Combining with Principal component analysis and multi-features fusion, subsection auto-regressive model method for sEMG signal was proposed to extract four channels sEMG features base on short time stationary hypothesis firstly.
首先,基于表面肌电信号的短时平稳假设,提出了表面肌电信号的分段自回归建模方法,并利用主成分分析与多特征融合实现四通道表面肌电信号的特征提取。
参考来源 - 基于sEMG信号的外骨骼式机器人上肢康复系统研究·2,447,543篇论文数据，部分数据来源于NoteExpress
Conclusion the simulated SEMG signal based on this method has key characters similar to those of the real SEMG, and it can be used to test the decomposition algorithms.
结论该方法模拟出的SEMG信号更能逼近真实表面肌电信号的特征，可用于验证SEMG分解算法。
Some methods of SEMG signal pretreatment based on blind source separation using second order statistics were proposed for noise separation and the elementary decomposition of multi-channel SEMG.
采用基于二阶统计量的盲源分离算法对多导表面肌电信号进行处理，实现噪声的分离和表面肌电信号的初步分解。
According to the chaotic characteristic of surface electromyography signal, a novel method that USES basic-scale entropy to extract feature from surface electromyography signal (SEMG) was proposed.
提出了一种基于基本尺度熵的表面肌电信号特征的提取方法。
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