ArticleJournal of neuroengineering and rehabilitation2020
Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury.
Article in Journal of neuroengineering and rehabilitation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Objective Biomarker Development for Parameter Optimization in Neuromodulation Using High-Density EMG Temporal and Spatial Features.Bioengineering (Basel, Switzerland) · 2026Article
- SR-FSL: Sample reconstruction enhanced few-shot learning for real-time motor unit identification from surface electromyogram.Journal of neuroengineering and rehabilitation · 2026Article
- Electromyography-Force Relation and Muscle Fiber Conduction Velocity Affected by Spinal Cord Injury.Bioengineering (Basel, Switzerland) · 2023Article
- Properties of the surface electromyogram following traumatic spinal cord injury: a scoping review.Journal of neuroengineering and rehabilitation · 2021Article
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Authors and funding
6 authors.
Funding
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Abstract
backgroundSpatial filtering of multi-channel signals is considered to be an effective pre-processing approach for improving signal-to-noise ratio. The use of spatial filtering for preprocessing high-density (HD) surface electromyogram (sEMG) helps to extract critical spatial information, but its application to non-invasive examination of neuromuscular changes have not been well investigated.
methodsAimed at evaluating how spatial filtering can facilitate examination of muscle paralysis, three different spatial filtering methods are presented using principle component analysis (PCA) algorithm, non-negative matrix factorization (NMF) algorithm, and both combination, respectively. Their performance was evaluated in terms of diagnostic power, through HD-sEMG clustering index (CI) analysis of neuromuscular changes in paralyzed muscles following spinal cord injury (SCI).
resultsThe experimental results showed that: (1) The CI analysis of conventional single-channel sEMG can reveal complex neuromuscular changes in paralyzed muscles following SCI, and its diagnostic power has been confirmed to be characterized by the variance of Z scores; (2) the diagnostic power was highly dependent on the location of sEMG recording channel. Directly averaging the CI diagnostic indicators over channels just reached a medium level of the diagnostic power; (3) the use of either PCA-based or NMF-based filtering method yielded a greater diagnostic power, and their combination could even enhance the diagnostic power significantly.
conclusionsThis study not only presents an essential preprocessing approach for improving diagnostic power of HD-sEMG, but also helps to develop a standard sEMG preprocessing pipeline, thus promoting its widespread application.
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