Independent component analysis based algorithms for high-density electromyogram decomposition: Systematic evaluation through simulation

Dai, Chenyun; Hu, Xiaogang

doi:10.1016/j.compbiomed.2019.04.033

Cited by 40 publications

(18 citation statements)

References 35 publications

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“…HD-EMG data were subjected to a motion artifact removal procedure [25] prior to further data analysis. MU firing activities were then derived from flexor EMG to estimate finger force and joint angle associated with finger flexion tasks using a previously developed algorithm [26,27]. In this study, the HD-EMG data collected for the single-isometric and single-dynamic task were used for MU decomposition, and the obtained separation information was directly applied to the single-combined data to predict motor output.…”

Section: Discussionmentioning

confidence: 99%

Optimized Model Selection for Concurrent Decoding of Finger Kinetics and Kinematics

Roy

Kamper

2023

IEEE Access

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Myoelectric-based motor intent detection is typically used to interface with assistive devices. However, the intent detection performance is sensitive to interference of electromyogram (EMG) signals.Recently, EMG signals are decomposed into motor units (MU) firing activities, and neuron binary firing activities can be used to predict motor output in a continuous manner. Different functions that map MU firings to motor output have been implemented, and both composite MU firing frequency and individual MU firing frequency have been used. It is unclear whether one mapping function outperform others. Accordingly, we evaluated three MU-based finger kinetic and kinematic prediction models, by varying the number of MUs and the method of including MU firings into the regression model. We also compared the performance of three EMG amplitude-based models with varying number of channels. We performed MU decomposition in advance for real-time implementations. Our results showed that individual firing frequency of five MUs provided the lowest estimation error (force: 4.66±0.36 %MVC; joint angle: 4.81±0.49°) and highest correlation (force: 0.86±0.01; joint angle: 0.93±0.01) with the measured motor outputs, when compared with mapping method using the populational firing frequency of all MUs or the populational firing frequency of a group of MUs with similar firing activities. The results indicated that firing information at the population level may mask critical information of individual MU firings. These findings allowed us to identify the optimal models for concurrent and continuous finger force and joint angle estimation. A combination of the minimal level of complexity and high accuracy make these models suitable for real-time control of assistive robotic devices.

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Section: Discussionmentioning

confidence: 99%

Optimized Model Selection for Concurrent Decoding of Finger Kinetics and Kinematics

Roy

Kamper

2023

IEEE Access

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“…It is notable that different or lower thresholds were used by others in the search for common motor units, resulting in larger numbers of common motor units at the expense of matching degree. For example, two spike trains were considered as from a common motor unit if the number of their matching spikes reached 30 or 50% of the total number of spikes (i.e., C ≥ 30% A and C ≥ 30% B ; or C ≥ 50% A and C ≥ 50% B ) (Negro et al, 2016 ; Dai and Hu, 2019 ). This corresponds to MR ≥ 30% (or 50%) and RoA ≥ 17.6% (or 33.3%).…”

Section: Motor Units With Intermediate Matching Require Attentionmentioning

confidence: 99%

“…In fact, most surface EMG decomposition algorithms based on blind source separation (BSS) use a parallel search strategy, i.e., they repeatedly search for a number of motor units from the original signal and then deal with duplicates at the end. A usual processing method is to keep the spike train with the highest silhouette (SIL) value among the duplicates (Dai and Hu, 2019 ). However, solely relying on SIL is not sufficient to exclude those motor units with intermediate matching.…”

Section: Examination Of Intermediate Matching In Experimental Surface...mentioning

confidence: 99%

“…In this development, a key issue is how to validate the decomposition yield. Various approaches have been proposed, including EMG simulation (Dai and Hu, 2019 ; Mohebian et al, 2019 ), two-source validation (Mambrito and De Luca, 1984 ; Holobar et al, 2010 ; Marateb et al, 2011 ; Negro et al, 2016 ; Chen et al, 2018a ), and decompose-synthesize-decompose-comparisons test (Nawab et al, 2010 ). The agreement in spike trains between the decomposed and reference motor units is often used to evaluate the performance of surface EMG decomposition algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Caution Is Necessary for Acceptance of Motor Units With Intermediate Matching in Surface EMG Decomposition

Chen

Zhou

2022

Front. Neurosci.

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“…Increased understanding of MUAP decomposition as a classification method has led to an increase in highdensity surface recordings being taken from muscles (Hassan et al, 2019;Chen et al, 2020a;Chen et al, 2020b;Dai & Hu, 2019). In this review we outlined how tissue regeneration can impact the spatial features of the EMG signals detected by these multichannel arrays.…”

Section: Inputs and Outputsmentioning

confidence: 99%

Cut wires: The Electrophysiology of Regenerated Tissue

Lowe

Thakor

2021

Bioelectron Med

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When nerves are damaged by trauma or disease, they are still capable of firing off electrical command signals that originate from the brain. Furthermore, those damaged nerves have an innate ability to partially regenerate, so they can heal from trauma and even reinnervate new muscle targets. For an amputee who has his/her damaged nerves surgically reconstructed, the electrical signals that are generated by the reinnervated muscle tissue can be sensed and interpreted with bioelectronics to control assistive devices or robotic prostheses. No two amputees will have identical physiologies because there are many surgical options for reconstructing residual limbs, which may in turn impact how well someone can interface with a robotic prosthesis later on. In this review, we aim to investigate what the literature has to say about different pathways for peripheral nerve regeneration and how each pathway can impact the neuromuscular tissue’s final electrophysiology. This information is important because it can guide us in planning the development of future bioelectronic devices, such as prosthetic limbs or neurostimulators. Future devices will primarily have to interface with tissue that has undergone some natural regeneration process, and so we have explored and reported here what is known about the bioelectrical features of neuromuscular tissue regeneration.

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Independent component analysis based algorithms for high-density electromyogram decomposition: Systematic evaluation through simulation

Cited by 40 publications

References 35 publications

Optimized Model Selection for Concurrent Decoding of Finger Kinetics and Kinematics

Optimized Model Selection for Concurrent Decoding of Finger Kinetics and Kinematics

Caution Is Necessary for Acceptance of Motor Units With Intermediate Matching in Surface EMG Decomposition

Cut wires: The Electrophysiology of Regenerated Tissue

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