EEG-signal-processing-脑电信号处理方法算法PPT课件

-,1,EEGSIGNALPROCESSING,-,2,EEGsignalmodelling,1,Availablefeatures,2,Classificationalgorithms,3,IndependentComponentAnalysis,4,Content,SparseRepresentation,5,-,3,1,EEGsignalmodelling,Bioelectricity,1,Signalgenerationsystem,2,-,4,bioelectricity,Signalgenerationsystem,Excitationmodel,-,5,signalgenerationsystem,bioelectricity,LinearModel,-,6,signalgenerationsystem,bioelectricity,NonlinearModel,-,7,2,Availablefeatures,Basicfeatures,1,Modernmethods,2,-,8,TemporalAnalysisSignalSegmentation:labeltheEEGsignalsbysegmentsofsimilarcharacteristics.,basicfeatures,Modernmethods,-,9,TemporalCriteria,basicfeatures,Modernmethods,-,10,FrequencyAnalysisSuboptimalDFT,DCT,DWT;OptimalKLT(Karhunen-Love)Demerits:completestatisticalinformation,nofastcalculation.,basicfeatures,Modernmethods,-,11,SignalParameterEstimationARmodel:Merits:OutperformDFTinfrequencyaccuracy.Demerits:sufferfrompoorestimationofparameters.Improvements:accurateorder&coefficients.,modernmethods,Basicfeatures,-,12,ARcoefficientsestimationmethodsYule-Walkeraryule(x,p)Merits:ToeplitzmatrixLevinson-Durbin,fastest!Demerits:withwindowbadresolutionofPSD,modernmethods,Basicfeatures,-,13,ARcoefficientsestimationmethodsCovariancemethodarcov(x,p),armcov(x,p)Merits:withoutwindowgoodresolutionofPSDDemerits:slowBurgarburg(x,p)Merits:accurateapproximationofPSDDemerits:lineskewing&splitting,modernmethods,Basicfeatures,-,14,modernmethods,Basicfeatures,Comparison,-,15,PrincipalComponentAnalysisUsesameconceptasSVDDecomposedataintouncorrelatedorthogonalcomponentsAutocorrelationmatrixisdiagonalizedEacheigenvectorrepresentsaprincipalcomponentApplicationdecomposition,classification,filtering,denoising,whitening.,modernmethods,Basicfeatures,-,16,3,SparseRepresentation,SparseApproximation,1,SparseDecomposition,2,-,17,Over-completedictionaryatomsHilbertspace:Signal:Error:“Sparse”:lN,satisfylimitederror.,sparseapproximation,Sparsedecomposition,-,18,Majoralgorithms:BasicPursuit,MatchingPursuits,OMPMatchingPursuits(MP):1st:kth:,sparsedecomposition,Sparseapproximation,与正交,-,19,K-SVD:trainingdictionaryPotentialapplicationsforEEG:CoefficientsfeaturesERPdetectionAbnormalEEGdetectionClassificationofdifferentstatusofEEG,sparsedecomposition,Sparseapproximation,-,20,4,Classificationalgorithms,Commonmethods,1,-,21,NaveBayesLDA:LinearDiscriminantAnalysisHMM:HiddenMarkovModellingSVM:SupportVectorMachineK-meansANNs:ArtificialNeuralNetworksFuzzyLogic,Commonmethods,-,22,5,IndependentComponentAnalysis,ICAapproaches,1,Application,2,-,23,IndependentComponentAnalysis,icaapproaches,applications,-,24,icaapproaches,applications,ICAapproaches:FactorizingthejointPDFintoitsmarginalPDFsDecorrelatingsignalsthroughtimeEliminatingtemporalcross-correlationfunction,-,25,