A Novel Framework Based on FastICA for High Density Surface EMG Decomposition.
A Novel Framework Based on FastICA for High Density Surface EMG Decomposition.
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一种基于 FastICA 的高密度表面肌电分解新框架。
DOI：
10.1109/tnsre.2015.2412038
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发表时间：
2016-01
期刊：
影响因子：
--
通讯作者：
Zhou P
中科院分区：
文献类型：
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作者：
Chen M;Zhou P
本研究提出了一个渐进式FastICA剥离（PFP）框架，用于高密度表面肌电图（EMG）分解。新的框架是基于一个平移不变的模型来描述表面肌电信号。分解过程可以被看作是逐步扩大运动单位尖峰序列的集合，这主要是基于FastICA。为了克服FastICA的局部收敛，使用“剥离”策略（即从前一步骤中去除估计的运动单位动作电位（MUAP）序列）来减轻已经识别的运动单位的影响，因此可以提取更多的运动单位。此外，约束FastICA应用于评估提取的尖峰序列，并纠正可能的错误或遗漏的尖峰。这些过程一起工作，以提高分解性能。使用不同运动单元数（30，70，91）和信噪比（SNR）（20，10，0 dB）的模拟表面肌电信号对所提出的框架进行了验证。结果表明，提取的运动单位数量相对较大，准确性较高（F1分数较高）。该框架还测试了111次试验的64通道电极阵列实验表面肌电信号在第一背侧骨间（FDI）肌肉收缩在不同强度。平均14.1 ± 5.0个运动单位，从实验表面肌电信号的每次试验中识别。
This study presents a progressive FastICA peel-off (PFP) framework for high density surface electromyogram (EMG) decomposition. The novel framework is based on a shift-invariant model for describing surface EMG. The decomposition process can be viewed as progressively expanding the set of motor unit spike trains, which is primarily based on FastICA. To overcome the local convergence of FastICA, a “peel off” strategy (i.e. removal of the estimated motor unit action potential (MUAP) trains from the previous step) is used to mitigate the effects of the already identified motor units, so more motor units can be extracted. Moreover, a constrained FastICA is applied to assess the extracted spike trains and correct possible erroneous or missed spikes. These procedures work together to improve the decomposition performance. The proposed framework was validated using simulated surface EMG signals with different motor unit numbers (30, 70, 91) and signal to noise ratios (SNRs) (20, 10, 0 dB). The results demonstrated relatively large numbers of extracted motor units and high accuracies (high F1-scores). The framework was also tested with 111 trials of 64-channel electrode array experimental surface EMG signals during the first dorsal interosseous (FDI) muscle contraction at different intensities. On average 14.1 ± 5.0 motor units were identified from each trial of experimental surface EMG signals.