The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part II-modelling interpretation.

The purpose of this article was to investigate whether or not FastICA can separate identical motor unit action potential trains (MUAPTs) of the 8-channel surface electromyographic (sEMG) signals constructed by an sEMG model into the independent components. Firstly, we have examined how much the increase of motor units (MUs) in the simulated sEMG signals influenced the performance on the separation of MUAPTs by kurtosis. The decreased trend of mean kurtosis on both sEMG signals and their independent components were observed as MUs were increased. These data suggested that the separation performance decayed when MUs were increased. Secondary, the differences between the independent components and the principal components have been also applied to the simulated sEMG signals with or without time delay between the sEMG channels. FastICA could successfully separate identical MUAPTs with no time delay but principal component analysis (PCA) could not do so. Against it, both FastICA and PCA could not separate MUAPTs with some time delay. In conclusion, our results suggested that FastICA could separate identical MUAPTs with no time delay into the independent components by FastICA, which might offer a new technique for the separation of interfered MUAP waveforms based on statistical properties of sEMG signal distributions.     

空间独立成分分析实现fMRI信号的盲分离

独立成分分析（ICA）在功能核磁共振成像（fMRI)技术中的应用是近年来人们关注的一个热点。简要介绍了空间独立成分分析（SICA）的模型和方法,将fMRI信号分析看作是一种盲源分离问题,用快速算法实现fMRI信号的盲源分离。对fMRI信号的研究大多是在假定已知事件相关时间过程曲线的情况下,利用相关性分析得到脑的激活区域。在不清楚有哪几种因素对fMRI信号有贡献、也不清楚其时间过程曲线的情况下,用SICA可以对fMRI信号进行盲源分离,提取不同独立成分得到任务相关成分、头动成分、瞬时任务相关成分、噪声干扰、以及其它产生fMRI信号的多种源信号。     

The influence of iliotibial band syndrome history on running biomechanics examined via principal components analysis

Iliotibial band syndrome (ITBS) is a common knee overuse injury among female runners. Atypical discrete trunk and lower extremity biomechanics during running may be associated with the etiology of ITBS. Examining discrete data points limits the interpretation of a waveform to a single value. Characterizing entire kinematic and kinetic waveforms may provide additional insight into biomechanical factors associated with ITBS. Therefore, the purpose of this cross-sectional investigation was to determine whether female runners with previous ITBS exhibited differences in kinematics and kinetics compared to controls using a principal components analysis (PCA) approach. Forty participants comprised two groups: previous ITBS and controls. Principal component scores were retained for the first three principal components and were analyzed using independent t-tests. The retained principal components accounted for 93–99% of the total variance within each waveform. Runners with previous ITBS exhibited low principal component one scores for frontal plane hip angle. Principal component one accounted for the overall magnitude in hip adduction which indicated that runners with previous ITBS assumed less hip adduction throughout stance. No differences in the remaining retained principal component scores for the waveforms were detected among groups. A smaller hip adduction angle throughout the stance phase of running may be a compensatory strategy to limit iliotibial band strain. This running strategy may have persisted after ITBS symptoms subsided.     

Time-frequency changes in electromyographic signals after hamstring lengthening surgery in children with cerebral palsy

Increased knee flexion during stance is a common gait deviation in the child with cerebral palsy (CP), with distal hamstring lengthening surgeries being an accepted course of treatment. Post-operatively, improvements in gait kinematics have been reported, however little change is noted in the patterns of muscle activity as portrayed by onset and offset timing in the surface electromyographic (sEMG) signals. Similar analysis based on the frequency content of the sEMG signals has seldom been applied, yet may provide additional insight into changes in muscle activity in response to surgery. The purpose of this study was to determine if changes in the time-frequency characteristics of the sEMG, extracted using wavelet analysis techniques, corresponded to improved gait kinematics observed post-surgical intervention, and whether there existed a relationship between frequency characteristics of the sEMG signals and the type of surgery required to correct gait kinematics. Data were collected from 16 children with typical development (TD) and 17 children with CP pre- and post-surgery. Muscle activity was recorded from the medial hamstring (MH) and vastus lateralis (VL) muscles, processed using the wavelet transform, and analyzed using functional principal component analyses (PCA). Results indicated that frequency differences were present pre-operatively depending if surgery was to be performed bilaterally or involved bone modification. Post-operatively, frequency characteristics of the VL more closely approximated those observed in children with TD, agreeing with the improved gait kinematics. MH characteristics, however, for the surgical groups demonstrated a deviation away for TD reflecting the altered muscle structure.     

An application of pattern recognition for the comparison of trunk muscles EMG waveforms between subjects with and without chronic low back pain during flexion-extension and lateral bending tasks.

The purpose of the study was twofold: (1) to evaluate the reliability of distance measures computed from a principal component analysis (PCA) of electromyographic (EMG) waveforms of trunk muscles recorded during standardized trunk movements and (2) to evaluate their sensitivity to the load lifted, the trunk range of motion (ROM) and to low back status. Thirty-three male subjects (18 normals, 15 suffering from non specific CLBP) aged between 35-45 years participated. The EMG signals from 12 trunk muscles and the kinematics of trunk segments were recorded during 12 tasks. The subjects performed flexion-extension and lateral bending (left and right) tasks (three complete cycles) with and without a 12 kg load and at different trunk ROM (maximal or at defined submaximal angles). Distance measures locating each subject relative to a reference PCA model were computed for each muscle and task. The reliability of these distance measures was evaluated for 10 subjects (five normals and five CLBP) who performed two tasks on three different days. The reliability of distance measures was acceptable for agonist muscles only. The distance measures were sensitive to the load lifted and to the trunk ROM for different muscles and tasks but poorly sensitive to low back status. Several reasons that could explain the low sensitivity of these measures to low back status are discussed and potential solutions are proposed. A procedure based on a reliability analysis is proposed to select the number of principal components to include in the reference PCA model. It is expected that the refinement of the method used in this study could provide an effective clinical tool to assess EMG waveforms of trunk muscles during dynamic tasks.     

Independent EEG sources are dipolar

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).     

Automatic detection of motor unit innervation zones of the external anal sphincter by multichannel surface EMG

A method to detect automatically the location of innervation zones (IZs) from 16-channel surface EMG (sEMG) recordings from the external anal sphincter (EAS) muscle is presented in order to guide episiotomy during child delivery. The new algorithm (2DCorr) is applied to individual motor unit action potential (MUAP) templates and is based on bidimensional cross correlation between the interpolated image of each MUAP template and two images obtained by flipping upside-down (around a horizontal axis) and left–right (around a vertical axis) the original one. The method was tested on 640 simulated MUAP templates of the sphincter muscle and compared with previously developed algorithms (Radon Transform, RT; Template Match, TM). Experimental signals were detected from the EAS of 150 subjects using an intra-anal probe with 16 equally spaced circumferential electrodes. The results of the three algorithms were compared with the actual IZ location (simulated signal) and with IZ location provided by visual analysis (VA) (experimental signals). For simulated signals, the inter quartile error range (IQR) between the estimated and the actual locations of the IZ was 0.20, 0.23, 0.42, and 2.32 interelectrode distances (IED) for the VA, 2DCorr, RT and TM methods respectively.     

Automatic decomposition of multichannel intramuscular EMG signals

We describe an automatic algorithm for decomposing multichannel EMG signals into their component motor unit action potential (MUAP) trains, including signals from widely separated recording sites in which MUAPs exhibit appreciable interchannel offset and jitter. The algorithm has two phases. In the clustering phase, the distinct, recurring MUAPs in each channel are identified, the ones that correspond to the same motor units are determined by their temporal relationships, and multichannel templates are computed. In the identification stage, the MUAP discharges in the signal are identified using matched filtering and superimposition resolution techniques. The algorithm looks for the MUAPs with the largest single channel components first, using matches in one channel to guide the search in other channels, and using information from the other channels to confirm or refute each identification. For validation, the algorithm was used to decompose 10 real 6-to-8-channel EMG signals containing activity from up to 25 motor units. Comparison with expert manual decomposition showed that the algorithm identified more than 75% of the total 176 MUAP trains with an accuracy greater than 95%. The algorithm is fast, robust, and shows promise to be accurate enough to be a useful tool for decomposing multichannel signals. It is freely available at http://emglab.stanford.edu.     

An application of common principal component analysis to cranial morphometry of Microtus californicus and M. ochrogaster(Mammalia, Rodentia)

Principal component analysis (PCA) is a one-group method. Its purpose is to transform correlated variables into uncorrelated ones and to find linear combinations accounting for a relatively large amount of the total variability, thus reducing the number of original variables to a few components only.
In the simultaneous analysis of different groups, similarities between the principal component structures can often be modelled by the methods of common principal components (CPCs) or partial CPCs. These methods assume that either all components or only some of them are common to all groups, the discrepancies being due mainly to sampling error.
Previous authors have dealt with the k-group situation either by pooling the data of all groups, or by pooling the within-group variance-covariance matrices before performing a PCA. The latter technique is known as multiple group principal component analysis or MGPCA (Thorpe, 1983a). We argue that CPC- or partial CPC-analysis is often more appropriate than these previous methods.
A morphometrical example using males and females of Microtus californicus and M. ochrogaster is presented, comparing PCA, CPC and partial CPC analyses. It is shown that the new methods yield estimated components having smaller standard errors than when groupwise analyses are performed. Formulas are given for estimating standard errors of the eigenvalues and eigenvectors, as well as for computing the likelihood ratio statistic used to test the appropriateness of the CPC- or partial CPC-model.     

Separation of propagating and non propagating components in surface EMG

Surface electromyogram (EMG) detected by electrode arrays along the muscle fibre direction can be approximated by the sum of propagating and non propagating components. A technique to separate propagating and non propagating components in surface EMG signals is developed. The first step is an adaptive filter, which allows obtaining an estimation of the delay between signals detected at different channels and a first estimate of propagating and non propagating components; the second step is used to optimise the estimation of the two components. The method is applicable to signals with one propagating and one non propagating component. It was optimised on simulated signals, and then applied to single motor unit action potentials (MUAP) and to electrically elicited EMG (M-waves).

The new method was first tested on phenomenological signals constituted by the sum of a propagating and a non propagating signal and then applied to simulated and experimental EMG signals. Simulated signals were generated by a cylindrical, layered volume conductor model. Experimental signals were monopolar surface EMG signals collected from the abductor pollicis brevis muscle and M-waves recorded during transcutaneous electrical stimulation of the biceps muscle. The technique may find different applications: in single motor unit (MU) studies (a) for decreasing the variability and bias of CV estimates due to the presence of the non propagating components, (b) for estimating automatically the length of the muscle fibres from only three detected channels and (c) for removal of the stimulation artifact M-waves.     

食用牛肝菌不同部位紫外指纹图谱鉴别分析

采用紫外光谱技术结合主成分分析法,建立快速鉴别牛肝菌的不同部位及不同种类、产地的方法。通过单因素实验确定提取牛肝菌特征成分的最佳条件,制备测试液并进行紫外光谱测定。结果表明,牛肝菌样品的重现性、精密度和10h内稳定性的RSD（%）分别在0.09–1.81、0.11–1.92、0.06–2.33之间;牛肝菌不同部位紫外指纹图谱具有明显的指纹特性;主成分分析（PCA）表明菌盖和菌柄前3个主成分累积贡献率分别为94.797%和92.961%,能够反映样品的主要信息;SIMCA软件分析显示牛肝菌菌盖和菌柄化学成分积累不同。根据牛肝菌不同部位紫外光谱信息和主成分分析能区分同一牛肝菌的不同部位,鉴别不同种类、不同产地的食用牛肝菌。     

An improved method for two-dimensional fluorescence monitoring of complex bioreactors

An improved method for deconvoluting complex spectral maps from bidimensional fluorescence monitoring is presented, relying on a combination of principal component analysis (PCA) and feedforward artificial neural networks (ANN). With the aim of reducing ANN complexity, spectral maps are first subjected to PCA, and the scores of the retained principal components are subsequently used as ANN input vector. The method is presented using the case study of an extractive membrane biofilm reactor, where fluorescence maps of a membrane-attached biofilm were analysed, which were collected under different reactor operating conditions. During ANN training, the spectral information is associated with process performance indicators. Originally, 231 excitation/emission pairs per fluorescence map were used as ANN input vector. Using PCA, each fluorescence map could be represented by a maximum of six principal components, thereby catching 99.5% of its variance. As a result, the dimension of the ANN input vector and hence the complexity of the artificial neural network was significantly reduced, and ANN training speed was increased. Correlations between principal components and ANN predicted process performance parameters were good with correlation coefficients in the order of 0.7 or higher.     

云南医学革螨数值分类研究

以云南省57种医学革螨作为分类单元,以形态特征为主列出60项分类性状特征来探讨云南省医学革螨不同属和种的亲缘关系。运用SPSS 11.5 统计软件中的系统聚类分析和主成分分析,对57种医学革螨进行了数值分类分析。结果显示：57种医学革螨划分为厉螨科（La elapidae）、寄螨科（Parasitidae）、皮刺螨科（Dermanyssidae）、赫刺螨科（Hirstionyssidae）和裂胸螨科（Aceosejidae）5个类群。赫刺螨属和棘刺螨属从厉螨科中分离出来另立为赫刺螨科,柏氏禽刺螨归入了皮刺螨科而不是巨刺螨科。分类结果与传统形态分类结果基本一致,因而认为数值分类能比较客观地反映医学革螨各分类阶元的分类地位与亲缘关系。     

Bayesian independent component analysis recovers pathway signatures from blood metabolomics data

Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the source matrix of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a mixing matrix, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field.     

The use of principal component analysis to resolve the spectra and kinetics of cytochrome c oxidase reduction by 5,10-dihydro-5-methyl phenazine.

The method of principal component analysis (PCA) was applied to the absorption-wavelength-time surfaces generated by rapid scanning stopped-flow spectrophotometry (RSSFS). The method was used to resolve the absorption surfaces generated during the reduction of cytochrome c oxidase by 5,10-dihydro-5-methyl phenazine (MPH) into the individual spectral shapes and time courses of the component chromophores. Two forms of resting cytochrome oxidase were used in these analyses: one that has its maximum absorption in the Soret region at 418 nm (418-nm species) and the other has its absorption maximum at 424 nm (424-nm species). A weighting scheme suitable for RSSFS data was developed. The optical absorption spectra obtained by W.H. Vanneste (1966, Biochemistry, 5:838-848) for the oxidase components were found to fit adequately as components of the experimental surfaces. Among these spectra were the oxidized forms of cytochromes a and a3 in the wavelength region 330-520 nm for the 418-nm species. Vanneste's spectral shape for the oxidized cytochrome a3 did not fit as a component in the spectrum of the 424-nm species. After accounting for the spectral shape of all components present, PCA provided a straightforward method for determining the separate time courses of each chromophore. We have found for both forms used that cytochrome a is reduced by MPH in the initial stages of the reaction, while cytochrome a3 is reduced in subsequent, slow phases. An important aspect of PCA is that it provided confirmation of the spectra of the various oxidase components without requiring the use of inhibitors or the use of simplifying mechanistic assumptions. The resolution of time profiles of strongly overlapping chromophores is also demonstrated.     

Biometric variability of goat populations revealed by means of principal component analysis

The aim was to analyze variation in 12 Brazilian and Moroccan goat populations, and, through principal component analysis (PCA), check the importance of body measures and their indices as a means of distinguishing among individuals and populations. The biometric measurements were wither height (WH), brisket height (BH) and ear length (EL). Thorax depth (WH-BH) and the three indices, TD/WH, EL/TD and EL/WH, were also calculated. Of the seven components extracted, the first three principal components were sufficient to explain 99.5% of the total variance of the data. Graphical dispersion by genetic groups revealed that European dairy breeds clustered together. The Moroccan breeds were separated into two groups, one comprising the Drâa and the other the Zagora and Rhâali breeds. Whereas, on the one side, the Anglo-Nubian and undefined breeds were the closest to one another the goats of the Azul were observed to have the highest variation of all the breeds. The Anglo-Nubian and Boer breeds were similar to each other. The Nambi-type goats remained distinct from all the other populations. In general, the use of graphical representation of PCA values allowed to distinguish genetic groups.     

新的独立成分分析算法实现功能磁共振成像信号的盲分离

采用独立成分分析(independent component analysis,ICA)的一种新的牛顿型算法来提取功能磁共振成像(functional magnetic rasonance imaging,fMRI)信号中的各种独立成分(包括与实验设计相关的成分以及各种噪声)。与fastICA相比,该算法减少了运算量,提高了运算速度,而且能够很好地分离出各个独立成分。结果表明该算法是一种有效的fMRI信号分析手段。     

Estimation of independent and dependent components of non-invasive EMG using fast ICA: validation in recognising complex gestures

The identification of a number of active muscles during complex actions is the useful information to identify different gestures. Biosignals such as surface electromyogram (sEMG) are a result of the summation of electrical activity of a number of sources. The complexity of the anatomy and actions makes it difficult in identifying the number of active sources from the multiple channel recordings. This paper addresses two applications of independent component analysis (ICA) on sEMG: the first one is to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The second application is to identify complex hand gestures using decomposed sEMG. The theoretical analysis and experimental results demonstrate that the ICA is suitable for the separation of myoelectric signals. The results identify the usage of ICA for identifying complex gestures.     

Estimation of independent and dependent components of non-invasive EMG using fast ICA: validation in recognising complex gestures

The identification of a number of active muscles during complex actions is the useful information to identify different gestures. Biosignals such as surface electromyogram (sEMG) are a result of the summation of electrical activity of a number of sources. The complexity of the anatomy and actions makes it difficult in identifying the number of active sources from the multiple channel recordings. This paper addresses two applications of independent component analysis (ICA) on sEMG: the first one is to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The second application is to identify complex hand gestures using decomposed sEMG. The theoretical analysis and experimental results demonstrate that the ICA is suitable for the separation of myoelectric signals. The results identify the usage of ICA for identifying complex gestures.     

Motor Unit Characteristics after Targeted Muscle Reinnervation

Targeted muscle reinnervation (TMR) is a surgical procedure used to redirect nerves originally controlling muscles of the amputated limb into remaining muscles above the amputation, to treat phantom limb pain and facilitate prosthetic control. While this procedure effectively establishes robust prosthetic control, there is little knowledge on the behavior and characteristics of the reinnervated motor units. In this study we compared the m. pectoralis of five TMR patients to nine able-bodied controls with respect to motor unit action potential (MUAP) characteristics. We recorded and decomposed high-density surface EMG signals into individual spike trains of motor unit action potentials. In the TMR patients the MUAP surface area normalized to the electrode grid surface (0.25 ± 0.17 and 0.81 ± 0.46, p < 0.001) and the MUAP duration (10.92 ± 3.89 ms and 14.03 ± 3.91 ms, p < 0.01) were smaller for the TMR group than for the controls. The mean MUAP amplitude (0.19 ± 0.11 mV and 0.14 ± 0.06 mV, p = 0.07) was not significantly different between the two groups. Finally, we observed that MUAP surface representation in TMR generally overlapped, and the surface occupied by motor units corresponding to only one motor task was on average smaller than 12% of the electrode surface. These results suggest that smaller MUAP surface areas in TMR patients do not necessarily facilitate prosthetic control due to a high degree of overlap between these areas, and a neural information—based control could lead to improved performance. Based on the results we also infer that the size of the motor units after reinnervation is influenced by the size of the innervating motor neuron.