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。
The Experimental result shows that the SEMG detection electrode can further improve SNR, reduce the noise and get effective surface electromyographic signal.
试验结果表明，采用该SEMG检测电极可以进一步提高信噪比，减少噪声，提取到有效的表面肌电信号。
The Experimental result shows that the SEMG detection electrode can further improve SNR, reduce the noise and get effective surface electromyographic signal.
试验结果表明，采用该SEMG检测电极可以进一步提高信噪比，减少噪声，提取到有效的表面肌电信号。
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