High-density surface EMG decomposition based on a convolutive blind source separation approach

2012 ◽  
Author(s):  
Xiangjun Zhu ◽  
Yingchun Zhang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Surface Emg ◽  
High Density ◽  
Density Surface ◽  
Convolutive Blind Source Separation

Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation

2016 ◽  
Vol 13 (2) ◽  
pp. 026027 ◽  
Author(s):  
Francesco Negro ◽  
Silvia Muceli ◽  
Anna Margherita Castronovo ◽  
Ales Holobar ◽  
Dario Farina
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Surface Emg ◽  
Convolutive Blind Source Separation

Analysis of High-Density Surface EMG and Finger Pressure in the Left Forearm of Violin Players: A Feasibility Study

2017 ◽  
Vol 32 (3) ◽  
pp. 139-151 ◽  
Author(s):  
Paolo Cattarello ◽  
Roberto Merletti ◽  
Francesco Petracca
Keyword(s):  
Pressure Sensors ◽  
Surface Emg ◽  
High Density ◽  
String Instruments ◽  
Activation Patterns ◽  
Study Objective ◽  
Left Hand ◽  
Density Surface ◽  
The Right ◽  
Finger Pressure

Wrist and finger flexor muscles of the left hand were evaluated using high-density surface EMG (HDsEMG) in 17 violin players. Pressure sensors also were mounted below the second string of the violin to evaluate, simultaneously, finger pressure. Electrode grid size was 110x70 mm (12x8 electrodes with interelectrode distance=10 mm and Ø=3 mm). The study objective was to observe the activation patterns of these muscles while the violinists sequentially played four notes—-SI (B), DO# (C#), RE (D), MI (E)—-at 2 bows/s (one bow up in 0.5 s and one down in 0.5 s) and 4 bows/s on the second string, while producing a constant (CONST) or ramp (RAMP) sound volume. HDsEMG images obtained while playing the notes were compared with those obtained during isometric radial or ulnar flexion of the wrist or fingers. Two image descriptors provided information on image differences. Results showed that the technique was reliable and provided reliable signals, and that recognizably different sEMG images could be associated with the four notes tested, despite the variability within and between subjects playing the same note. sEMG activity of the left hand muscles and pressure on the string in the RAMP task were strongly affected in some individuals by the sound volume (controlled by the right hand) and much less in other individuals. These findings question whether there is an individual or generally optimal way of pressing violin strings with the left hand. The answer to this question might substantially modify the teaching of string instruments.

scholarly journals Real Time Blind Source Separation in Reverberant Environments

2021 ◽  
Author(s):  
◽  
Timothy Sherry
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Real World Data ◽  
The Core ◽  
Reverberant Environments ◽  
Ica Algorithm ◽  
Stationary Sources ◽  
Convolutive Blind Source Separation

<p>An online convolutive blind source separation solution has been developed for use in reverberant environments with stationary sources. Results are presented for simulation and real world data. The system achieves a separation SINR of 16.8 dB when operating on a two source mixture, with a total acoustic delay was 270 ms. This is on par with, and in many respects outperforms various published algorithms [1],[2]. A number of instantaneous blind source separation algorithms have been developed, including a block wise and recursive ICA algorithm, and a clustering based algorithm, able to obtain up to 110 dB SIR performance. The system has been realised in both Matlab and C, and is modular, allowing for easy update of the ICA algorithm that is the core of the unmixing process.</p>

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