Beamforming for Powerline Interference in Large Sensor Arrays

This paper shows how to use beamforming to remove the power-line interference (PLI) in large surface electromyography (sEMG) sensor array or high-density sEMG. The method exploits the highly correlated nature of the different sources of interference, being part of the same electrical grid, and their narrow frequency bands. The idea is to use a very narrow pass-band filter around 50 or 60 Hz to get signals with high PLI content before applying a spatial filtering by principal component analysis (PCA). This way, beamforming are done on the frequency bands where PLI are presents. Also, it ensures that even if the PLI has a smaller overall power than the desired signal, it will be easily found as the most powerful component of the decomposition. The PLI can then be removed from the signal. With trivial modification, harmonics of the PLI can also be removed. The approach was used in the context of muscle behavior analyses of low back pain patients using a sEMG array of 64 sensors. The performances of the filter are studied by experimental and semi-empirical methods. Compared to the usual notch filter, an improvement of up 10 dB is found.

[1]  Carlos Guerrero-Mosquera,et al.  Automatic removal of ocular artifacts from EEG data using adaptive filtering and Independent Component Analysis , 2009, 2009 17th European Signal Processing Conference.

[2]  Sanqing Hu,et al.  Removal of Scalp Reference Signal and Line Noise for Intracranial EEGs , 2008, 2008 IEEE International Conference on Networking, Sensing and Control.

[3]  Daniel Massicotte,et al.  Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain , 2012, EURASIP J. Adv. Signal Process..

[4]  Daniel Massicotte,et al.  Surrogate analysis of fractal dimensions from SEMG sensor array as a predictor of chronic low back pain , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[5]  Allan Kardec Barros,et al.  independent , 2006, Gumbo Ya Ya.

[6]  M. P. S. Chawla,et al.  PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: A survey and comparison , 2011, Appl. Soft Comput..

[7]  L. Tarassenko,et al.  Application of ICA in Removing Artefacts from the ECG , 2002 .

[8]  B.D. Van Veen,et al.  Beamforming: a versatile approach to spatial filtering , 1988, IEEE ASSP Magazine.

[9]  Sanqing Hu,et al.  Automatic Identification and Removal of Scalp Reference Signal for Intracranial EEGs Based on Independent Component Analysis , 2007, IEEE Transactions on Biomedical Engineering.

[10]  F. Mohd-Yasin,et al.  Techniques of EMG signal analysis: detection, processing, classification and applications , 2006, Biological Procedures Online.

[11]  Jiang Shengtao,et al.  Removal of Power Line Interference of ECG Signal Based on Independent Component Analysis , 2009, 2009 First International Workshop on Education Technology and Computer Science.