独立分量分析在表面肌电信号分解中的应用

采用独立分量分析中的信息极大化快速算法初步探讨了表面肌电信号的分解问题。研究结果表明 ,独立分量分析对肌肉轻度收缩力水平下 (<10 %MVC)表面肌电信号的分解有较好的效果 ,可以作为表面肌电信号分解的一种预处理手段     

Removing electroencephalographic artifacts by blind source separation

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.     

Applicability of independent component analysis on high-density microelectrode array recordings

Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as "spike sorting." For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.     

基于卷积混合盲源分离技术的表面肌电信号分解研究

根据表面肌电信号（SEMG）形成的生理学特性，采用一种基于卷积混合过程的盲源分离技术来分析隐含在SEMG信号中的运动单位动作电位信息，利用仿真的SEMG信号对这种算法的分解性能进行实验研究，并与采用瞬时混合过程的独立分量分析（ICA）算法的分解性能进行比较，同时将该算法应用于真实SEMG信号的分解实验。研究结果表明，无论是对模拟SEMG信号还是真实SEMG信号，采用卷积混合盲源分离技术的分解方法均能得到较明显的分解效果，且该方法较符合表面肌电信号的形成过程，因而具有重要的研究价值。     

Using independent component analysis for noise reduction of magnetocardiographic data in case of exercise with an ergometer

In 1992, Brockmeier et al. showed that there is a strong difference in magnetocardiography (MCG)-detected field distribution generated by the heart at rest and under stress. To study the possible clinical applications of this finding, it is convenient to avoid pharmacological stress and to perform stress MCG (SMCG) using conventional physical stress with an ergometer. When using a non-magnetic ergometer, the MCG recordings under physical stress are more noisy due to the unavoidable movement artefacts from the patient and from the residual artefacts of the ergometer. To remove these artefacts a denoising was performed using independent component analysis (ICA) in a new implementation. This work shows that with ICA in this special implementation it is becoming feasible to extract heart signals from SMCG data recorded during ergometer exercise.     

The Artifact-Free MEG-MUSIC Algorithm on Magnetoencephalographic Source Localization

INTRODUCTION　　 Multichannel superconducting quantum interference device ( SQUID) magne-tometers can beused to measure the spatio-temporal magnetoencephalogram ( MEG)produced by the neural activity in the human brain.From analysis of the MEG,onecan obtai…     

基于独立分量分析的脑电噪声消除

作为一种新的多元统计处理方法，独立分量分析(ICA)是解决盲源分离(BSS)问题的一个有效手段。在简要分析ICA理论及其算法的基础上，提出将其应用到脑电中的眼电伪迹的去除任务。实际采集的生理信号大多由相互独立的成分线性迭加而成，符合ICA要求源信号统计独立的基本假设。与传统方法相比，ICA这种空间滤波器不受信号频谱混迭的限制，消噪的同时能对有用信号的细节成分做到很好的保留，很大程度上弥补了时频域方法的不足。此外解混矩阵的逆可以用来反映独立源的空间分布模式，具有重要的生理意义。     

Using independent component analysis for noise reduction of magnetocardiographic data in case of exercise with an ergometer

In 1992, Brockmeier et al. showed that there is a strong difference in magnetocardiography (MCG)-detected field distribution generated by the heart at rest and under stress. To study the possible clinical applications of this finding, it is convenient to avoid pharmacological stress and to perform stress MCG (SMCG) using conventional physical stress with an ergometer. When using a non-magnetic ergometer, the MCG recordings under physical stress are more noisy due to the unavoidable movement artefacts from the patient and from the residual artefacts of the ergometer. To remove these artefacts a denoising was performed using independent component analysis (ICA) in a new implementation. This work shows that with ICA in this special implementation it is becoming feasible to extract heart signals from SMCG data recorded during ergometer exercise.     

一种独立成分分析的鲁棒算法及其在脑磁图数据分析中的应用

独立成分分析是一种新的信号处理统计方法。被广泛用于各个领域。在信号分析中面临的难题是：源信号的不同特性（既包括超高斯信号又包括亚高斯信号）；未知的独立源数目；传感器信号受到较大的加性噪声污染。针对以上难题，本文提出了一种独立成分分析的鲁棒算法。该方法先对观测数据作预处理，将包含噪声的高维传感器观测信号降维分解到信号子空间和噪声子空间。利用交叉验证法估计出独立源的数目（解决了独立成分分析本身不能确定源数目的缺陷）；然后利用快速稳定的FastICA算法分离独立成分。通过人工合成的数据和实际的脑磁图数据分析。验证了这种方法的功效。     

Denoising of HD-sEMG signals using canonical correlation analysis

High-density surface electromyography (HD-sEMG) is a recent technique that overcomes the limitations of monopolar and bipolar sEMG recordings and enables the collection of physiological and topographical informations concerning muscle activation. However, HD-sEMG channels are usually contaminated by noise in an heterogeneous manner. The sources of noise are mainly power line interference (PLI), white Gaussian noise (WGN) and motion artifacts (MA). The spectral components of these disruptive signals overlap with the sEMG spectrum which makes classical filtering techniques non effective, especially during low contraction level recordings. In this study, we propose to denoise HD-sEMG recordings at 20 % of the maximum voluntary contraction by using a second-order blind source separation technique, named canonical component analysis (CCA). For this purpose, a specific and automatic canonical component selection, using noise ratio thresholding, and a channel selection procedure for the selective version (sCCA) are proposed. Results obtained from the application of the proposed methods (CCA and sCCA) on realistic simulated data demonstrated the ability of the proposed approach to retrieve the original HD-sEMG signals, by suppressing the PLI and WGN components, with high accuracy (for five different simulated noise dispersions using the same anatomy). Afterward, the proposed algorithms are employed to denoise experimental HD-sEMG signals from five healthy subjects during biceps brachii contractions following an isometric protocol. Obtained results showed that PLI and WGN components could be successfully removed, which enhances considerably the SNR of the channels with low SNR and thereby increases the mean SNR value among the grid. Moreover, the MA component is often isolated on specific estimated sources but requires additional signal processing for a total removal. In addition, comparative study with independent component analysis, CCA-wavelet and CCA-empirical mode decomposition (EMD) proved a higher efficiency of the presented method over existing denoising techniques and demonstrated pointless a second filtering stage for denoising HD-sEMG recordings at this contraction level.     

Removal of eye movement artefacts from single channel recordings of retinal evoked potentials using <Emphasis Type="Italic">synchronous</Emphasis> dynamical embedding and independent component analysis

A system is described for the removal of eye movement and blink artefacts from single channel pattern reversal electroretinogram recordings of very poor signal-to-noise ratios. Artefacts are detected and removed by using a blind source separation technique based on the jadeR independent component analysis algorithm. The single channel data are arranged as a series of overlapping time-delayed vectors forming a dynamical embedding matrix. The structure of this matrix is constrained to the phase of the stimulation epoch: the term synchronous dynamical embedding is coined. A novel method using a marker channel with a non-independent synchronous feature is employed to identify the single most relevant source estimation for reconstruction and signal recovery. This method is non-lossy, all underlying signal being recovered. In synthetic datasets of defined noise content and in standardised real data recordings, the performance of this technique is compared to conventional fixed-threshold hard-limit rejection. The most significant relative improvements are achieved when movement and blink artefacts are greatest: no improvement is demonstrable for the random noise only situation.     

MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition

We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short periodȁ9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive characteristics compared with the former decomposition methods: (1) it bandpass filters the EMG signal through wavelet filter and utilizes threshold estimation calculated in wavelet transform for noise reduction in EMG signals to detect MUAPs before amplitude single threshold filtering; (2) it removes the power interference component from EMG recordings by combining independent component analysis (ICA) and wavelet filtering method together; (3) the similarity measure for MUAP clustering is based on the variance of the error normalized with the sum of RMS values for segments; (4) it finally uses ICA method to subtract all accurately classified MUAP spikes from original EMG signals. The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals.     

ICA-based muscle–tendon units localization and activation analysis during dynamic motion tasks

This study proposed an independent component analysis (ICA)-based framework for localization and activation level analysis of muscle–tendon units (MTUs) within skeletal muscles during dynamic motion. The gastrocnemius muscle and extensor digitorum communis were selected as target muscles. High-density electrode arrays were used to record surface electromyographic (sEMG) data of the targeted muscles during dynamic motion tasks. First, the ICA algorithm was used to decompose multi-channel sEMG data into a weight coefficient matrix and a source matrix. Then, the source signal matrix was analyzed to determine EMG sources and noise sources. The weight coefficient vectors corresponding to the EMG sources were mapped to target muscles to find the location of the MTUs. Meanwhile, the activation level changes in MTUs during dynamic motion tasks were analyzed based on the corresponding EMG source signals. Eight subjects were recruited for this study, and the experimental results verified the feasibility and practicality of the proposed ICA-based method for the MTUs’ localization and activation level analysis during dynamic motion. This study provided a new, in-depth way to analyze the functional state of MTUs during dynamic tasks and laid a solid foundation for MTU-based accurate muscle force estimation, muscle fatigue prediction, neuromuscular control characteristic analysis, etc.     

Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets

Several recent studies have used matrix factorization algorithms to assess the hypothesis that behaviors might be produced through the combination of a small number of muscle synergies. Although generally agreeing in their basic conclusions, these studies have used a range of different algorithms, making their interpretation and integration difficult. We therefore compared the performance of these different algorithms on both simulated and experimental data sets. We focused on the ability of these algorithms to identify the set of synergies underlying a data set. All data sets consisted of nonnegative values, reflecting the nonnegative data of muscle activation patterns. We found that the performance of principal component analysis (PCA) was generally lower than that of the other algorithms in identifying muscle synergies. Factor analysis (FA) with varimax rotation was better than PCA, and was generally at the same levels as independent component analysis (ICA) and nonnegative matrix factorization (NMF). ICA performed very well on data sets corrupted by constant variance Gaussian noise, but was impaired on data sets with signal-dependent noise and when synergy activation coefficients were correlated. Nonnegative matrix factorization (NMF) performed similarly to ICA and FA on data sets with signal-dependent noise and was generally robust across data sets. The best algorithms were ICA applied to the subspace defined by PCA (ICAPCA) and a version of probabilistic ICA with nonnegativity constraints (pICA). We also evaluated some commonly used criteria to identify the number of synergies underlying a data set, finding that only likelihood ratios based on factor analysis identified the correct number of synergies for data sets with signal-dependent noise in some cases. We then proposed an ad hoc procedure, finding that it was able to identify the correct number in a larger number of cases. Finally, we applied these methods to an experimentally obtained data set. The best performing algorithms (FA, ICA, NMF, ICAPCA, pICA) identified synergies very similar to one another. Based on these results, we discuss guidelines for using factorization algorithms to analyze muscle activation patterns. More generally, the ability of several algorithms to identify the correct muscle synergies and activation coefficients in simulated data, combined with their consistency when applied to physiological data sets, suggests that the muscle synergies found by a particular algorithm are not an artifact of that algorithm, but reflect basic aspects of the organization of muscle activation patterns underlying behaviors.     

Characterisation of electrocardiogram signals based on blind source separation

Blind source separation assumes that the acquired signal is composed of a weighted sum of a number of basic components corresponding to a number of limited sources. This work poses the problem of ECG signal diagnosis in the form of a blind source separation problem. In particular, a large number of ECG signals undergo two of the most commonly used blind source separation techniques, namely, principal component analysis (PCA) and independent component analysis (ICA), so that the basic components underlying this complex signal can be identified. Given that such techniques are sensitive to signal shift, a simple transformation is used that computes the magnitude of the Fourier transformation of ECG signals. This allows the phase components corresponding to such shifts to be removed. Using the magnitude of the projection of a given ECG signal onto these basic components as features, it was shown that accurate arrhythmia detection and classification were possible. The proposed strategies were applied to a large number of independent 3s intervals of ECG signals consisting of 320 training samples and 160 test samples from the MIT-BIH database. The samples equally represent five different ECG signal types, including normal, ventricular couplet, ventricular tachycardia, ventricular bigeminy and ventricular fibrillation. The intervals analysed were windowed using either a rectangular or a Hamming window. The methods demonstrated a detection rate of sensitivity 98% at specificity of 100% using nearest neighbour classification of features from ICA and a rectangular window. Lower classification rates were obtained using the same classifier with features from either PCA or ICA and a rectangular window. The results demonstrate the potential of the new method for clinical use.     

基于独立分量分析的诱发电位信号噪声分离方法

诱发电位(EP)信号的检测与分析技术是临床医学诊断神经系统损伤及病变的重要手段之一。但是，从人体体表所得到的EP信号含有大量的噪声，最典型的噪声是人体自发产生的脑电图信号(EEG)。因此，为利用EP信号诊断神经系统的损伤和病变，需要从混合信号中去除EEG等噪声。独立分量分析(ICA)是一种新近发展起来的统计信号处理方法。本文把ICA方法应用于EP信号的噪声消除，并与传统的自适应滤波方法进行了比较。计算机模拟表明，采用ICA方法进行信号噪声分离的结果明显优于自适应滤波方法。     

Noise reduction in magnetocardiography by singular value decomposition and independent component analysis

In the routine recording of magnetocardiograms (MCGs), it is necessary to underline the problem of noise cancellation. Source separation has often been suggested to solve this problem. In this paper, blind source separation (BSS), by means of singular value decomposition (SVD) and independent component analysis (ICA), was used for noise reduction in MCG data to improve the signal to noise ratio. Special techniques, based on statistical parameters, for identifying noise and disturbances, have been introduced to automatically eliminate noise-related and disturbance-related components before reconstructing cleaned data sets. The results show that ICA and SVD can detect and remove a variety of noise and artefact sources from MCG data, as well as from stress MCG.     

应用独立分量分析去除体表肌电中的心电干扰

体表肌电特别是从躯干获得的体表肌电往往受到被测对象自身心电信号的严重干扰。本文利用一种基于独立分量分析(ICA)的去噪方法,去除体表肌电中的心电干扰。该方法将多通道体表肌电进行独立分量分解,并用高通滤波器处理所分解出的心电独立分量以尽可能地保留其中的肌电成分,进而将去除心电干扰后的所有独立分量反向投影回原始信号空间得到去噪后的信号。仿真信号的处理结果表明,当高通滤波器的截止频率为30Hz时,该方法在有效去除心电干扰的同时使体表肌电的保真度达到最大。同时讨论了将信号的峰度(Kurtosis)值作为自动判别心电分量和肌电分量的标准的可能性。     

基于独立分量分析的生理信号盲源分离

用于盲源分离的独立分量分析 (ICA)和扩展ICA算法 ,基于极大似然估计 ,给出一个衡量输出分量统计独立的目标函数 ,最优化该目标函数 ,得到一种用于独立分量分析的迭代算法。扩展ICA算法的优点在于迭代过程中不需要计算信号的高阶统计量 ,收敛速度快 ,同时适用于超高斯和亚高斯信号的分离。应用该算法实现了脑电、心电信号以及语音信号的分离 ,并给出了实验结果     

An automated ECG-artifact removal method for trunk muscle surface EMG recordings

This study aimed at developing a method for automated electrocardiography (ECG) artifact detection and removal from trunk electromyography signals. Independent Component Analysis (ICA) method was applied to the simulated data set of ECG-corrupted surface electromyography (SEMG) signals. Independent Components (ICs) correspond to ECG artifact were then identified by an automated detection algorithm and subsequently removed. The detection performance of the algorithm was compared to that by visual inspection, while the artifact elimination performance was compared with Butterworth high pass filter at 30 Hz cutoff (BW HPF 30). The automated ECG-artifact detection algorithm successfully recognized the ECG source components in all data sets with a sensitivity of 100% and specificity of 99%. Better performance indicated by a significantly higher correlation coefficient (p < 0.001) with the original EMG recordings was found in the SEMG data cleaned by the ICA-based method, than that by BW HPF 30. The automated ECG-artifact removal method for trunk SEMG recordings proposed in this study was demonstrated to produce a very good detection rate and preserved essential EMG components while keeping its distortion to minimum. The automatic nature of our method has solved the problem of visual inspection by standard ICA methods and brings great clinical benefits.