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Adaptive Spatial Filtering of High-Density EMG for Reducing the Influence of Noise and Artefacts in Myoelectric Control
- Source :
- IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28:1511-1517
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- Electromyography (EMG) is a source of neural information for controlling neuroprosthetic devices. To enhance the information content of conventional bipolar EMG, high-density EMG systems include tens to hundreds of closely spaced electrodes that non-invasively record the electrical activity of muscles with high spatial resolution. Despite the advantages of relying on multiple signal sources, however, variations in electrode-skin contact impedance and noise remain challenging for multichannel myocontrol systems. These spatial and temporal non-stationarities negatively impact the control accuracy and therefore substantially limit the clinical viability of high-density EMG techniques. Here, we propose an adaptive algorithm for automatic artefact/noise detection and attenuation for high-density EMG control. The method infers the presence of noise in each EMG channel by spectro-temporal measures of signal similarity. These measures are then used for establishing a scoring system based on an adaptive weighting and reinforcement formulation. The method was experimentally tested as a pre-processing step for a multi-class discrimination problem of 4-digit activation. The approach was proven to enhance the discriminative information content of high-density EMG signals, as well as to attenuate non-stationary artefacts, with improvements in accuracy and robustness of the classification.
- Subjects :
- 0301 basic medicine
Computer science
Biomedical Engineering
Electromyography
Signal
03 medical and health sciences
0302 clinical medicine
Discriminative model
Robustness (computer science)
Electric Impedance
Internal Medicine
medicine
Humans
Electrodes
Spatial filter
Adaptive algorithm
medicine.diagnostic_test
Noise (signal processing)
business.industry
General Neuroscience
Rehabilitation
Signal Processing, Computer-Assisted
Pattern recognition
body regions
030104 developmental biology
Artificial intelligence
Artifacts
business
Algorithms
030217 neurology & neurosurgery
Communication channel
Subjects
Details
- ISSN :
- 15580210 and 15344320
- Volume :
- 28
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Accession number :
- edsair.doi.dedup.....b6fc1442b1cf74fe796ddd1f46e01bb4