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Source separation and localization of individual superficial forearm extensor muscles using high-density surface electromyography

机译:使用高密度表面肌电图形的各个浅表前臂伸肌肌的源分离和定位

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The limitations of conventional surface electromyography (sEMG) cause it to be unsuitable for use with the deep and compact muscles of the forearm. However, while source separation and localization techniques have been extensively explored to identify active sources in the brain using electroencephalography (EEG) signals, these techniques have not been adapted for identifying active sources in muscles using sEMG signals, despite being of a similar premise. Here, we perform an experiment to explore the capabilities of conventional EEG single-dipole localization techniques to localize the extensor digitorum and extensor indicis when selectively activated. The localization methodology consists of separating the raw sEMG signals using independent component analysis (ICA), estimating a physics-based forward model, and then correlating the obtained lead-field matrix with the ICA mixing matrix. The results show that single-dipole localization is not suitable for describing the active sources of muscles.
机译:常规表面肌电图(SEMG)的局限性使其不适合使用前臂的深和紧凑的肌肉。然而,虽然已经广泛地探索了源分离和定位技术,以使用脑电图(EEG)信号识别大脑中的有源源,但这些技术尚未适于使用SEM信号识别肌肉中的有源源,尽管存在类似的前提。在这里,我们执行实验以探索传统EEG单偶极定位技术的能力,以在选择性激活时本地化延伸点数字和伸出标记。本地化方法包括使用独立分量分析(ICA)来分离原始SEMG信号,估计基于物理学的前瞻性模型,然后用ICA混合矩阵与所获得的引线矩阵相关联。结果表明,单偶极本地化不适合描述主动肌源。

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