Bingham Adrian, Jelfs Beth, Arjunan Sridhar P, Kumar Dinesh K
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2325-2328. doi: 10.1109/EMBC.2018.8512846.
In this study we developed a technique for identifying noisy electrodes in high density surface electromyography (HD-sEMG). The technique finds the spatial similarity of each electrode in the electrode array by counting the number of interactions the electrode has. Using this information the technique identifies noisy electrodes by finding electrodes that are significantly dissimilar to the other electrodes. The HD-sEMG recordings used in this study were taken from three participants who performed two isometric contractions of their biceps at 40% and 80% of their maximum voluntary contraction (MVC) force. White Gaussian noisy was added to a varying number of recorded signals before being digital filtering to generate a variety of recordings to test the technique with. In the recordings, groups of 2, 4, 8, and 16 electrodes had noise added such that the signal to noise ratios (SNR) were 0, 5, 10, 15, and 20dB. The results show that the technique can reliably identify groups of 2, 4, and 8 noisy electrodes with SNRs of 0, 5, and 10dB.
在本研究中，我们开发了一种用于识别高密度表面肌电图（HD-sEMG）中噪声电极的技术。该技术通过计算电极的相互作用次数来找出电极阵列中每个电极的空间相似性。利用这些信息，该技术通过找出与其他电极显著不同的电极来识别噪声电极。本研究中使用的HD-sEMG记录取自三名参与者，他们以最大自主收缩（MVC）力的40%和80%进行了两次肱二头肌等长收缩。在进行数字滤波之前，向不同数量的记录信号中添加了高斯白噪声，以生成各种记录来测试该技术。在这些记录中，分别对2、4、8和16个电极组添加噪声，使得信噪比（SNR）分别为0、5、10、15和20dB。结果表明，该技术能够可靠地识别信噪比为0、5和10dB时的2、4和8个噪声电极组。
