基于运动单位放电信息的实时等距手指伸展力估计。
Real-time isometric finger extension force estimation based on motor unit discharge information.
机构信息
Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, United States of America.
出版信息
J Neural Eng. 2019 Oct 10;16(6):066006. doi: 10.1088/1741-2552/ab2c55.
OBJECTIVE
The goal of this study was to perform real-time estimation of isometric finger extension force using the discharge information of motor units (MUs).
APPROACH
A real-time electromyogram (EMG) decomposition method based on the fast independent component analysis (FastICA) algorithm was developed to extract MU discharge events from high-density (HD) EMG recordings. The decomposition was first performed offline during an initialization period, and the obtained separation matrix was then applied to new data samples in real-time. Since MU pool discharge probability reflects the neural drive to spinal motoneurons, individual finger forces were estimated based on a firing rate-force model established during the initialization, termed the neural-drive method. The conventional EMG amplitude-based method was used to estimate the forces as a comparison, termed the EMG-amplitude method. Simulated HD-EMG signals were first used to evaluate the accuracy of the real-time decomposition. Experimental EMG recordings of 5 min of isometric finger extension with pseudorandom force levels were used to assess the performance of force estimation over time.
MAIN RESULTS
The simulation results showed that the accuracy of real-time decomposition was 86%, compared with an offline accuracy of 94%. However, the real-time decomposition accuracy was stable over time. The experimental results showed that the neural-drive method had a significantly smaller root mean square error (RMSE) of the force estimation compared with the EMG-amplitude method, which was consistent across fingers. Additionally, the RMSE of the neural-drive method was stable until 230 s, while the RMSE of the EMG-amplitude method increased progressively over time.
SIGNIFICANCE
The neural-drive method on real-time finger force estimation was more accurate over time compared with the conventional EMG-amplitude method during prolonged muscle contractions. The outcomes can potentially offer a more accurate and robust neural interface technique for reliable neural-machine interactions based on MU pool discharge information.
目的
本研究旨在通过运动单位（MU）放电信息对等长手指伸展力进行实时估计。
方法
开发了一种基于快速独立成分分析（FastICA）算法的实时肌电图（EMG）分解方法，从高密度（HD）EMG 记录中提取 MU 放电事件。该分解首先在初始化期间离线执行，然后将获得的分离矩阵实时应用于新的数据样本。由于 MU 池放电概率反映了对脊髓运动神经元的神经驱动，因此根据初始化过程中建立的放电率-力模型估计单个手指的力，称为神经驱动方法。将传统的基于 EMG 幅度的方法用于估计力作为比较，称为 EMG 幅度方法。首先使用模拟的 HD-EMG 信号评估实时分解的准确性。使用 5 分钟的等长手指伸展实验的 EMG 记录来评估随时间推移的力估计性能。
主要结果
模拟结果表明，实时分解的准确性为 86%，而离线准确性为 94%。然而，实时分解的准确性随时间保持稳定。实验结果表明，与 EMG 幅度方法相比，神经驱动方法的力估计均方根误差（RMSE）明显更小，且在所有手指中均一致。此外，神经驱动方法的 RMSE 在 230 秒内保持稳定，而 EMG 幅度方法的 RMSE 随时间逐渐增加。
意义
与传统的基于 EMG 幅度的方法相比，在长时间肌肉收缩期间，神经驱动方法对实时手指力估计的准确性随时间的推移更加稳定。研究结果可能为基于 MU 池放电信息的可靠神经-机器交互提供更准确和稳健的神经接口技术。
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