scholarly journals Convolutive blind source separation on surface EMG signals for respiratory diagnostics and medical ventilation control

2016 ◽  
Author(s):  
Herbert Buchner ◽  
Eike Petersen ◽  
Marcus Eger ◽  
Philipp Rostalski
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Surface Emg ◽  
Ventilation Control ◽  
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

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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