基于卒中脑电信号的交叉频率耦合分析

基于卒中脑电信号的交叉频率耦合分析基于卒中脑电信号的交叉频率耦合分析

摘要：卒中是一种常见的神经系统疾病，其严重程度和预后问题导致许多人遭受严重的健康和社会影响。随着技术的发展和研究的深入，越来越多的研究表明，卒中患者的脑电信号中存在着不同频率之间的耦合。本文提出了一种基于卒中脑电信号的交叉频率耦合分析方法，用于研究卒中患者脑电信号的特征和变化。

首先，本文介绍了卒中患者脑电信号的获取和预处理方法，采用了快速傅里叶变换和滤波等技术，对脑电信号进行了预处理。然后，本文提出了一种基于互信息的交叉频率耦合分析方法，旨在研究卒中患者不同频率之间的关系和耦合。通过对卒中患者和正常对照组进行对比分析，研究发现，在卒中患者的脑电信号中存在着显著的交叉频率耦合，表现为不同频率之间的关系和变化。同时，本文还探讨了交叉频率耦合在卒中患者脑电信号中的生理意义和应用前景。

关键词：卒中；脑电信号；交叉频率耦合；互信息；生理意义。

Abstract:Strokeisacommonneurologicaldiseasewithsevereseverityandprognosisissuesthatleadtoserioushealthandsocialimpactsformanypeople.Withthedevelopmentoftechnologyandresearch,moreandmorestudieshaveshownthattherearecouplingsbetweendifferentfrequenciesinthebrainelectroencephalogram(EEG)signalsofstrokepatients.Thispaperproposesamethodofcross-frequencycouplinganalysisbasedontheEEGsignalsofstrokepatients,whichisusedtostudythecharacteristicsandchangesoftheEEGsignalsinstrokepatients.

Firstly,thispaperintroducestheacquisitionandpre-processingmethodsofEEGsignalsofstrokepatients,whichadoptsthetechniquessuchasFastFourierTransformandfilteringtopreprocesstheEEGsignals.Then,thispaperproposesacross-frequencycouplinganalysismethodbasedonmutualinformationwiththeaimofstudyingtherelationshipandcouplingbetweendifferentfrequenciesinstrokepatients.Throughcomparativeanalysisbetweenstrokepatientsandnormalcontrolgroup,itwasfoundthatthereweresignificantcross-frequencycouplingsintheEEGsignalsofstrokepatients,reflectingtherelationshipandchangebetweendifferentfrequencies.Meanwhile,thispaperalsodiscussesthephysiologicalsignificanceandapplicationprospectsofcross-frequencycouplinginEEGsignalsofstrokepatients.

Keywords:stroke;EEGsignals;cross-frequencycoupling;mutualinformation;physiologicalsignificanceStrokeisaneurologicaldisorderthataffectsmorethan15millionpeopleworldwide.Itcancauseseriousandlong-termdisabilityduetodamagetothebrain.Electroencephalography(EEG)isawell-establishedtechniqueformonitoringtheelectricalactivityofthebrain.Inrecentyears,studieshaveshownthattherearesignificantchangesinEEGsignalsofstrokepatients,especiallyintheformofcross-frequencycouplings.

Cross-frequencycouplingreferstotheinteractionbetweendifferentfrequencybandsinEEGsignals.Itisacomplexanddynamicprocessthatreflectsthefunctionalconnectivityofdifferentbrainregions.Studieshavefoundthatcross-frequencycouplingsareinvolvedinawiderangeofcognitiveprocesses,suchasattention,memory,andperception.Instrokepatients,cross-frequencycouplingsmayprovideimportantinformationabouttheprogressionofthediseaseandtheeffectivenessoftreatment.

Mutualinformationisapowerfultoolforanalyzingcross-frequencycouplingsinEEGsignals.Itmeasurestheamountofinformationsharedbetweendifferentfrequencybandsandcanrevealtheunderlyingfunctionalconnectivityinthebrain.Studieshaveshownthatmutualinformationofcross-frequencycouplingsissignificantlydifferentbetweenstrokepatientsandnormalcontrolgroups.Thissuggeststhatcross-frequencycouplingsareapromisingbiomarkerforidentifyingandmonitoringstrokepatients.

Thephysiologicalsignificanceofcross-frequencycouplingsinstrokepatientsisstillnotfullyunderstood.Onehypothesisisthattheabnormalcross-frequencycouplingsmayreflectthealteredneuralnetworkinthebraincausedbystroke.Anotherhypothesisisthattheymayberelatedtothecompensationmechanismorrecoveryprocessafterstroke.Furtherstudiesareneededtoinvestigatethemechanismandclinicalimplicationsofcross-frequencycouplingsinstroke.

Insummary,cross-frequencycouplingsinEEGsignalsofstrokepatientsareanimportantandpromisingresearchtopic.Theyprovidevaluableinformationaboutthealterationofneuralnetworkandpotentialcompensationorrecoveryafterstroke,whichmayfacilitatethedevelopmentofeffectivetreatmentandrehabilitationstrategiesforstrokepatientsOnepotentialdirectionforfutureresearchistoexaminetherelationshipbetweencross-frequencycouplingsandfunctionalconnectivityinstrokepatients.Functionalconnectivityreferstothecoordinationandsynchronizationofneuralactivitybetweendifferentbrainregions,whichiscrucialfornormalbrainfunction.Ithasbeenshownthatstrokecandisruptfunctionalconnectivity[33],butitisunclearhowthisdisruptionaffectscross-frequencycouplings.Understandingtheinterplaybetweenthesetwophenomenamayhelpustobetterunderstandtheoverallimpactofstrokeonthebrainandidentifynewtargetsfortherapy.

Anotherinterestingavenueforfutureresearchistoinvestigatethepotentialofcross-frequencycouplingsasbiomarkersforstrokediagnosisandprognosis.EEGisanon-invasiveandinexpensivetoolthatcanbeeasilyadministeredatthebedside,makingitanattractiveoptionforclinicaluse.However,currentmethodsforstrokediagnosisandmonitoringprimarilyrelyonneuroimagingtechniques,whichareexpensiveandnotwidelyavailableinmanycountries.Ifcross-frequencycouplingscanbeshowntoreliablypredictstrokeoutcomes,thismayleadtothedevelopmentofportable,low-costEEG-baseddiagnostictoolsthatcouldbeusedinawiderrangeofsettings.

Finally,itwillbeimportanttovalidatethefindingsfromEEGstudiesofcross-frequencycouplingsinstrokeusingothertechniques,suchasfunctionalMRI(fMRI)andmagnetoencephalography(MEG).EEGhasexcellenttemporalresolutionbutpoorspatialresolution,whereasfMRIandMEGprovidecomplementaryinformationwithhighspatialresolutionbutpoortemporalresolution.Combiningthesetechniquesmayallowustobetterunderstandthelarge-scalebrainnetworksthatareaffectedbystrokeandhowcross-frequencycouplingscontributetofunctionalrecovery.

Inconclusion,cross-frequencycouplingsinEEGsignalsareavaluableandpromisingtoolforstudyingtheneuralmechanismsunderlyingstrokeanditsrecovery.Bysheddinglightontheinteractionsbetweendifferentfrequencybandsinthebrain,cross-frequencycouplingsprovideimportantinformationaboutthecomplexneuralnetworksthatareinvolvedinstrokepathophysiology.Furtherresearchinthisareamayleadtonewdiagnostictools,therapeuticinterventions,andabetterunderstandingofhowthebrainrepairsitselfafterstrokeInadditiontocross-frequencycouplings,otherEEGmeasuressuchasevent-relatedpotentials(ERPs),spectralpowerdensityandcoherence,andphase-amplitudecoupling(PAC)havealsobeenusedtostudystrokeanditsrecovery.ERPsaretime-lockedtoaspecificstimulusorevent,andcanprovideinformationaboutthetimingandsequenceofneuralprocessing.Spectralpowerdensityandcoherencearemeasuresofthestrengthandcoherenceofoscillatoryactivitywithinaspecificfrequencyband,andcanrevealchangesinneuralconnectivityandsynchronization.PACisameasureoftherelationshipbetweenthephaseofaslowoscillationandtheamplitudeofafasteroscillation,andcanprovideinsightsintohowdifferentfrequencybandsinteracttosupportneuralprocessing.

AnumberofstudieshaveusedEEGmeasurestoinvestigatetheeffectsofstrokeonneuralprocessing.Onestudyfoundthatstrokepatientsshowedreducedfunctionalconnectivitybetweenbrainregionsinvolvedinsensorimotorprocessing,andthatthisdisruptionwasrelatedtotheseverityofmotorimpairment(Rossoetal.,2014).Otherstudieshaveshownthatstrokecanleadtochangesinoscillatoryactivityandconnectivitywithinandbetweenbrainregions,particularlyinthealphaandbetafrequencybands(Wangetal.,2019;Buchetal.,2018).

EEGmeasureshavealsobeenusedtoinvestigatetheneuralmechanismsunderlyingstrokerecovery.Onestudyfoundthatstrokepatientswithbettermotoroutcomesshowedincreasedbetabandcoherencewithintheipsilesionalmotorcortex,suggestingthatincreasedneuralsynchronizationmaysupportmotorrecovery(Rossiteretal.,2013).Anotherstudyfoundthatstrokepatientswhoshowedgreaterfunctionalconnectivitybetweentheipsilesionalmotorcortexandthecontralesionalcerebellumhadbettermotoroutcomes,suggestingthatthisneuralcircuitmayplayaroleinmotorrecovery(Schulzetal.,2015).

Overall,EEGmeasuresp