Gallina Alessio, Garland S Jayne, Wakeling James M
Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver V6T 1Z3, Canada.
Department of Physical Therapy, University of British Columbia, Vancouver V6T 1Z3, Canada; Faculty of Health Sciences, University of Western Ontario, London N6A 5B9, Canada.
J Electromyogr Kinesiol. 2018 Aug;41:116-123. doi: 10.1016/j.jelekin.2018.05.002. Epub 2018 May 22.
In this study, we investigated whether principal component analysis (PCA) and non-negative matrix factorization (NMF) perform similarly for the identification of regional activation within the human vastus medialis. EMG signals from 64 locations over the VM were collected from twelve participants while performing a low-force isometric knee extension. The envelope of the EMG signal of each channel was calculated by low-pass filtering (8 Hz) the monopolar EMG signal after rectification. The data matrix was factorized using PCA and NMF, and up to 5 factors were considered for each algorithm. Association between explained variance, spatial weights and temporal scores between the two algorithms were compared using Pearson correlation. For both PCA and NMF, a single factor explained approximately 70% of the variance of the signal, while two and three factors explained just over 85% or 90%. The variance explained by PCA and NMF was highly comparable (R > 0.99). Spatial weights and temporal scores extracted with non-negative reconstruction of PCA and NMF were highly associated (all p < 0.001, mean R > 0.97). Regional VM activation can be identified using high-density surface EMG and factorization algorithms. Regional activation explains up to 30% of the variance of the signal, as identified through both PCA and NMF.
在本研究中，我们调查了主成分分析（PCA）和非负矩阵分解（NMF）在识别人类股内侧肌区域激活方面的表现是否相似。在十二名参与者进行低强度等长膝关节伸展时，从股内侧肌上64个位置采集肌电图（EMG）信号。每个通道的EMG信号包络通过对整流后的单极EMG信号进行低通滤波（8赫兹）来计算。使用PCA和NMF对数据矩阵进行分解，每种算法考虑最多5个因子。使用Pearson相关性比较两种算法之间的解释方差、空间权重和时间得分之间的关联。对于PCA和NMF，单个因子解释了信号方差的约70%，而两个和三个因子分别解释了略超过85%或90%。PCA和NMF解释的方差具有高度可比性（R>0.99）。通过PCA和NMF的非负重建提取的空间权重和时间得分高度相关（所有p<0.001，平均R>0.97）。可以使用高密度表面肌电图和分解算法识别股内侧肌区域激活。如通过PCA和NMF所确定的，区域激活解释了信号方差的高达30%。
