To advance the motor unit number estimation (MUNE) technique using high density surface electromyography (EMG) decomposition. The K-means clustering convolution kernel compensation algorithm was employed to detect the single motor unit potentials (SMUPs) from high-density surface EMG recordings of the biceps brachii muscles in eight healthy subjects. Contraction forces were controlled at 10%, 20% and 30% of the maximal voluntary contraction (MVC). Achieved MUNE results and the representativeness of the SMUP pools were evaluated using a high-density weighted-average method. Mean numbers of motor units were estimated as 288±132, 155±87, 107±99 and 132±61 by using the developed new MUNE at 10%, 20%, 30% and 10-30% MVCs, respectively. Over 20 SMUPs were obtained at each contraction level, and the mean residual variances were lower than 10%. The new MUNE method allows a convenient and non-invasive collection of a large size of SMUP pool with great representativeness. It provides a useful tool for estimating the motor unit number of proximal muscles. The present new MUNE method successfully avoids the use of intramuscular electrodes or multiple electrical stimuli which is required in currently available MUNE techniques; as such the new MUNE method can minimize patient discomfort for MUNE tests.

This study aimed to evaluate the utility of sample entropy (SampEn) of surface electromyographic signals (sEMG) in quantifying neuromuscular changes after spinal cord injury (SCI). Using a linear electrode array, sEMG signals were obtained from the biceps brachii muscles of 13 healthy control subjects and 13 SCI subjects during isometric elbow flexion muscle contraction at various constant force levels. The SampEn analysis was performed on both the representative channel (emerging the largest signal amplitude) and the channel over muscle innervation zone (IZ) determined by the linear array. The SampEn values were further averaged across muscle force levels to examine difference between SCI survivors and control subjects. Results showed that SampEn values after SCI had a significantly greater range than those of the control subjects at the group level. Abnormal increases or decreases in SampEn were also found after SCI at the individual subject level. In addition, a significant difference was found between the representative channel and the IZ channel. SampEn is a valuable indicator for identifying neuromuscular changes after SCI, and the effect of the IZ on the sEMG examination is particularly noteworthy. The approach presented in this study may facilitate the development of appropriate rehabilitation methods to enhance motor recovery.

Background: In motor imagery brain–computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing the limitations of traditional methods in capturing linear features. However, the multitude of entropy types leads to unclear application scenarios, with a lack of systematic descriptions. Methods: This study conducted a review of 63 high-quality research articles focused on the application of entropy in MI-BCI, published between 2019 and 2023. It summarizes the names, functions, and application scopes of 13 commonly used entropy measures. Results: The findings indicate that sample entropy (16.3%), Shannon entropy (13%), fuzzy entropy (12%), permutation entropy (9.8%), and approximate entropy (7.6%) are the most frequently utilized entropy features in MI-BCI. The majority of studies employ a single entropy feature (79.7%), with dual entropy (9.4%) and triple entropy (4.7%) being the most prevalent combinations in multiple entropy applications. The incorporation of entropy features can significantly enhance pattern classification accuracy (by 8–10%). Most studies (67%) utilize public datasets for classification verification, while a minority design and conduct experiments (28%), and only 5% combine both methods. Conclusions: Future research should delve into the effects of various entropy features on specific problems to clarify their application scenarios. As research methodologies continue to evolve and advance, entropy features are poised to play a significant role in a wide array of fields and contexts.

Decoding movement intentions from motor unit (MU) activities remains an ongoing challenge, which restricts our comprehension of the intricate transition mechanism from microscopic neural drive to macroscopic movements. This study presents an innovative neuro-musculoskeletal (NMS) model driven by MU activities for online estimation of continuous wrist movements. The proposed model employs a physiological and comprehensive utilization of MU firings and waveforms, thus facilitating the localization of MUs to muscle-tendon units (MTU) as well as the computation of MU-specific neural excitation. Subsequently, the MU-specific neural excitation was integrated to form the MTU-specific neural excitation, which were then inputted into a musculoskeletal model to accomplish the joint angle estimation. To assess the effectiveness of this model, high-density surface electromyography and angular data were collected from the forearms of eight subjects during their performance of wrist flexion-extension task. Two pieces of 8 × 8 electrode arrays and a motion capture system were employed for data acquisition. Following offline model calibration with a global optimization algorithm, online angle estimation results demonstrated a significant superiority of the proposed model over the state-of-the-art NMS models ( p < 0.05), yielding the lowest normalized root mean square error (0.10 ± 0.02) and the highest determination coefficient (0.87 ± 0.06). This study provides a novel idea for the decoding of joint movements from MU activities. The research findings hold the potential to advance the development of NMS models towards the control of multiple degrees of freedom, with promising applications in the fields of motor control, biomechanics, and neuro-rehabilitation engineering.

A surface electromyography (EMG) analysis was performed in this study to examine central neural and peripheral muscle changes after a spinal cord injury (SCI). A linear electrode array was used to record surface EMG signals from the biceps brachii (BB) in 15 SCI subjects and 14 matched healthy control subjects as they performed elbow flexor isometric contractions from 10% to 80% maximum voluntary contraction. Muscle fiber conduction velocity (MFCV) and BB EMG–force relation were examined. MFCV was found to be significantly slower in the SCI group than the control group, evident at all force levels. The BB EMG–force relation was well fit by quadratic functions in both groups. All healthy control EMG–force relations were best fit with positive quadratic coefficients. In contrast, the EMG–force relation in eight SCI subjects was best fit with negative quadratic coefficients, suggesting impaired EMG modulation at high forces. The alterations in MFCV and EMG–force relation after SCI suggest complex neuromuscular changes after SCI, including alterations in central neural drive and muscle properties.

This study explores the important role of assessing force levels in accurately controlling upper limb movements in human–computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human–computer interface technologies.