体和 2 名肢体残缺个体的 3 个自由度腕部运动中记录了表面肌电信号。使用卷积盲源分离算法将信号分解为个体运动单元活动。平均而言，对于每个运动任务，每个个体可识别 16 ± 7 个运动单元。这些运动单元的放电时间估计精度>85%。此外，在同一任务的所有重复中，都能一致检测到 6 ± 5 个运动单元的活动。首次识别运动单元的关节角度为运动范围的 62.5 ± 26.4%，表明高阈值运动单元的识别较为普遍。这些发现证明了在与肌电控制相关的收缩中准确识别肌肉神经驱动的可行性，为基于运动单元尖峰序列的新一代肌电控制方法的发展奠定了基础。
