A multi-label deep residual shrinkage network for high-density surface electromyography decomposition in real-time
Background The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including blind source separation (BSS) and deep learning, have...
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| Vydané v: | Journal of neuroengineering and rehabilitation Ročník 22; číslo 1; s. 106 - 19 |
|---|---|
| Hlavní autori: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
BioMed Central
08.05.2025
BioMed Central Ltd BMC |
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| ISSN: | 1743-0003, 1743-0003 |
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| Abstract | Background
The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including blind source separation (BSS) and deep learning, have not yet achieved satisfactory performance, due to high latency or low accuracy.
Methods
This study introduces a novel real-time high-density sEMG (HD-sEMG) decomposition algorithm named ML-DRSNet, which combines multi-label learning with a deep residual shrinkage network (DRSNet) to improve accuracy and reduce latency. ML-DRSNet was evaluated on a public sEMG dataset and the corresponding MUSTs extracted via the convolutional BSS algorithm. An improved multi-label deep convolutional neural network (ML-DCNN) was also evaluated and compared against a conventional multi-task DCNN (MT-DCNN). These networks were trained and tested on various window sizes and step sizes.
Results
With the shortest window size (20 data points) and step size (10 data points), ML-DRSNet significantly outperformed both ML-DCNN (0.86 ± 0.18 vs. 0.71 ± 0.24,
P
< 0.001) and MT-DCNN (0.86 ± 0.18 vs. 0.66 ± 0.16,
P
< 0.001) in decomposition precision. Moreover, ML-DRSNet demonstrated a notably lower latency (15.15 ms) compared to ML-DCNN (69.36 ms) and MT-DCNN (76.96 ms), both of which demonstrated reduced latency relative to BSS-based decomposition methods.
Conclusions
The proposed ML-DRSNet and the improved ML-DCNN algorithms substantially enhance both the accuracy and real-time performance in decomposing MUSTs, establishing a technical foundation for neuro-information-driven motor intention recognition and disease assessment. |
|---|---|
| AbstractList | Abstract Background The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including blind source separation (BSS) and deep learning, have not yet achieved satisfactory performance, due to high latency or low accuracy. Methods This study introduces a novel real-time high-density sEMG (HD-sEMG) decomposition algorithm named ML-DRSNet, which combines multi-label learning with a deep residual shrinkage network (DRSNet) to improve accuracy and reduce latency. ML-DRSNet was evaluated on a public sEMG dataset and the corresponding MUSTs extracted via the convolutional BSS algorithm. An improved multi-label deep convolutional neural network (ML-DCNN) was also evaluated and compared against a conventional multi-task DCNN (MT-DCNN). These networks were trained and tested on various window sizes and step sizes. Results With the shortest window size (20 data points) and step size (10 data points), ML-DRSNet significantly outperformed both ML-DCNN (0.86 ± 0.18 vs. 0.71 ± 0.24, P < 0.001) and MT-DCNN (0.86 ± 0.18 vs. 0.66 ± 0.16, P < 0.001) in decomposition precision. Moreover, ML-DRSNet demonstrated a notably lower latency (15.15 ms) compared to ML-DCNN (69.36 ms) and MT-DCNN (76.96 ms), both of which demonstrated reduced latency relative to BSS-based decomposition methods. Conclusions The proposed ML-DRSNet and the improved ML-DCNN algorithms substantially enhance both the accuracy and real-time performance in decomposing MUSTs, establishing a technical foundation for neuro-information-driven motor intention recognition and disease assessment. The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including blind source separation (BSS) and deep learning, have not yet achieved satisfactory performance, due to high latency or low accuracy. This study introduces a novel real-time high-density sEMG (HD-sEMG) decomposition algorithm named ML-DRSNet, which combines multi-label learning with a deep residual shrinkage network (DRSNet) to improve accuracy and reduce latency. ML-DRSNet was evaluated on a public sEMG dataset and the corresponding MUSTs extracted via the convolutional BSS algorithm. An improved multi-label deep convolutional neural network (ML-DCNN) was also evaluated and compared against a conventional multi-task DCNN (MT-DCNN). These networks were trained and tested on various window sizes and step sizes. With the shortest window size (20 data points) and step size (10 data points), ML-DRSNet significantly outperformed both ML-DCNN (0.86 ± 0.18 vs. 0.71 ± 0.24, P < 0.001) and MT-DCNN (0.86 ± 0.18 vs. 0.66 ± 0.16, P < 0.001) in decomposition precision. Moreover, ML-DRSNet demonstrated a notably lower latency (15.15 ms) compared to ML-DCNN (69.36 ms) and MT-DCNN (76.96 ms), both of which demonstrated reduced latency relative to BSS-based decomposition methods. The proposed ML-DRSNet and the improved ML-DCNN algorithms substantially enhance both the accuracy and real-time performance in decomposing MUSTs, establishing a technical foundation for neuro-information-driven motor intention recognition and disease assessment. Background The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including blind source separation (BSS) and deep learning, have not yet achieved satisfactory performance, due to high latency or low accuracy. Methods This study introduces a novel real-time high-density sEMG (HD-sEMG) decomposition algorithm named ML-DRSNet, which combines multi-label learning with a deep residual shrinkage network (DRSNet) to improve accuracy and reduce latency. ML-DRSNet was evaluated on a public sEMG dataset and the corresponding MUSTs extracted via the convolutional BSS algorithm. An improved multi-label deep convolutional neural network (ML-DCNN) was also evaluated and compared against a conventional multi-task DCNN (MT-DCNN). These networks were trained and tested on various window sizes and step sizes. Results With the shortest window size (20 data points) and step size (10 data points), ML-DRSNet significantly outperformed both ML-DCNN (0.86 ± 0.18 vs. 0.71 ± 0.24, P < 0.001) and MT-DCNN (0.86 ± 0.18 vs. 0.66 ± 0.16, P < 0.001) in decomposition precision. Moreover, ML-DRSNet demonstrated a notably lower latency (15.15 ms) compared to ML-DCNN (69.36 ms) and MT-DCNN (76.96 ms), both of which demonstrated reduced latency relative to BSS-based decomposition methods. Conclusions The proposed ML-DRSNet and the improved ML-DCNN algorithms substantially enhance both the accuracy and real-time performance in decomposing MUSTs, establishing a technical foundation for neuro-information-driven motor intention recognition and disease assessment. Background The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including blind source separation (BSS) and deep learning, have not yet achieved satisfactory performance, due to high latency or low accuracy. Methods This study introduces a novel real-time high-density sEMG (HD-sEMG) decomposition algorithm named ML-DRSNet, which combines multi-label learning with a deep residual shrinkage network (DRSNet) to improve accuracy and reduce latency. ML-DRSNet was evaluated on a public sEMG dataset and the corresponding MUSTs extracted via the convolutional BSS algorithm. An improved multi-label deep convolutional neural network (ML-DCNN) was also evaluated and compared against a conventional multi-task DCNN (MT-DCNN). These networks were trained and tested on various window sizes and step sizes. Results With the shortest window size (20 data points) and step size (10 data points), ML-DRSNet significantly outperformed both ML-DCNN (0.86 ± 0.18 vs. 0.71 ± 0.24, P < 0.001) and MT-DCNN (0.86 ± 0.18 vs. 0.66 ± 0.16, P < 0.001) in decomposition precision. Moreover, ML-DRSNet demonstrated a notably lower latency (15.15 ms) compared to ML-DCNN (69.36 ms) and MT-DCNN (76.96 ms), both of which demonstrated reduced latency relative to BSS-based decomposition methods. Conclusions The proposed ML-DRSNet and the improved ML-DCNN algorithms substantially enhance both the accuracy and real-time performance in decomposing MUSTs, establishing a technical foundation for neuro-information-driven motor intention recognition and disease assessment. Keywords: Real-time decomposition, Motor unit spike trains, High-density surface electromyography, Multi-label learning, Deep residual shrinkage network The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including blind source separation (BSS) and deep learning, have not yet achieved satisfactory performance, due to high latency or low accuracy.BACKGROUNDThe swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including blind source separation (BSS) and deep learning, have not yet achieved satisfactory performance, due to high latency or low accuracy.This study introduces a novel real-time high-density sEMG (HD-sEMG) decomposition algorithm named ML-DRSNet, which combines multi-label learning with a deep residual shrinkage network (DRSNet) to improve accuracy and reduce latency. ML-DRSNet was evaluated on a public sEMG dataset and the corresponding MUSTs extracted via the convolutional BSS algorithm. An improved multi-label deep convolutional neural network (ML-DCNN) was also evaluated and compared against a conventional multi-task DCNN (MT-DCNN). These networks were trained and tested on various window sizes and step sizes.METHODSThis study introduces a novel real-time high-density sEMG (HD-sEMG) decomposition algorithm named ML-DRSNet, which combines multi-label learning with a deep residual shrinkage network (DRSNet) to improve accuracy and reduce latency. ML-DRSNet was evaluated on a public sEMG dataset and the corresponding MUSTs extracted via the convolutional BSS algorithm. An improved multi-label deep convolutional neural network (ML-DCNN) was also evaluated and compared against a conventional multi-task DCNN (MT-DCNN). These networks were trained and tested on various window sizes and step sizes.With the shortest window size (20 data points) and step size (10 data points), ML-DRSNet significantly outperformed both ML-DCNN (0.86 ± 0.18 vs. 0.71 ± 0.24, P < 0.001) and MT-DCNN (0.86 ± 0.18 vs. 0.66 ± 0.16, P < 0.001) in decomposition precision. Moreover, ML-DRSNet demonstrated a notably lower latency (15.15 ms) compared to ML-DCNN (69.36 ms) and MT-DCNN (76.96 ms), both of which demonstrated reduced latency relative to BSS-based decomposition methods.RESULTSWith the shortest window size (20 data points) and step size (10 data points), ML-DRSNet significantly outperformed both ML-DCNN (0.86 ± 0.18 vs. 0.71 ± 0.24, P < 0.001) and MT-DCNN (0.86 ± 0.18 vs. 0.66 ± 0.16, P < 0.001) in decomposition precision. Moreover, ML-DRSNet demonstrated a notably lower latency (15.15 ms) compared to ML-DCNN (69.36 ms) and MT-DCNN (76.96 ms), both of which demonstrated reduced latency relative to BSS-based decomposition methods.The proposed ML-DRSNet and the improved ML-DCNN algorithms substantially enhance both the accuracy and real-time performance in decomposing MUSTs, establishing a technical foundation for neuro-information-driven motor intention recognition and disease assessment.CONCLUSIONSThe proposed ML-DRSNet and the improved ML-DCNN algorithms substantially enhance both the accuracy and real-time performance in decomposing MUSTs, establishing a technical foundation for neuro-information-driven motor intention recognition and disease assessment. The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including blind source separation (BSS) and deep learning, have not yet achieved satisfactory performance, due to high latency or low accuracy. This study introduces a novel real-time high-density sEMG (HD-sEMG) decomposition algorithm named ML-DRSNet, which combines multi-label learning with a deep residual shrinkage network (DRSNet) to improve accuracy and reduce latency. ML-DRSNet was evaluated on a public sEMG dataset and the corresponding MUSTs extracted via the convolutional BSS algorithm. An improved multi-label deep convolutional neural network (ML-DCNN) was also evaluated and compared against a conventional multi-task DCNN (MT-DCNN). These networks were trained and tested on various window sizes and step sizes. With the shortest window size (20 data points) and step size (10 data points), ML-DRSNet significantly outperformed both ML-DCNN (0.86 ± 0.18 vs. 0.71 ± 0.24, P < 0.001) and MT-DCNN (0.86 ± 0.18 vs. 0.66 ± 0.16, P < 0.001) in decomposition precision. Moreover, ML-DRSNet demonstrated a notably lower latency (15.15 ms) compared to ML-DCNN (69.36 ms) and MT-DCNN (76.96 ms), both of which demonstrated reduced latency relative to BSS-based decomposition methods. The proposed ML-DRSNet and the improved ML-DCNN algorithms substantially enhance both the accuracy and real-time performance in decomposing MUSTs, establishing a technical foundation for neuro-information-driven motor intention recognition and disease assessment. |
| ArticleNumber | 106 |
| Audience | Academic |
| Author | Ma, Jinting Wang, Lifen Dan, Guo Zhang, Naiwen Wei, Jing Li, Jianjun Jiang, Naifu Tan, Lihai Wu, Renxiang Li, Qiuyuan Li, Guanglin |
| Author_xml | – sequence: 1 givenname: Jinting surname: Ma fullname: Ma, Jinting organization: School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University – sequence: 2 givenname: Lifen surname: Wang fullname: Wang, Lifen organization: School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University – sequence: 3 givenname: Renxiang surname: Wu fullname: Wu, Renxiang organization: School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University – sequence: 4 givenname: Naiwen surname: Zhang fullname: Zhang, Naiwen organization: School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University – sequence: 5 givenname: Jing surname: Wei fullname: Wei, Jing organization: School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University – sequence: 6 givenname: Jianjun surname: Li fullname: Li, Jianjun organization: Rehabilitation Clinic, Shenzhen University General Hospital – sequence: 7 givenname: Qiuyuan surname: Li fullname: Li, Qiuyuan organization: Rehabilitation Clinic, Shenzhen University General Hospital – sequence: 8 givenname: Lihai surname: Tan fullname: Tan, Lihai organization: Guangdong-Hongkong-Macau Institute of CNS Regeneration and Jinan University (Shenzhen), Center for Language and Brain, Shenzhen Institute of Neuroscience – sequence: 9 givenname: Guanglin surname: Li fullname: Li, Guanglin organization: Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences – sequence: 10 givenname: Naifu surname: Jiang fullname: Jiang, Naifu email: nf.jiang@siat.ac.cn organization: Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences – sequence: 11 givenname: Guo surname: Dan fullname: Dan, Guo email: danguo@szu.edu.cn organization: School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40340912$$D View this record in MEDLINE/PubMed |
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| Keywords | Motor unit spike trains Multi-label learning Real-time decomposition High-density surface electromyography Deep residual shrinkage network |
| Language | English |
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The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time... The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in... Background The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time... Abstract Background The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling... |
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| SubjectTerms | Algorithms Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Deep Learning Deep residual shrinkage network Electromyography Electromyography - methods High-definition television High-density surface electromyography Humans Machine learning Methods Motor unit spike trains Multi-label learning Muscle, Skeletal - physiology Neural Networks, Computer Neurology Neurophysiology Neurosciences Real-time decomposition Rehabilitation Medicine Signal processing Signal Processing, Computer-Assisted Specific gravity |
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| Title | A multi-label deep residual shrinkage network for high-density surface electromyography decomposition in real-time |
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