Study on Segmented Correlation in EEG Based on Principal Component Analysis-中国期刊网

摘要 InordertoexplorethecorrelationbetweentheadjacentsegmentsofalongtermEEG,animprovedprincipalcomponentanalysis(PCA)methodbasedonmutualinformationalgorithmisproposed.Aone-dimensionEEGtimeseriesisdividedequallyintomanysegments,sothateachsegmentcanberegardedasanindependentvariablesandmulti-segmentedEEGcanbeexpressedasadatamatrix.Then,wesubstitutemutualinformationmatrixforcovariancematrixinPCAandconducttherelevanceanalysisofsegmentedEEG.Theexperimentalresultsshowthatthecontributionrateoffirstprincipalcomponent(FPC)ofsegmentedEEGismorelargerthanothers,whichcaneffectivelyreflectthedifferenceofepilepticEEGandnormalEEGwiththechangeofsegmentnumber.Inaddition,theevolutionofFPCconducetoidentifythetime-segmentlocationsofabnormaldynamicprocessesofbrainactivities,theseconclusionsarehelpfulfortheclinicalanalysisofEEG.

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Study on Segmented Correlation in EEG Based on Principal Component Analysis

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Study on Segmented Correlation in EEG Based on Principal Component Analysis

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