IEEE Trans Neural Syst Rehabil Eng. 2023;31:4225-4234. doi: 10.1109/TNSRE.2023.3326065. Epub 2023 Oct 27.
Motor unit (MU) discharge timings encode human motor intentions to the finest degree. Whilst tapping into such information can bring significant gains to a range of applications, current approaches to MU decoding from surface signals do not scale well with the demands of dexterous human-machine interfacing (HMI). To optimize the forward estimation accuracy and time-efficiency of such systems, we propose the inclusion of task-wise initialization and MU subset selection.
Offline analyses were conducted on data recorded from 11 non-disabled subjects. Task-wise decomposition was applied to identify MUs from high-density surface electromyography (HD-sEMG) pertaining to 18 wrist/forearm motor tasks. The activities of a selected subset of MUs were extracted from test data and used for forward estimation of intended motor tasks and joint kinematics. To that end, various combinations of subset selection and estimation algorithms (both regression and classification-based) were tested for a range of subset sizes.
The mutual information-based minimum Redundancy Maximum Relevance (mRMR-MI) criterion retained MUs with the highest predicative power. When the portion of tracked MUs was reduced down to 25%, the regression performance decreased only by 3% (R2=0.79) while classification accuracy dropped by 2.7% (accuracy = 74%) when kernel-based estimators were considered.
Careful selection of tracked MUs can optimize the efficiency of MU-driven interfacing. In particular, prioritization of MUs exhibiting strong nonlinear relationships with target motions is best leveraged by kernel-based estimators. Hence, this frees resources for more robust and adaptive MU decoding techniques to be implemented in future.
运动单位 (MU) 的放电时序能以最细微的程度对人类运动意图进行编码。虽然利用这些信息可以为各种应用带来显著的收益，但目前从表面信号解码 MU 的方法并不能很好地满足灵巧人机交互 (HMI) 的需求。为了优化这些系统的正向估计准确性和时间效率，我们建议纳入任务特定的初始化和 MU 子集选择。
对 11 名非残疾受试者记录的数据进行离线分析。任务分解用于从高分辨率表面肌电图 (HD-sEMG) 中识别与 18 个腕/前臂运动任务相关的 MU。从测试数据中提取选定 MU 子集的活动，并用于正向估计预期的运动任务和关节运动学。为此，针对各种子集大小测试了子集选择和估计算法（回归和分类）的各种组合。
基于互信息的最小冗余最大相关性 (mRMR-MI) 准则保留了具有最高预测能力的 MU。当跟踪 MU 的比例减少到 25%时，回归性能仅下降 3%（R2=0.79），而当考虑基于核的估计器时，分类准确性下降 2.7%（准确性=74%）。
仔细选择跟踪 MU 可以优化 MU 驱动接口的效率。特别是，与目标运动具有强非线性关系的 MU 的优先级最好由基于核的估计器利用。因此，这为未来实施更稳健和自适应的 MU 解码技术释放了资源。
