A Reliable Muscle Synergy Extraction Method based on Multivariate Curve Resolution-Alternating Least Squares

Muscle synergy is an important approach to evaluate motor function for patients with neurological diseases. Nonnegative matrix factorization (NMF) is the most widely used muscle synergy extraction method from electromyography (EMG) data. However, NMF usually falls into local optimum and is susceptible to noise, which significantly limit the promotion of muscle synergy. In this paper, a reliable synergy extraction method based on multivariate curve resolution-alternating least squares (MCRALS) was put forward. Its performance was compared with NMF through analyzing the EMG data of upper limb motor. The repeatability and intra-subject consistency were used to evaluate the two methods. As a result, MCR-ALS provided unique resolution result and better repeatability and consistency in contrast to NMF. Thus, the results of this study are of significance for the expansion and application of muscle synergy in medicine

A comparative study of GNN and MLP based machine learning for the diagnosis of Alzheimer’s Disease involving data synthesis

Alzheimer’s Disease (AD) is a neurodegenerative disease that commonly occurs in older people. It is characterized by both cognitive and functional impairment. However, as AD has an unclear pathological cause, it can be hard to diagnose with confidence. This is even more so in the early stage of Mild Cognitive Impairment (MCI). This paper proposes a U-Net based Generative Adversarial Network (GAN) to synthesize fluorodeoxyglucose -positron emission tomography (FDG-PET) from magnetic resonance imaging - T1 weighted imaging (MRI-T1WI) for further usage in AD diagnosis including its early-stage MCI. The experiments have displayed promising results with Structural Similarity Index Measure (SSIM) reaching 0.9714. Furthermore, three types of classifiers are developed, i.e., one Multi-Layer Perceptron (MLP) based classifier, two Graph Neural Network (GNN) based classifiers where one is for graph classification and the other is for node classification. 10-fold cross-validation has been conducted on all trials of experiments for classifier comparison. The performance of these three types of classifiers has been compared with the different input modalities setting and data fusion strategies. The results have shown that GNN based node classifier surpasses the other two types of classifiers, and has achieved the state-of-the-art (SOTA) performance with the best accuracy at 90.18% for 3-class classification, namely AD, MCI and normal control (NC) with the synthesized fluorodeoxyglucose - positron emission tomography (FDG-PET) features fused at the input level. Moreover, involving synthesized FDG-PET as part of the input with proper data fusion strategies has also proved to enhance all three types of classifiers’ performance. This work provides support for the notion that machine learning-derived image analysis may be a useful approach to improving the diagnosis of AD

MCR-ALS-based muscle synergy extraction method combined with LSTM neural network for motion intention detection

IntroductionThe time-varying and individual variability of surface electromyographic signals (sEMG) can lead to poorer motor intention detection results from different subjects and longer temporal intervals between training and testing datasets. The consistency of using muscle synergy between the same tasks may be beneficial to improve the detection accuracy over long time ranges. However, the conventional muscle synergy extraction methods, such as non-negative matrix factorization (NMF) and principal component analysis (PCA) have some limitations in the field of motor intention detection, especially in the continuous estimation of upper limb joint angles.MethodsIn this study, we proposed a reliable multivariate curve-resolved-alternating least squares (MCR-ALS) muscle synergy extraction method combined with long-short term memory neural network (LSTM) to estimate continuous elbow joint motion by using the sEMG datasets from different subjects and different days. The pre-processed sEMG signals were then decomposed into muscle synergies by MCR-ALS, NMF and PCA methods, and the decomposed muscle activation matrices were used as sEMG features. The sEMG features and elbow joint angular signals were input to LSTM to establish a neural network model. Finally, the established neural network models were tested by using sEMG dataset from different subjects and different days, and the detection accuracy was measured by correlation coefficient.ResultsThe detection accuracy of elbow joint angle was more than 85% by using the proposed method. This result was significantly higher than the detection accuracies obtained by using NMF and PCA methods. The results showed that the proposed method can improve the accuracy of motor intention detection results from different subjects and different acquisition timepoints.DiscussionThis study successfully improves the robustness of sEMG signals in neural network applications using an innovative muscle synergy extraction method. It contributes to the application of human physiological signals in human-machine interaction

The effect of on-line correction on stiffness of human upper limb during goal-directed movement

Objective To study the relationship between on-line correction and stiffness of the upper limb during human movements,so as to improve the measurement accuracy of stiffness and to assess the on-line correction capability. Methods Five kinds of upper limb goal-directed movements in a horizontal plane were designed. The stiffness values at 5 different positions,i. e. in the early period,early to mid period,mid period,mid to late period and late period separately during the movements with sudden perturbation were measured to investigate the regular pattern of human hand stiffness influenced by such on-line correction,as well as the relationship between the movement accuracy and hand stiffness. Results The stiffness was always varying during the movements,and the variation of the stiffness would influence the movement error. On-line correction during the movements could induce an increase in the value of stiffness amplitude,especially at the position in late period of the movement. However,no significant linkage was found between the change of stiffness and the occurrence time or content of on-line correction. Conclusions On-line correction plays an important role in goal-directed movements. Considering that on-line correction may cause a change in the amplitude of the stiffness,the on-line correction function of patients can be more accurately assessed by measuring stiffness value in specific experiments,combined with other medical diagnosis methods in clinic

基于互信息与主成分分析的运动想象脑电特征选择算法

Aiming at feature selection problem of motor imagery task in brain computer interface (BCI), an algorithm based on mutual information and principal component analysis (PCA) for electroencephalogram (EEG) feature selection is presented. This algorithm introduces the category information, and uses the sum of mutual information matrices between features under different motor imagery category to replace the covariance matrix. The eigenvectors of the sum matrix represent the direction of the principal components and the eigenvalues of the sum matrix are used to determine the dimensionality of principal components. 2005 International BCI competition data set was used in our experiments, and four feature extraction methods were adopted, i. e. power spectrum estimation, continuous wavelet transform, wavelet packet decomposition and Hjorth parameters. The proposed feature selection algorithm was adopted to select and combine the most useful features for classification. The results showed that relative to the PCA algorithm, our algorithm had better performance in dimensionality reduction and in classification accuracy with the assistance of support vector machine classifier under the same dimensionality of principal components

A study on EEG differences between active counting and focused breathing tasks for more sensitive detection of consciousness

IntroductionIn studies on consciousness detection for patients with disorders of consciousness, difference comparison of EEG responses based on active and passive task modes is difficult to sensitively detect patients’ consciousness, while a single potential analysis of EEG responses cannot comprehensively and accurately determine patients’ consciousness status. Therefore, in this paper, we designed a new consciousness detection paradigm based on a multi-stage cognitive task that could induce a series of event-related potentials and ERD/ERS phenomena reflecting different consciousness contents. A simple and direct task of paying attention to breathing was designed, and a comprehensive evaluation of consciousness level was conducted using multi-feature joint analysis.MethodsWe recorded the EEG responses of 20 healthy subjects in three modes and reported the consciousness-related mean event-related potential amplitude, ERD/ERS phenomena, and the classification accuracy, sensitivity, and specificity of the EEG responses under different conditions.ResultsThe results showed that the EEG responses of the subjects under different conditions were significantly different in the time domain and time-frequency domain. Compared with the passive mode, the amplitudes of the event-related potentials in the breathing mode were further reduced, and the theta-ERS and alpha-ERD phenomena in the frontal region were further weakened. The breathing mode showed greater distinguishability from the active mode in machine learning-based classification.DiscussionBy analyzing multiple features of EEG responses in different modes and stimuli, it is expected to achieve more sensitive and accurate consciousness detection. This study can provide a new idea for the design of consciousness detection methods

Sensorless force estimation of end-effect upper limb rehabilitation robot system with friction compensation

Sensorless force estimation of end-effect upper limb rehabilitation robot system with friction compensatio

Hardware-In-the-Loop Simulation System In the Development of Temperature Controller of Blood Glucose Meter

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DL-2-amino-3-phosphonopropionic acid protects primary neurons from oxygen-glucose deprivation induced injury

Cerebral infarction is a type of ischemic stroke and is one of the main causes of irreversible brain damage. Although multiple neuroprotective agents have been investigated recently, the potential of DL-2-amino-3-phosphonopropionic acid (DL-AP3) in treating oxygen-glucose deprivation (OGD)-induced neuronal injury, has not been clarified yet. This study was aimed to explore the role of DL-AP3 in primary neuronal cell cultures. Primary neurons were divided into four groups: (1) a control group that was not treated; (2) DL-AP3 group treated with 10 μM of DL-AP3; (3) OGD group, in which neurons were cultured under OGD conditions; and (4) OGD + DL-AP3 group, in which OGD model was first established and then the cells were treated with 10 μM of DL-AP3. Neuronal viability and apoptosis were measured using Cell Counting Kit-8 and flow cytometry. Expressions of phospho-Akt1 (p-Akt1) and cytochrome c were detected using Western blot. The results showed that DL-AP3 did not affect neuronal viability and apoptosis in DL-AP3 group, nor it changed p-Akt1 and cytochrome c expression (p > 0.05). In OGD + DL-AP3 group, DL-AP3 significantly attenuated the inhibitory effects of OGD on neuronal viability (p < 0.001), and reduced OGD induced apoptosis (p < 0.01). Additionally, the down-regulation of p-Akt1 and up-regulation of cytochrome c, induced by OGD, were recovered to some extent after DL-AP3 treatment (p < 0.05 or p < 0.001). Overall, DL-AP3 could protect primary neurons from OGD-induced injury by affecting the viability and apoptosis of neurons, and by regulating the expressions of p-Akt1 and cytochrome c