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Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation.

机译：利用图切割分割从低信噪比多通道表面肌电图记录中检测多个神经支配区。

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Hamid Reza Marateb; Morteza Farahi; Monica Rojas; Miguel Angel Mañanas; Dario Farina;
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1. Three-Dimensional Innervation Zone Imaging from Multi-Channel Surface EMG Recordings [J] . Liu Yang, Ning Yong, Li Sheng, International Journal of Neural Systems . 2015,第6期

机译：多通道表面肌电图记录的三维神经区域成像
2. Automatic detection of motor unit innervation zones of the external anal sphincter by multichannel surface EMG [J] . Ullah Khalil, Cescon Corrado, Afsharipour Babak, Journal of electromyography and kinesiology: Official journal of the International Society of Electrophysiological Kinesiology . 2014,第6期

机译：通过多通道表面肌电图自动检测肛门外括约肌的运动单元神经支配区
3. Innervation asymmetry of the external anal sphincter in aging characterized from high‐density intra‐rectal surface EMG recordings [J] . Dias Nicholas, Li Xuhong, Zhang Chuan, Neurourology and urodynamics. . 2018,第8期

机译：衰老中的外部肛门括约肌的支护不对称性来自高密度内侧表面EMG录音
4. Automatic Location Of Muscle Innervation Zones From Multi-Channel Surface EMG Signals [C] . Cescon, C. . 2006

机译：从多通道表面肌电信号自动定位肌肉神经区域
5. Iterative timing recovery for magnetic recording channels with low signal-to-noise ratio. [D] . Nayak, Aravind R. 2004

机译：具有低信噪比的磁记录通道的迭代定时恢复。
6. Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation [O] . Hamid Reza Marateb, Morteza Farahi, Monica Rojas, 2011

机译：使用图割分割技术从低信噪比的多通道表面肌电图记录中检测多个神经区域
7. Detection of multiple innervation zones from multi-channel surface emg recordings with low signal-to-noise ratio using graph-cut segmentation [O] . Marateb HR, Farahi M, Rojas M, 2016

机译：使用图切割分割从具有低信噪比的多通道表面emg记录中检测多个神经支配区域
8. Changes in Impedance at the Electrode-Skin Interface of Surface EMG electrodes During Long-Term EMG Recordings. [R] . Hewson, D. J., Duchene, J., Hogrel, J. Y. 2001

机译：在长期肌电图记录期间表面肌电图电极的电极 - 皮肤界面的阻抗变化。

Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation.

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