Objective: Real-time intramuscular electromyography (iEMG) decomposition, which is needed in biofeedback studies and interfacing applications, is a complex procedure that involves identifying the motor neuron spike trains from a streaming iEMG recording. Methods: We have previously proposed a sequential decomposition algorithm based on a Hidden Markov Model of EMG, which used Bayesian filter to estimate unknown parameters of motor unit (MU) spike trains, as well as their action potentials (MUAPs). Here, we present a modification of this original model in order to achieve a real-time performance of the algorithm as well as a parallel computation implementation of the algorithm on Graphics Processing Unit (GPU). Specifically, the Kalman filter previously used to estimate the MUAPs, is replaced by a least-mean-square filter. Additionally, we introduce a number of heuristics that help to omit the most improbable decomposition scenarios while searching for the best solution. Then, a GPU-implementation of the proposed algorithm is presented. Results: Simulated iEMG signals containing up to 10 active MUs, as well as five experimental fine-wire iEMG signals acquired from the tibialis anterior muscle, were decomposed in real time. The accuracy of decompositions depended on the level of muscle activation, but in all cases exceeded 85${\%}$. Conclusion: The proposed method and implementation provide an accurate, real-time interface with spinal motor neurons. Significance: The presented real time implementation of the decomposition algorithm substantially broadens the domain of its application.

中文翻译：

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使用 GPU 实现的贝叶斯滤波对肌内 EMG 信号进行在线递归分解

目标：生物反馈研究和接口应用中需要实时肌内肌电图 (iEMG) 分解，这是一个复杂的过程，涉及从流式 iEMG 记录中识别运动神经元尖峰序列。方法：我们之前提出了一种基于 EMG 隐马尔可夫模型的顺序分解算法，该算法使用贝叶斯滤波器来估计运动单元 (MU) 尖峰序列的未知参数，以及它们的动作电位 (MUAP)。在这里，我们提出了对该原始模型的修改，以实现算法的实时性能以及该算法在图形处理单元 (GPU) 上的并行计算实现。具体来说，之前用于估计 MUAP 的卡尔曼滤波器被最小均方滤波器取代。此外，我们引入了一些启发式方法，它们有助于在寻找最佳解决方案时忽略最不可能的分解场景。然后，介绍了所提出算法的 GPU 实现。结果：实时分解包含多达 10 个活动 MU 的模拟 iEMG 信号，以及从胫骨前肌获取的 5 个实验性细线 iEMG 信号。分解的准确性取决于肌肉激活的水平，但在所有情况下都超过了 85${\%}$。结论：所提出的方法和实现提供了与脊髓运动神经元的准确、实时接口。意义：所提出的分解算法的实时实现大大拓宽了其应用领域。