ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2018
A Novel Validation Approach for High-Density Surface EMG Decomposition in Motor Neuron Disease.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Applications of electromyography in Amyotrophic Lateral Sclerosis: A systematic review.PloS one · 2026Pooled it
- The use of high-density surface electromyography in amyotrophic lateral sclerosis: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Characterization of motor unit activities during isometric elbow flexion with different speeds.Journal of neuroengineering and rehabilitation · 2025Article
- A motor unit action potential-based method for surface electromyography decomposition.Journal of neuroengineering and rehabilitation · 2025Article
- Novel Diagnostic and Therapeutic Approaches to Temporomandibular Dysfunction: A Narrative Review.Life (Basel, Switzerland) · 2023Review
- Towards Evaluating Pitch-Related Phonation Function in Speech Communication Using High-Density Surface Electromyography.Frontiers in neuroscience · 2022Article
- Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury.Journal of neuroengineering and rehabilitation · 2020Article
- Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points.Frontiers in computational neuroscience · 2019Article
- Automatic Multichannel Intramuscular Electromyogram Decomposition: Progressive FastICA Peel-Off and Performance Validation.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
This paper presents a novel two-source approach for validating the performance of high-density surface electromyogram (EMG) decomposition. The approach was developed taking advantage of surface EMG characteristics of amyotrophic lateral sclerosis (ALS). High-density surface EMG data from ALS patients can be divided to the sparse data set and the interference data set, with the former decomposed by expert visual inspection while the latter independently decomposed by the surface EMG decomposition algorithm. The agreement of the decomposition yields from the two data sets can be quantified for evaluating the surface EMG decomposition performance. The novel validation approach was performed for a recently developed method called automatic progressive FastICA peel-off (APFP) for high-density surface EMG decomposition. The APFP framework was used to automatically decompose high-density surface EMG signals recorded from the first dorsal interosseous muscle of ALS subjects. The common motor units independently decomposed from the interference data set and the sparse data set demonstrated an average matching rate of 99.18% ± 1.11%. The characteristics of the ALS surface EMG also facilitate a step by step illustration of the APFP framework for high-density surface EMG decomposition. The novel approach presented in this paper can supplement conventional two-source validation for accuracy assessment of decomposed motor units from experimental signals, which is essential for development of surface EMG decomposition methods.
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