基于颞肌表面肌电的鼠标装置研究

目的设计一种基于颞肌表面肌电的鼠标装置,用于解决高位截瘫、上肢残疾等手部功能障碍者操作鼠标的难题。方法提出一种基于表面肌电信号控制鼠标的方法,通过分析与试验,选取面部颞肌肌电信号作为控制信息,以咬牙作为触发动作,根据不同咬牙动作时,面部颞肌产生不同的肌电信号特征,设计一种利用颞肌表面肌电信号进行鼠标控制的装置。结果实验结果表明,利用该装置,受试者能够通过简单的咬牙动作实现控制鼠标的目的。结论利用颞肌表面肌电控制鼠标的方法是切实可行的,基于该方法设计的鼠标装置具有较好的应用推广价值。     

基于表面肌电相位同步分析的跌倒辨识研究

人体有意识的活动是由大脑皮层和运动神经肌肉组织两大体系内部及相互之间信息的同步化震荡实现的,本文通过分析各通道肌电信号相位同步性来区别有意识的日常活动和无意识的跌倒。实验肌电数据从5名健康受试者身上采集,在5名受试者完成4个不同动作(走路、跌倒、坐下、坐下站起)时,采集其胫骨前肌、腓肠肌、股直肌、半腱肌4路肌电信号。运用希尔伯特相位同步分析方法,计算相位同步指数。先用原始肌电信号对不同动作下各肌肉组间的同步性情况进行比较分析;再将肌电信号进行小波包分解,研究不同频段下肌肉间肌电信号同步性情况。实验表明,摔倒动作中胫骨前肌和股直肌以及股直肌和半腱肌肌电信号间的同步性情况与其他有意识动作中的情况有着明显差异。利用这一特征,用普通的fisher线性分类器对日常活动动作与跌倒进行判别,用全频段信号和所选频段信号对跌倒的识别率分别达到85.5%和91.0%,表明肌肉间相位同步性情况可以反映肌肉间的协同工作情况,可用于跌倒辩识。     

基于模糊自相似性的表面肌电信号分形分析

表面肌电信号(surface EMG,sEMG)是一种复杂的非线性信号.近年来,分形分析常被用来揭示这种非线性特征.本文采用一种基于模糊自相似性的分数维计算方法,来获取前臂执行四种动作时所对应的动作sEMG的分数维.结果表明,通过这种方法获得的分数维能够聚集在各自特定的范围内,并且,通过分形维能够区分部分动作sEMG.     

基于手臂表面肌电信号的智能小车控制系统

研究一种基于手臂表面肌电信号的智能小车控制系统。当左臂做出曲臂、左勾、右勾、自然下垂四种动作时,小车将完成前进、左转、右转、停止四种运动状态。手臂表面肌电信号采集模块通过三个单通道肌电信号传感器采集左臂四种动作下的肌电信号并进行预处理;在信号识别及指令编码模块对肌电信号进行分类识别,识别出手臂的不同动作,并编码出对应的控制指令。通过无线传输模块把控制指令传输给智能小车控制模块;智能小车控制模块根据控制指令驱动电机转动,从而实现对小车运动状态的调控。实验证明,本智能小车控制系统信号识别率高,延迟小。     

局部肌疲劳过程与表面肌电信号分维值变化关系的研究

利用肱二头肌在不同收缩力水平上持续恒力收缩时采集的表面肌电信号，研究局部肌疲劳过程中肌电信号的分形维变化规律。结果表明，随着疲劳程度的加深，表面肌电信号的分维值在不同收缩力水平上均呈下降趋势，与中值频率的下降趋势相一致。     

基于脑肌电融合的混合脑机接口研究

动作模式识别是脑机接口技术的核心内容之一。针对目前脑机接口动作识别模式单一、识别率低等问题,基于混合脑机接口思想,提出一种脑电和肌电特征融合策略,可实现单侧肢体不同动作模式的有效分类,进而可用于脑机接口技术。同步采集9名健康受试者单侧手腕屈/伸两种动作模式下的脑电信号和表面肌电信号,分别提取脑电信号事件相关去同步化特征和表面肌电信号的积分肌电值特征,构建基于支持向量机和粒子群优化算法的脑肌电融合及运动模式识别模型,通过调整“特征融合系数”来实现动作模式最优分类,从而提高模式识别的准确率;进一步通过递降健康人的肌电信号幅值来模拟患者和运动疲劳状态下的肌电信号,验证所提出方法对动作模式识别的有效性。实验结果表明,基于脑肌电融合特征的动作模式识别率(98%)比单纯依靠脑电特征的识别率(73%)提高25%;在运动疲劳状态下,基于脑肌电融合特征的识别率稳定在80%以上,比单纯依靠肌电特征的识别率提高14%。可见,脑肌电融合策略能提高动作模式识别的准确性和鲁棒性,为混合脑机接口技术提供条件。     

采集表面肌电信号应用于动作识别的可行性

背景：文献表明上肢前臂运动时所产生的表面肌电信号具有非线性特征,而肢体运动时肌电信号又呈现出非平稳特性。 目的：设计一种简单的拾取电路采集表面肌电信号,拟应用于动作肌电信号的特征识别。 方法：根据表面肌电信号的特点,设计高共模抑制比的前端放大电路,抑制共模干扰;采用低通滤波电路,有源双T带阻滤波器对信号进行去噪处理;对采集得到的信号进行小波包变换,得到信号的特征量。 结果与结论：所设计的表面肌电信号检测电路具有较高共模抑制比,并能有效地滤除50 Hz工频信号,可以满足肌电信号采集电路的基本要求。肌电信号的处理结果表明采用子频段能量值的方法可以区分手部4种不同动作。     

基于肌电信号的人手运动状态的辨识

研究的目的在于利用人体前臂的肌电信号进行人手动作模式的识别。根据采集的肌电信号，判断动作始末状态并对该肌电信号进行小波降噪预处理，利用小波变换的高频细节系数极值构造特征矢量，经过学习矢量量化（LVQ）神经网络训练，能够有效地识别握拳、展拳、手腕内旋和手腕外旋4种动作模式。和前馈型神经网络比较，LVQ神经网络具有更高的识别准确率和更稳定的再现性。     

基于小波分析与神经网络相结合的表面肌电信号识别的研究

表面肌电信号是从人体骨骼肌表面通过电极记录下来的神经肌肉活动发放的生物电信号,具有非平稳性和复杂性的特点。本研究通过使用小波分析与神经网络相结合的方法,识别正常肌电信号与疲劳肌电信号。实验表明,将小波分解后的肌电信号代替原始肌电信号,能明显提高神经网络对肌电信号的识别准确率。     

前臂肌电信号的测试与分析

研制有实用价值的前臂肌电控制假手,必须选择合宜的肌电信号源。本文通过对截肢者前臂残端肌群情况的调查分析,分别用针电极和表面电极测试正常人和前臂残肢者在自然下垂位、功能位做各种动作时的肌电信号,并进行时域和频域的分析比较,从而获得关于肌电信号的一些特征,进一步为前臂肌电控制假手之肌电信号源的选择提供了重要的依据。     

独立分量分析和小波熵在动作模式分类中的应用

在表面肌电信号(electromyography,EMG)中,各类动作的识别是一个重要研究方向.本文采用独立分量分析independent component analysis,ICA)对肌电信号进行处理,消除各动作信号之间的相互线性耦合叠加,并采用信号的小波熵作为特征向量进行模式识别.试验表明,在对信号进行先期ICA处理以后,动作模式的识别效果较好.此方法也可应用于其他生理信号的识别分类.     

Examination of the Diagnostic Value and Estimation of the Chaos Phenomenon in Temporomandibular Joint Sounds Using Fractal Dimension

.The aim of this study is to estimate the chaos phenomenon in temporomandibular joints (TMJ) sound using fractal dimension (FD), and to examine the diagnostic value of the FD in comparing TMJ sounds produced by 6 asymptomatic and 25 symptomatic TMJ. Multiple mandibular opening and closing cycles recorded were used to calculate the waveform dimension and correlation dimension in the FD. Chaos in the TMJ sounds was estimated by the FD that was saturated with some constant value to an increase of embedding dimension. Results reveal that fractal analysis produces a high degree of reproducibility within, and similarity across subjects, and indicate that both FD values of the asymptomatic TMJ sounds are significantly higher than those of the symptomatic. These findings suggest that chaos is present in TMJ sounds and the difference in the FD is of diagnostic value in evaluation of pathological change in TMJ sound signals.     

Chaotic analysis of electromyography signal at low back and lower limb muscles during forward bending posture

The present study aims to perform further investigation on muscular activity during forward bending posture by applying a nonlinear dynamic (chaotic) analysis method. The objective is to determine the characteristics of the low back and lower limb muscle electromyography (EMG) signal under chaotic analysis while maintaining a certain posture. Twelve subjects were asked to maintain postures of six stages in bending angles from 0 to 180 degrees, and the EMG signals of erector spinae (ES) at L1 and L5 levels, hamstrings, and gastrocnemius were recorded. Two important concepts to characterize deterministic chaos, Correlation Dimension (D2) and Lyapunov Exponents (lambda, LE), were applied to observe the chaotic characteristic of the EMG signals, and the results were also compared to the FFT based total power value. The EMG signals in all observed muscles during bending posture showed results of positive LE and high D2 at 5.5 to 7.5, which led us to classify EMG as a high dimension chaotic signal. The result obtained showed that the correlation dimension could be used as a reliable method to compare the EMG signal in various postures (or muscle contraction conditions). However, Lyapunov exponents did not show a significant difference of comparison result thus leading to the conclusion that LE could not be a reliable measure for high dimension chaotic system, such as an EMG signal. It was also shown that in both light and deep bending, the EMG signal of the low back muscles was of the same complexity level due to the D2 result. It was evident that somehow the low back muscle remained loaded in all bending stages which was contrary to the hypothesis that the low back muscle was less active during the deep bending, as was the case in most of the previous studies. The reason of such phenomenon was elucidated with use of the theory of muscular functional differentiation, including corticalization and spinalization.     

Feature dimensionality reduction for myoelectric pattern recognition: A comparison study of feature selection and feature projection methods

This study investigates the effect of the feature dimensionality reduction strategies on the classification of surface electromyography (EMG) signals toward developing a practical myoelectric control system. Two dimensionality reduction strategies, feature selection and feature projection, were tested on both EMG feature sets, respectively. A feature selection based myoelectric pattern recognition system was introduced to select the features by eliminating the redundant features of EMG recordings instead of directly choosing a subset of EMG channels. The Markov random field (MRF) method and a forward orthogonal search algorithm were employed to evaluate the contribution of each individual feature to the classification, respectively. Our results from 15 healthy subjects indicate that, with a feature selection analysis, independent of the type of feature set, across all subjects high overall accuracies can be achieved in classification of seven different forearm motions with a small number of top ranked original EMG features obtained from the forearm muscles (average overall classification accuracy >95% with 12 selected EMG features). Compared to various feature dimensionality reduction techniques in myoelectric pattern recognition, the proposed filter-based feature selection approach is independent of the type of classification algorithms and features, which can effectively reduce the redundant information not only across different channels, but also cross different features in the same channel. This may enable robust EMG feature dimensionality reduction without needing to change ongoing, practical use of classification algorithms, an important step toward clinical utility.     

Automatic identification of motor unit action potential trains from electromyographic signals using fuzzy techniques

A technique is proposed that allows automatic decomposition of electromyographic (EMG) signals into their constituent motor unit action potential trains (MUAPTs). A specific iterative algorithm with a classification method using fuzzy-logic techniques was developed. The proposed classification method takes into account imprecise information, such as waveform instability and irregular firing patterns, that is often encountered in EMG signals. Classification features were determined by the combining of time position and waveform information. Statistical analysis of inter-pulse intervals and spike amplitude provided an accurate estimation of features used in the classification step. Algorithm performance was evaluated using simulated EMG signals composed of up to six different discharging motor units corrupted with white noise. The algorithm was then applied to real signals recorded by a high spatial resolution surface EMG device based on a Laplacian spatial filter. On six groups of 20 simulated signals, the decomposition algorithm performed with a maximum and an average mean error rate of 2.13% and 1.37%, respectively. On real surface EMG signals recorded at different force levels (from 10% to 40% of the maximum voluntary contraction), the algorithm correctly identified 21 MUAPTs, compared with the 29 MUAPTs identified by an experienced neurophysiologist. The efficiency of the decomposition on surface EMG signals makes this method very attractive for non-invasive investigation of physiological muscle properties. However, it can also be used to decompose intramuscularly recorded EMG signals.     

Relating biceps EMG to elbow kinematics during self-paced arm flexions

Repetitive reaching movements to a fixed target can be generally characterized by bell-shaped velocity profiles and sigmoidal trajectories with variable morphologies across multiple repetitions. A neuromuscular correspondence of these kinematic variations has thus far eluded electromyographic (EMG) analysis. We recorded EMG and elbow kinematics from fourteen healthy individuals performing repetitive, self-paced, isolated elbow flexions, with their arms supported against gravity. The global kinematic pattern of each flexion was classified as either sigmoidal (S) or non-sigmoidal (NS), based on goodness of fit with analytical curves. Ten of the fourteen subjects generated an approximately equal number of S and NS types (383 movement cycles). Trajectories of the other four subjects were not classifiable or did not vary sufficiently and were excluded from subsequent analysis. A post hoc predictor of trajectory type was derived by testing linear support vector machines trained with a strategically selected 3-feature sub-space of the early phase of enveloped biceps EMG during a leave-one-out cross-validation paradigm. Results showed that EMG features predicted kinematic morphology with sensitivity and specificity both exceeding 80%. The high predictive accuracy suggests neuromotor signals coding for subtle variations in elbow kinematics during self-paced, unloaded motions, can be deciphered from the biceps EMG.     

Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines

The motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide a significant source of information for the assessment of neuromuscular disorders. In this work, different types of machine learning methods were used to classify EMG signals and compared in relation to their accuracy in classification of EMG signals. The models automatically classify the EMG signals into normal, neurogenic or myopathic. The best averaged performance over 10 runs of randomized cross-validation is also obtained by different classification models. Some conclusions concerning the impacts of features on the EMG signal classification were obtained through analysis of the classification techniques. The comparative analysis suggests that the fuzzy support vector machines (FSVM) modelling is superior to the other machine learning methods in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. The combined model with discrete wavelet transform (DWT) and FSVM achieves the better performance for internal cross validation (External cross validation) with the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy equal to 0.996 (0.970) and 97.67% (93.5%), respectively. These results show that the proposed model have the potential to obtain a reliable classification of EMG signals, and to assist the clinicians for making a correct diagnosis of neuromuscular disorders.     

Comparisons between surface electrodes and intramuscular wire electrodes in isometric and dynamic conditions

The purpose of this study was to compare EMG surface electrodes (SE) and intramuscular wire electrodes (IWE) for isometric and dynamic contractions during an occupational cervico-brachial working task (OCWT). Six normal adult male subjects were tested on two days (two conditions with three trials each). Raw EMG signals from middle deltoid, anterior deltoid and trapezius muscles were recorded by both IWE and SE for two conditions (isometric and dynamic contractions). Full wave rectified and low pass filtered EMG, and integrated EMG were processed from raw EMG signals. The statistical analysis performed on the integrated EMG was a factorial analysis model with repeated measures. Statistical results confirmed that EMG signals, from both SE and IWE, are reliable between trials on the same day. These statistical results also confirmed that SE are more reliable than IWE on day-to-day investigations. Both electrodes recorded statistically similar signals, although the coefficient of variability between electrodes was very high (STDE%; 48% and 84%, for isometric and dynamic conditions respectively).