Force and Neuromuscular Responses During a Handgrip Hold to Failure Anchored to a Moderate Perceptual Intensity in Males.

OBJECTIVES: The current study investigated performance fatigability (PF) and time course of changes in force, electromyographic amplitude (EMG AMP) and frequency (EMG MPF), and neuromuscular efficiency (NME) during a sustained, isometric, handgrip hold to failure (HTF) using the rating of perceived exertion (RPE)-Clamp Model. METHODS: Twelve males performed a handgrip HTF anchored to RPE=5. The time to task failure (Tlim), force (N), EMG AMP and MPF, and NME (normalized force/ normalized EMG AMP) were recorded. Analyses included a paired samples t-test for PF at an alpha of p<0.05, 1-way repeated measures ANOVA across time and post-hoc t-tests (p<0.0025) for force, EMG AMP and MPF, and NME responses. RESULTS: The PF (pre- to post- maximal force % decline) was 38.2±11.5%. There were decreases in responses, relative to 0% Tlim, from 40% to 100% Tlim (force), at 30%, 60%, and 100% Tlim (EMG AMP), from 10% to 100% Tlim(EMP MPF), and from 50% to 65%, and 80% to 100% Tlim (NME) (p<0.0025). CONCLUSIONS: The RPE-Clamp Model in this study demonstrated that pacing strategies may be influenced by the integration of anticipatory, feedforward, and feedback mechanisms, and provided insights into the relationship between neuromuscular and perceptual responses, and actual force generating capacity.

Neural Drive and Motor Unit Characteristics of the Serratus Anterior in Individuals With Scapular Dyskinesis.

OBJECTIVE: Scapular dyskinesis is one of the causes of shoulder disorders and involves muscle weakness in the serratus anterior. This study investigated whether motor unit (MU) recruitment and firing property, which are important for muscle exertion, have altered in serratus anterior of the individuals with scapular dyskinesis. METHODS: Asymptomatic adults with (SD) and without (control) scapular dyskinesis were analyzed. Surface electromyography (sEMG) waveforms were collected at submaximal voluntary contraction of the serratus anterior. The sEMG waveform was decomposed into MU action potential amplitude (MUAPAMP), mean firing rate (MFR), and recruitment threshold. MUs were divided into low, moderate, and high thresholds, and MU recruitment and firing properties of the groups were compared. RESULTS: High-threshold MUAPAMP was significantly smaller in the SD group than in the control group. The control group also exhibited recruitment properties that reflected the size principle, however, the SD group did not. Furthermore, the SD group had a lower MFR than the control group. CONCLUSIONS: Individuals with scapular dyskinesis exhibit altered MU recruitment properties and lower firing rates of the serratus anterior; this may be detrimental to muscle performance. Thus, it may be necessary to improve the neural drive of the serratus anterior when correcting scapular dyskinesis.

Effects of Bilateral or Unilateral Plyometric Training of Lower Limbs on the Bilateral Deficit During Explosive Efforts.

OBJECTIVES: Bilateral Deficit (BLD) occurs when the force generated by both limbs together is smaller than the sum of the forces developed separately by the two limbs. BLD may be modulated by physical training. Here, were investigated the effects of unilateral or bilateral plyometric training on BLD and neuromuscular activation during lower limb explosive extensions. METHODS: Fourteen young males were randomized into the unilateral (UL_) or bilateral (BL_) training group. Plyometric training (20 sessions, 2 days/week) was performed on a sled ergometer, and consisted of UL or BL consecutive, plyometric lower limb extensions (3-to-5 sets; 8-to-10 repetitions). Before and after training, maximal explosive efforts with both lower limbs or with each limb separately were assessed. Electromyography of representative lower limb muscles was measured. RESULTS: BL_training significantly and largely decreased BLD (p=0.003, effect size=1.63). This was accompanied by the reversion from deficit to facilitation of the electromyography amplitude of knee extensors during bilateral efforts (p=0.007). Conversely, UL_training had negligible effects on BLD (p=0.781). Also, both groups showed similar improvements in their maximal explosive power generated after training. CONCLUSIONS: Bilateral plyometric training can mitigate BLD, and should be considered for training protocols focused on improving bilateral lower limb motor performance.

Optimizing shoulder elevation assist rate in exoskeletal rehabilitation based on muscular activity indices: a clinical feasibility study.

BACKGROUND: Restoring shoulder function is critical for upper-extremity rehabilitation following a stroke. The complex musculoskeletal anatomy of the shoulder presents a challenge for safely assisting elevation movements through robotic interventions. The level of shoulder elevation assistance in rehabilitation is often based on clinical judgment. There is no standardized method for deriving an optimal level of assistance, underscoring the importance of addressing abnormal movements during shoulder elevation, such as abnormal synergies and compensatory actions. This study aimed to investigate the effectiveness and safety of a newly developed shoulder elevation exoskeleton robot by applying a novel optimization technique derived from the muscle synergy index. METHODS: Twelve chronic stroke participants underwent an intervention consisting of 100 robot-assisted shoulder elevation exercises (10 × 10 times, approximately 40 min) for 10 days (4-5 times/week). The optimal robot assist rate was derived by detecting the change points using the co-contraction index, calculated from electromyogram (EMG) data obtained from the anterior deltoid and biceps brachii muscles during shoulder elevation at the initial evaluation. The primary outcomes were the Fugl-Meyer assessment-upper extremity (FMA-UE) shoulder/elbow/forearm score, kinematic outcomes (maximum angle of voluntary shoulder flexion and elbow flexion ratio during shoulder elevation), and shoulder pain outcomes (pain-free passive shoulder flexion range of motion [ROM] and visual analogue scale for pain severity during shoulder flexion). The effectiveness and safety of robotic therapy were examined using the Wilcoxon signed-rank sum test. RESULTS: All 12 patients completed the procedure without any adverse events. Two participants were excluded from the analysis because the EMG of the biceps brachii was not obtained. Ten participants (five men and five women; mean age: 57.0 [5.5] years; mean FMA-UE total score: 18.7 [10.5] points) showed significant improvement in the FMA-UE shoulder/elbow/forearm score, kinematic outcomes, and pain-free passive shoulder flexion ROM (P < 0.05). The shoulder pain outcomes remained unchanged or improved in all patients. CONCLUSIONS: The study presents a method for deriving the optimal robotic assist rate. Rehabilitation using a shoulder robot based on this derived optimal assist rate showed the possibility of safely improving the upper-extremity function in patients with severe stroke in the chronic phase.

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data.

BACKGROUND: In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method. METHODS: A total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification. RESULTS: Seven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores. CONCLUSION: This study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

Effects of static exercises on hip muscle fatigue and knee wobble assessed by surface electromyography and inertial measurement unit data.

Hip muscle weakness can be a precursor to or a result of lower limb injuries. Assessment of hip muscle strength and muscle motor fatigue in the clinic is important for diagnosing and treating hip-related impairments. Muscle motor fatigue can be assessed with surface electromyography (sEMG), however sEMG requires specialized equipment and training. Inertial measurement units (IMUs) are wearable devices used to measure human motion, yet it remains unclear if they can be used as a low-cost alternative method to measure hip muscle fatigue. The goals of this work were to (1) identify which of five pre-selected exercises most consistently and effectively elicited muscle fatigue in the gluteus maximus, gluteus medius, and rectus femoris muscles and (2) determine the relationship between muscle fatigue using sEMG sensors and knee wobble using an IMU device. This work suggests that a wall sit and single leg knee raise activity fatigue the gluteus medius, gluteus maximus, and rectus femoris muscles most reliably (p < 0.05) and that the gluteus medius and gluteus maximus muscles were fatigued to a greater extent than the rectus femoris (p = 0.031 and p = 0.0023, respectively). Additionally, while acceleration data from a single IMU placed on the knee suggested that more knee wobble may be an indicator of muscle fatigue, this single IMU is not capable of reliably assessing fatigue level. These results suggest the wall sit activity could be used as simple, static exercise to elicit hip muscle fatigue in the clinic, and that assessment of knee wobble in addition to other IMU measures could potentially be used to infer muscle fatigue under controlled conditions. Future work examining the relationship between IMU data, muscle fatigue, and multi-limb dynamics should be explored to develop an accessible, low-cost, fast and standardized method to measure fatiguability of the hip muscles in the clinic.

The role of electrodiagnosis in focal neuropathies.

Electrodiagnostic (EDX) testing plays an important role in confirming a mononeuropathy, localizing the site of nerve injury, defining the pathophysiology, and assessing the severity and prognosis. The combination of nerve conduction studies (NCS) and needle electromyography findings provides the necessary information to fully assess a nerve. The pattern of NCS abnormalities reflects the underlying pathophysiology, with focal slowing or conduction block in neuropraxic injuries and reduced amplitudes in axonotmetic injuries. Needle electromyography findings, including spontaneous activity and voluntary motor unit potential changes, complement the NCS findings and further characterize chronicity and degree of axon loss and reinnervation. EDX is used as an objective marker to follow the progression of a mononeuropathy over time.

Combining enhanced spectral resolution of EMG and a deep learning approach for knee pathology diagnosis.

Knee osteoarthritis (OA) is a prevalent, debilitating joint condition primarily affecting the elderly. This investigation aims to develop an electromyography (EMG)-based method for diagnosing knee pathologies. EMG signals of the muscles surrounding the knee joint were examined and recorded. The principal components of the proposed method were preprocessing, high-order spectral analysis (HOSA), and diagnosis/recognition through deep learning. EMG signals from individuals with normal and OA knees while walking were extracted from a publicly available database. This examination focused on the quadriceps femoris, the medial gastrocnemius, the rectus femoris, the semitendinosus, and the vastus medialis. Filtration and rectification were utilized beforehand to eradicate noise and smooth EMG signals. Signals' higher-order spectra were analyzed with HOSA to obtain information about nonlinear interactions and phase coupling. Initially, the bicoherence representation of EMG signals was devised. The resulting images were fed into a deep-learning system for identification and analysis. A deep learning algorithm using adapted ResNet101 CNN model examined the images to determine whether the EMG signals were conventional or indicative of knee osteoarthritis. The validated test results demonstrated high accuracy and robust metrics, indicating that the proposed method is effective. The medial gastrocnemius (MG) muscle was able to distinguish Knee osteoarthritis (KOA) patients from normal with 96.3±1.7% accuracy and 0.994±0.008 AUC. MG has the highest prediction accuracy of KOA and can be used as the muscle of interest in future analysis. Despite the proposed method's superiority, some limitations still require special consideration and will be addressed in future research.

Contribution of neurophysiology to the diagnosis and monitoring of ALS.

This chapter describes the role of neurophysiological techniques in diagnosing and monitoring amyotrophic lateral sclerosis (ALS). Despite many advances, electromyography (EMG) remains a keystone investigation from which to build support for a diagnosis of ALS, demonstrating the pathophysiological processes of motor unit hyperexcitability, denervation and reinnervation. We consider development of the different diagnostic criteria and the role of EMG therein. While not formally recognised by established diagnostic criteria, we discuss the pioneering studies that have demonstrated the diagnostic potential of transcranial magnetic stimulation (TMS) of the motor cortex and highlight the growing evidence for TMS in the diagnostic process. Finally, accurately monitoring disease progression is crucial for the successful implementation of clinical trials. Neurophysiological measures of disease state have been incorporated into clinical trials for over 20 years and we review prominent techniques for assessing disease progression.

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition.

Discrete myoelectric control-based gesture recognition has recently gained interest as a possible input modality for many emerging ubiquitous computing applications. Unlike the continuous control commonly employed in powered prostheses, discrete systems seek to recognize the dynamic sequences associated with gestures to generate event-based inputs. More akin to those used in general-purpose human-computer interaction, these could include, for example, a flick of the wrist to dismiss a phone call or a double tap of the index finger and thumb to silence an alarm. Moelectric control systems have been shown to achieve near-perfect classification accuracy, but in highly constrained offline settings. Real-world, online systems are subject to 'confounding factors' (i.e. factors that hinder the real-world robustness of myoelectric control that are not accounted for during typical offline analyses), which inevitably degrade system performance, limiting their practical use. Although these factors have been widely studied in continuous prosthesis control, there has been little exploration of their impacts on discrete myoelectric control systems for emerging applications and use cases. Correspondingly, this work examines, for the first time, three confounding factors and their effect on the robustness of discrete myoelectric control: (1)limb position variability, (2)cross-day use, and a newly identified confound faced by discrete systems (3)gesture elicitation speed. Results from four different discrete myoelectric control architectures: (1) Majority Vote LDA, (2) Dynamic Time Warping, (3) an LSTM network trained with Cross Entropy, and (4) an LSTM network trained with Contrastive Learning, show that classification accuracy is significantly degraded (p<0.05) as a result of each of these confounds. This work establishes that confounding factors are a critical barrier that must be addressed to enable the real-world adoption of discrete myoelectric control for robust and reliable gesture recognition.

Utility of single fibre electromyography (SFEMG)and motor unit number estimation technique (MUNE) in the diagnosis and differentiation of polyneuropathies of different aetiology.

OBJECTIVE: To determine whether single fibre electromyography and motor unit number index can distinguish between axonal and myelin lesions in polyneuropathies. METHODS: This case-control study was conducted at the Department of Medical Physiology, School of Medicine, University of Duhok, Iraq, and the Neurophysiology Department, Hawler Teaching Hospital, Erbil, Iraq, from January 2021 to March 2022. Group A had patients diagnosed with polyneuropathy regardless of the aetiology, while group B had age-matched healthy controls. Both groups were subjected to single fibre electromyography and motor unit number index as well as conventional nerve conduction study and concentric needle electromyography. Data was analysed using SPSS 26. RESULTS: Of the 140 subjects, 60(43%) were patients in group A; 40(67%) males and 20(33%) females with mean age 55.3±7.2 years. There were 80(57%) controls in group B; 43(54%) females and 37(46%) males with mean age 53.81±7.15. Group A had significantly higher single fibre electromyography jitter, and mean consecutive difference (MCD) values than group B (p<0.05). Group A patients with axonal polyneuropathy had a higher mean jitter (MCD) value (36.476.7ms) than those with demyelinating polyneuropathy (23.262.31 ms) (P <0.05). Patients in group A had a motor unit number index value with a significantly lower mean value (p<0.05) when compared to the controls. Axonal polyneuropathy patients had a lower MUNIX value (99.612.8) than demyelinating polyneuropathy patients (149.845.7) (P< 0.05). CONCLUSIONS: Single fibre electromyography and motor unit number index could help differentiate between the pathophysiology of axonal and demyelinating polyneuropathy.

Intermuscular coherence as an early biomarker for amyotrophic lateral sclerosis: The protocol for a prospective, multicenter study.

OBJECTIVE: To describe the protocol of a prospective study to test the validity of intermuscular coherence (IMC) as a diagnostic tool and biomarker of upper motor neuron degeneration in amyotrophic lateral sclerosis (ALS). METHODS: This is a multicenter, prospective study. IMC of muscle pairs in the upper and lower limbs is gathered in ∼650 subjects across three groups using surface electrodes and conventional electromyography (EMG) machines. The following subjects will be tested: 1) neurotypical controls; 2) patients with symptomatology suggestive for early ALS but not meeting probable or definite ALS by Awaji Criteria; 3) patients with a known ALS mimic. The recruitment period is between 3/31/2021 and 12/31/2025. Written consent will be sought from the subject or the subject's legally authorized representative during enrollment. RESULTS: The endpoints of this study include: 1) whether adding IMC to the Awaji ALS criteria improve its sensitivity in early ALS and can allow for diagnosis earlier; 2) constructing a database of IMC across different ages, genders, and ethnicities. SIGNIFICANCE: This study may validate a new inexpensive, painless, and widely available tool for the diagnosis of ALS.

Performance in myoelectric pattern recognition improves with transcranial direct current stimulation.

Sensorimotor impairments, resulting from conditions like stroke and amputations, can profoundly impact an individual's functional abilities and overall quality of life. Assistive and rehabilitation devices such as prostheses, exo-skeletons, and serious gaming in virtual environments can help to restore some degree of function and alleviate pain after sensorimotor impairments. Myoelectric pattern recognition (MPR) has gained popularity in the past decades as it provides superior control over said devices, and therefore efforts to facilitate and improve performance in MPR can result in better rehabilitation outcomes. One possibility to enhance MPR is to employ transcranial direct current stimulation (tDCS) to facilitate motor learning. Twelve healthy able-bodied individuals participated in this crossover study to determine the effect of tDCS on MPR performance. Baseline training was followed by two sessions of either sham or anodal tDCS using the dominant and non-dominant arms. Assignments were randomized, and the MPR task consisted of 11 different hand/wrist movements, including rest or no movement. Surface electrodes were used to record EMG and the MPR open-source platform, BioPatRec, was used for decoding motor volition in real-time. The motion test was used to evaluate performance. We hypothesized that using anodal tDCS to increase the excitability of the primary motor cortex associated with non-dominant side in able-bodied individuals, will improve motor learning and thus MPR performance. Overall, we found that tDCS enhanced MPR performance, particularly in the non-dominant side. We were able to reject the null hypothesis and improvements in the motion test's completion rate during tDCS (28% change, p-value: 0.023) indicate its potential as an adjunctive tool to enhance MPR and motor learning. tDCS appears promising as a tool to enhance the learning phase of using assistive devices using MPR, such as myoelectric prostheses.

Relevant Biomechanical Variables in Skateboarding: A Literature Review.

Skateboarding, once regarded primarily as a means of transportation and entertainment for youth, has become a recognized professional sport, gaining global popularity. With its recent inclusion in the Olympics, a growing imperative exists to comprehensively understand biomechanics explaining skateboarding performance. This literature review seeks to consolidate knowledge within this domain, focusing on experimental and modeling studies about skateboard riding and tricks. The criteria for study selection encompassed content relevance and publication year, spanning from the last two decades and extending further back to 1980 following cross-referencing of seminal works. Peer-reviewed journal articles, conference proceedings, and books were considered, with comprehensive searches conducted on electronic databases, including SCOPUS, PubMed, Scielo, and Taylor & Francis. Comprehending the biomechanical facets of skateboarding is essential in promoting its use and ensuring safety among all practitioners. Insights into factors such as body kinetics, kinematics, and muscle activation represent a foundational step toward understanding the nuances of this sport with implications for both clinical and biomechanical research. Modern data collection systems such as inertial measurement units (IMU) and electromyography (EMG) offer unprecedented insights into human performance during skateboarding, such as joint range of motion, coordination, and muscle activation, whether in casual riding or executing complex tricks and maneuvers. Developing robust modeling approaches also holds promise for enhancing skateboarding training and performance. Crucially, these models can serve as the initial framework for understanding injury mechanisms and implementing strategies to improve performance and mitigate injury risks.

Electromyography as a tool to motion analysis for people with Amyotrophic Lateral Sclerosis: A protocol for a systematic review.

Biomechanical analysis of human movement plays an essential role in understanding functional changes in people with Amyotrophic Lateral Sclerosis (ALS), providing information on muscle impairment. Studies suggest that surface electromyography (sEMG) may be able to quantify muscle activity, identify levels of fatigue, assess muscle strength, and monitor variation in limb movement. In this article, a systematic review protocol will analyze the psychometric properties of the sEMG regarding the clinical data on the skeletal muscles of people with ALS. This protocol uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological tool. A specific field structure was defined to reach each phase. Nine scientific databases (PubMed, Web of Science, Embase, Elsevier, IEEE, Google Scholar, SciELO, PEDro, LILACS E CENTRAL) were searched. The framework developed will extract data (i.e. study information, sample information, sEMG information, intervention, and outcomes) from the selected studies using a rigorous approach. The data will be described quantitatively using frequency and trend analysis methods, and heterogeneity between the included studies will be assessed using the I2 test. The risk of bias will be summarized using the most recent prediction model risk of bias assessment tool. Be sure to include relevant statistics here, such as sample sizes, response rates, P values or Confidence Intervals. Be specific (by stating the value) rather than general (eg, "there were differences between the groups"). This protocol will map out the construction of a systematic review that will identify and synthesize the advances in movement analysis of people with ALS through sEMG, using data extracted from articles.

OECT - Inspired electrical detection.

Organic Electrochemical Transistors (OECTs) are integral in detecting human bioelectric signals, attributing their significance to distinct electrochemical properties, the utilization of soft materials, compact dimensions, and pronounced biocompatibility. This review traverses the technological evolution of OECT, highlighting its profound impact on non-invasive detection methodologies within the biomedicalfield. Four sensor types rooted in OECT technology were introduced: Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyography (EMG), and Electrooculography (EOG), which hold promise for integration into wearable detection systems. The fundamental detection principles, material compositions, and functional attributes of these sensors are examined. Additionally, the performance metrics and delineates viable optimization strategies for assorted physiological electrical detection sensors are discussed. The overarching goal of this review is to foster deeper insights into the generation, propagation, and modulation of electrophysiological signals, thereby advancing the application and development of OECT in medical sciences.

Investigating cortical excitability and inhibition in patients with schizophrenia: A TMS-EEG study.

BACKGROUND: Transcranial magnetic stimulation (TMS) combined with electromyography (EMG) has widely been used as a non-invasive brain stimulation tool to assess excitation/inhibition (E/I) balance. E/I imbalance is a putative mechanism underlying symptoms in patients with schizophrenia. Combined TMS-electroencephalography (TMS-EEG) provides a detailed examination of cortical excitability to assess the pathophysiology of schizophrenia. This study aimed to investigate differences in TMS-evoked potentials (TEPs), TMS-related spectral perturbations (TRSP) and intertrial coherence (ITC) between patients with schizophrenia and healthy controls. MATERIALS AND METHODS: TMS was applied over the motor cortex during EEG recording. Differences in TEPs, TRSP and ITC between the patient and healthy subjects were analysed for all electrodes at each time point, by applying multiple independent sample t-tests with a cluster-based permutation analysis to correct for multiple comparisons. RESULTS: Patients demonstrated significantly reduced amplitudes of early and late TEP components compared to healthy controls. Patients also showed a significant reduction of early delta (50-160 ms) and theta TRSP (30-250ms),followed by a reduction in alpha and beta suppression (220-560 ms; 190-420 ms). Patients showed a reduction of both early (50-110 ms) gamma increase and later (180-230 ms) gamma suppression. Finally, the ITC was significantly lower in patients in the alpha band, from 30 to 260 ms. CONCLUSION: Our findings support the putative role of impaired GABA-receptor mediated inhibition in schizophrenia impacting excitatory neurotransmission. Further studies can usefully elucidate mechanisms underlying specific symptoms clusters using TMS-EEG biometrics.

Decomposition strategy for surface EMG with few channels: a simulation study.

Objective.In the specific use of electromyogram (EMG) driven prosthetics, the user's disability reduces the space available for the electrode array. We propose a framework for EMG decomposition adapted to the condition of a few channels (less than 30 observations), which can elevate the potential of prosthetics in terms of cost and applicability.Approach.The new framework contains a peel-off approach, a refining strategy for motor unit (MU) spike train and MU action potential and a re-subtracting strategy to adapt the framework to few channels environments. Simulated EMG signals were generated to test the framework. In addition, we quantify and analyze the effect of strategies used in the framework.Main results.The results show that the new algorithm has an average improvement of 19.97% in the number of MUs identified compared to the control algorithm. Quantitative analysis of the usage strategies shows that the re-subtracting and refining strategies can effectively improve the performance of the framework under the condition of few channels.Significance.These prove that the new framework can be applied to few channel conditions, providing a optimization space for neural interface design in cost and user adaptation.