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Independent component analysis approach for fault diagnosis of condenser system in thermal power plant 认领 引用 被引量：6
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作者 Ajami Ali Daneshvar Mahdi 《Journal of Central South University》 SCIE EI CAS 2014年第1期242-251,共10页
A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is t... A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants. 展开更多
关键词 condenser fault detection and diagnosis independent component analysis independent component analysis （ICA） principal component analysis （PCA） thermal power plant
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Source Separation of Diesel Engine Vibration Based on the Empirical Mode Decomposition and Independent Component Analysis 认领 引用 被引量：23
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作者 DU Xianfeng LI Zhijun +3 位作者 BI Fengrong ZHANG Junhong WANG Xia SHAO Kang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2012年第3期557-563,共7页
Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its ... Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis（ICA）.However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition（EMD） and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements. 展开更多
关键词 empirical mode decomposition independent component analysis source separation single-channel signal
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Independent Component Analysis Based Blind Adaptive Interference Reduction and Symbol Recovery for OFDM Systems 认领 引用 被引量：5
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作者 LUO Zhongqiang ZHU Lidong LI Chengjie 《China Communications》 SCIE CSCD 2016年第2期41-54,共14页
To overcome the inter-carrier interference （ICI） of orthogonal frequency division multiplexing （OFDM） systems subject to unknown carrier frequency offset （CFO） and multipath, this paper develops a blind adaptive... To overcome the inter-carrier interference （ICI） of orthogonal frequency division multiplexing （OFDM） systems subject to unknown carrier frequency offset （CFO） and multipath, this paper develops a blind adaptive interference suppression scheme based on independent component analysis （ICA）. Taking into account statistical independence of subcarriers＇ signals of OFDM, the signal recovery mechanism is investigated to achieve the goal of blind equalization. The received OFDM signals can be considered as the mixed observation signals. The effect of CFO and multipath corresponds to the mixing matrix in the problem of blind source separation （BSS） framework. In this paper, the ICA- based OFDM system model is built, and the proposed ICA-based detector is exploited to extract source signals from the observation of a received mixture based on the assumption of statistical independence between the sources. The blind separation technique can increase spectral efficiency and provide robustness performance against erroneous parameter estimation problem. Theoretical analysis and simulation results show that compared with the conventional pilot-based scheme, the improved performance of OFDM systems is obtained by the proposed ICA-based detection technique. 展开更多
关键词 orthogonal frequency divisionmultiplexing （OFDM） blind source separation（BSS） independent component analysis （ICA） blind interference suppression symbol recovery
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Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation 认领 引用 被引量：3
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作者 韩仲志 万剑华 +1 位作者 张杰 张汉德 《Chinese Journal of Oceanology and Limnology》 CSCD 2017年第4期978-986,共9页
The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills... The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size.We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm.For each independent component we added two constraint conditions:non-negativity and constant sum.We use priority weighting by higher-order statistics,and then the spectral angle match method to overcome the order nondeterminacy.By these steps,endmembers can be extracted and abundance quantified simultaneously.To examine the coverage of a real oil spill and correct our estimate,a simulation experiment and a real experiment were designed using the algorithm described above.The result indicated that,for the simulation data,the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6.We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011.The total oil spill area was 0.224 km^2,and the oil spill rate was 22.89%.The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates.It also allows the accurate estimation of the oil spill area. 展开更多
关键词 oil spill hyperspectral imagery endmember extraction abundance quantification independent component analysis （ICA）
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Study of engine noise based on independent component analysis 认领 引用 被引量：7
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作者 HAO Zhi-yong JIN Yan YANG Chen 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2007年第5期772-777,共6页
Independent component analysis was applied to analyze the acoustic signals from diesel engine.First the basic prin-ciple of independent component analysis(ICA)was reviewed.Diesel engine acoustic signal was decomposed ... Independent component analysis was applied to analyze the acoustic signals from diesel engine.First the basic prin-ciple of independent component analysis(ICA)was reviewed.Diesel engine acoustic signal was decomposed into several inde-pendent components(ICs);Fourier transform and continuous wavelet transform(CWT)were applied to analyze the independent components.Different noise sources of the diesel engine were separated,based on the characteristics of different component in time-frequency domain. 展开更多
关键词 Acoustic signals Independent component analysis(ICA) Wavelet transform Noise source identification
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A new process monitoring method based on noisy time structure independent component analysis 认领 引用 被引量：4
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作者 蔡连芳 田学民 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第1期162-172,共11页
Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the advers... Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method. 展开更多
关键词 Process monitoring Independent component analysis Measurement noises Kurtosis Mixing matrix Contribution plot Sensitivity analysis
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Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating 认领 引用 被引量：1
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作者 王文波 张晓东 +4 位作者 常毓禅 汪祥莉 王钊 陈希 郑雷 《Chinese Physics B》 SCIE EI CAS CSCD 2016年第1期400-406,共7页
In this paper, a new method to reduce noises within chaotic signals based on ICA （independent component analysis） and EMD （empirical mode decomposition） is proposed. The basic idea is decomposing chaotic signals a... In this paper, a new method to reduce noises within chaotic signals based on ICA （independent component analysis） and EMD （empirical mode decomposition） is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the indepen- dent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions （IMFs） of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. 展开更多
关键词 independent component analysis empirical mode decomposition chaotic signal denoising
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An Independent Component Analysis Algorithm through Solving Gradient Equation Combined with Kernel Density Estimation 认领 引用 被引量：2
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作者 薛云峰 王宇嘉 杨杰 《Journal of Shanghai Jiaotong university(Science)》 EI 2009年第2期204-209,共6页
A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introdu... A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introduced to solve this equation efficiently.The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method.Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm. 展开更多
关键词 independent component analysis blind source separation gradient method kernel density estimation
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Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis 认领 引用 被引量：1
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作者 张曦 阎威武 +1 位作者 赵旭 邵惠鹤 《Journal of Shanghai Jiaotong university(Science)》 EI 2007年第5期563-571,共9页
A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis ... A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration. 展开更多
关键词 kernel independent component analysis （KICA） multivariate exponentially weighted moving average（MEWMA） nonlinear fault detection process monitoring fluid catalytic cracking unit （FCCU） process
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Small Target Extraction Based on Independent Component Analysis for Hyperspectral Imagery 认领 引用 被引量：3
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作者 LU Wei YU Xuchu 《Geo-Spatial Information Science》 EI 2006年第2期103-107,共5页
A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target infor... A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target information hided in high-dimensional data and projects them into low-dimensional space.Secondly, the feature images are selected with kurtosis .At last, small targets are extracted with histogram image segmentation which has been labeled by skewness. 展开更多
关键词 fast independent component analysis skewness kurtosis target extraction
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Online Batch Process Monitoring Based on Just-in-Time Learning and Independent Component Analysis 认领 引用 被引量：1
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作者 WANG Li SHI Hong-bo 《Journal of Donghua University(English Edition)》 CAS 2016年第6期944-948,共5页
A new method was developed for batch process monitoring in this paper.In the devdopad method,just-in-time learning(JITL)and independent component analysis(ICA)were integrated to build JITL-ICA monitoring scheme.JITL w... A new method was developed for batch process monitoring in this paper.In the devdopad method,just-in-time learning(JITL)and independent component analysis(ICA)were integrated to build JITL-ICA monitoring scheme.JITL was employed to tackle with the characteristics of batch process such as inherent time-varying dynamics,multiple operating phases,and especially the case of uneven length stage.According to new coming test data,the most correlated segmentation was obtained from batch-wise unfolded training data by JITL.Then,ICA served as the principal components extraction approach.Therefore,the non.Gaussian distributed data can also be addressed under this modeling framework.The effectiveness and superiority of JITL-ICA based monitoring method was demonstrated by fed-batch penicillin fermentation. 展开更多
关键词 batch process monitoring just-in-time learning(JITL) independent component analysis(ICA)
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State Inspection for Transmission Lines Based on Independent Component Analysis 认领 引用
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作者 任丽佳 江秀臣 +1 位作者 盛戈嗥 杨巍巍 《Journal of Shanghai Jiaotong university(Science)》 EI 2009年第2期129-132,共4页
Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation.Independent component analysis(ICA) is a method for finding unde... Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation.Independent component analysis(ICA) is a method for finding underlying factors or components from multivariate statistical data based on dimension reduction methods,and it is applicable to extract the non-stationary signals.FastICA based on negentropy is presented to effectively extract and separate the vibration signals caused by human activity in this paper.A new method combined empirical mode decomposition(EMD) technique with the adaptive threshold method is applied to extract the vibration pulses,and suppress the interference signals.The practical tests demonstrate that the method proposed in the paper is effective in separating and extracting the vibration signals. 展开更多
关键词 state inspection transmission lines independent component analysis （ICA） negentropy adaptive threshold empirical mode decomposition （EMD）
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Multi-state Information Dimension Reduction Based on Particle Swarm Optimization-Kernel Independent Component Analysis 认领 引用
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作者 邓士杰 苏续军 +1 位作者 唐力伟 张英波 《Journal of Donghua University(English Edition)》 CAS 2017年第6期791-795,共5页
The precision of the kernel independent component analysis( KICA) algorithm depends on the type and parameter values of kernel function. Therefore,it's of great significance to study the choice method of KICA'... The precision of the kernel independent component analysis( KICA) algorithm depends on the type and parameter values of kernel function. Therefore,it's of great significance to study the choice method of KICA's kernel parameters for improving its feature dimension reduction result. In this paper, a fitness function was established by use of the ideal of Fisher discrimination function firstly. Then the global optimal solution of fitness function was searched by particle swarm optimization( PSO) algorithm and a multi-state information dimension reduction algorithm based on PSO-KICA was established. Finally,the validity of this algorithm to enhance the precision of feature dimension reduction has been proven. 展开更多
关键词 kernel independent component analysis(KICA) particle swarm optimization(PSO) feature dimension reduction fitness function
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Measurement of a thin layer's thickness using independent component analysis of ground penetrating radar data 认领 引用
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作者 李想堂 张肖宁 王端宜 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2008年第4期445-449,共5页
To detect overlapped echoes due to the thin pavement layers,we present a thickness measurement approach for the very thin layer of pavement structures.The term "thin" is relative to the incident wavelength o... To detect overlapped echoes due to the thin pavement layers,we present a thickness measurement approach for the very thin layer of pavement structures.The term "thin" is relative to the incident wavelength or pulse.By means of independent component analysis of noisy signals received by a single radar sensor,the overlapped echoes can be successfully separated.Once the echoes from the top and bottom side of a thin layer have been separated,the time delay and the layer thickness determination follow immediately.Results of the simulation and real data verify the feasibility of the presented method. 展开更多
关键词 thin layer thickness measurement independent component analysis ground penetrating radar pavement
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Altered intra- and inter-network brain functional connectivity in upper-limb amputees revealed through independent component analysis 认领 引用 被引量：3
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作者 Bing-Bo Bao Hong-Yi Zhu +6 位作者 Hai-Feng Wei Jing Li Zhi-Bin Wang Yue-Hua Li Xu-Yun Hua Mou-Xiong Zheng Xian-You Zheng 《Neural Regeneration Research》 SCIE CAS CSCD 2022年第12期2725-2729,共5页
Although cerebral neuroplasticity following amputation has been observed, little is understood about how network-level functional reorganization occurs in the brain following upper-limb amputation. The objective of th... Although cerebral neuroplasticity following amputation has been observed, little is understood about how network-level functional reorganization occurs in the brain following upper-limb amputation. The objective of this study was to analyze alterations in brain network functional connectivity(FC) in upper-limb amputees(ULAs). This observational study included 40 ULAs and 40 healthy control subjects;all participants underwent resting-state functional magnetic resonance imaging. Changes in intra-and inter-network FC in ULAs were quantified using independent component analysis and brain network FC analysis. We also analyzed the correlation between FC and clinical manifestations, such as pain. We identified 11 independent components using independent component analysis from all subjects. In ULAs, intra-network FC was decreased in the left precuneus(precuneus gyrus) within the dorsal attention network and left precentral(precentral gyrus) within the auditory network;but increased in the left Parietal_Inf(inferior parietal, but supramarginal and angular gyri) within the ventral sensorimotor network, right Cerebelum_Crus2(crus Ⅱ of cerebellum) and left Temporal_Mid(middle temporal gyrus) within the ventral attention network, and left Rolandic_Oper(rolandic operculum) within the auditory network. ULAs also showed decreased inter-network FCs between the dorsal sensorimotor network and ventral sensorimotor network, the dorsal sensorimotor network and right frontoparietal network, and the dorsal sensorimotor network and dorsal attention network. Correlation analyses revealed negative correlations between inter-network FC changes and residual limb pain and phantom limb pain scores, but positive correlations between inter-network FC changes and daily activity hours of stump limb. These results show that post-amputation plasticity in ULAs is not restricted to local remapping;rather, it also occurs at a network level across several cortical regions. This observation provides additional insights into the plasticity of brain networks after upper-limb amputation, and could contribute to identification of the mechanisms underlying post-amputation pain. 展开更多
关键词 amputation functional connectivity functional magnetic resonance imaging independent component analysis neuroimaging phantom pain phantom sensation resting-state networks
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Two Dimensional Spatial Independent Component Analysis and Its Application in fMRI Data Process 认领 引用
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作者 陈华富 尧德中 《Journal of Electronic Science and Technology of China》 2005年第3期231-233,237,共3页
One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is propo... One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional （2-D） image deta and one-dimensional （l-D） signal. Moreover the combination of the Newton （fixed-point algorithm） and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging （fMRI） show the potential application of composite 2-D ICA in the brain activity detection. 展开更多
关键词 independent component analysis image processing composite 2-D ICA algorithm functional magnetic resonance imaging
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Blind Separation of Speech Signals Based on Wavelet Transform and Independent Component Analysis 认领 引用 被引量：5
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作者 吴晓 何静菁 +2 位作者 靳世久 徐安桃 王伟魁 《Transactions of Tianjin University》 EI CAS 2010年第2期123-128,共6页
Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT... Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately. 展开更多
关键词 wavelet transform independent component analysis blind source separation
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Efficient Fast Independent Component Analysis Algorithm with Fifth-Order Convergence 认领 引用
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作者 Xuan-Sen He Tiao-Jiao Zhao Fang Wang 《Journal of Electronic Science and Technology》 CAS 2011年第3期244-249,共6页
Independent component analysis （ICA） is the primary statistical method for solving the problems of blind source separation. The fast ICA is a famous and excellent algorithm and its contrast function is optimized by ... Independent component analysis （ICA） is the primary statistical method for solving the problems of blind source separation. The fast ICA is a famous and excellent algorithm and its contrast function is optimized by the quadratic convergence of Newton iteration method. In order to improve the convergence speed and the separation precision of the fast ICA, an improved fast ICA algorithm is presented. The algorithm introduces an efficient Newton＇s iterative method with fifth-order convergence for optimizing the contrast function and gives the detail derivation process and the corresponding condition. The experimental results demonstrate that the convergence speed and the separation precision of the improved algorithm are better than that of the fast ICA. 展开更多
关键词 Index Terms---Blind source separation fast independent component analysis fifth-order convergence independent component analysis Newton＇s iterative method.
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MART(Splitting-Merging Assisted Reliable)Independent Component Analysis for Extracting Accurate Brain Functional Networks 认领 引用 被引量：2
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作者 Xingyu He Vince D.Calhoun Yuhui Du 《Neuroscience Bulletin》 SCIE CAS CSCD 2024年第7期905-920,共16页
Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determini... Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN estimation.Here,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders.We extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI data.Based on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters.Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age increases.Furthermore,both small-scale and large-scale brain FN templates are provided as benchmarks for future studies.Taken together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs. 展开更多
关键词 Independent component analysis Functional magnetic resonance imaging-Brain functional networks Clustering Multi-model-order
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Extracting Epileptic Feature Spikes Using Independent Component Analysis 认领 引用
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作者 颜红梅 夏阳 《Journal of Electronic Science and Technology of China》 2005年第4期369-371,共3页
In recent years, blind source separation （BSS） by independent component analysis （ICA） has been drawing much attention because of its potential applications in signal processing such as in speech recognition syste... In recent years, blind source separation （BSS） by independent component analysis （ICA） has been drawing much attention because of its potential applications in signal processing such as in speech recognition systems, telecommunication and medical signal processing. In this paper, two algorithms of independent component analysis （fixed-point IC,4 and natural gradient-flexible ICA） are adopted to extract human epileptic feature spikes from interferential signals. Experiment results show that epileptic spikes can be extracted from noise successfully. The kurtosis of the epileptic component signal separated is much better than that of other noisy signals. It shows that ICA is an effective tool to extract epileptic spikes from patients＇ electroencephalogram EEG and shows promising application to assist physicians to diagnose epilepsy and estimate the epileptogenic region in clinic. 展开更多
关键词 independent component analysis epilepsy feature spikes electroencephalogram （EGG）
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