简介:EEGisthestudyoftheelectricalactivityofthebrainasdetectedby8-16pairsofsurfaceelectrodesattachedtothescalpinstan-dardizedpositions(Figure1).EEGisacrudemeasureofcere-bralfunction,reflectingtheactivityoflessthanonequarterofcorticalneuronesforalimitedperiodonly,butisthemostusefulandmostreadilyaccessibletestofcerebralactivity,ifitisusedappropriatelyanditslimitationsareunderstood.
简介:AnewmethodofphasespectralanalysisofEEGisproposedforthecomparativeanalysisofphasespectrabetweennormalEEGandepilepticEEGsignalsbasedonthewaveletdecompositiontechnique.Byusingmultiscalewaveletdecomposition,theoriginalEEGsaremappedtoanorthogonalwaveletspace,suchthatthevariationsofphasecanbeobservedatmultiscale.Itisfoundthatthephase(andphasedifference)spectraofnormalEEGsaredistinctfromthatofepilepticEEGs.Thatisthevariationsofphase(andphasedifference)ofnormalEEGshaveadistinctperiodicpatternwiththeelectricalactivityproceedsinthebrain,butdonottheepilepticEEGs.ForepilepticEEGs,onlyatthosetransientpoints,thephasevariationsareobvious.Inordertoverifytheseresultswiththeobservationaldata,thephasevariationsofEEGsinprincipalcomponentspaceareobservedandfoundthat,thefeaturesofphasespectraisincorrespondencewiththatthewaveletspace.TheseresultsmakeitpossibletoviewthebehaviorofEEGrhythmsasadynamicspectrum.
简介:IMAGINGOFEEGBYSPHERICALHARMONICANALYSISIMAGINGOFEEGBYSPHERICALHARMONICANALYSISYaoDezhong;YangShaoguo(Dep.ofAuto,UESTofChina,C...
简介:THEORYOFTHEEEGCORTICALIMAGINGTECHNIQUESTHEORYOFTHEEEGCORTICALIMAGINGTECHNIQUESDezhongYao,BingweLuoi(DepartmentofAutomationUni...
简介:Withtherapiddevelopmentofbraincomputerinterface(simplycalledBCI),electroencephalography(EEG)willbeanotherinterestingbio-electricalsignalappliedinroboticsafterEMG.Inordertorealizeitfinally,theaccuratemeasurementandpatternrecognitionofEEGsignalmustbeaveryimportantandelementaryresearchobjective.Basedonourcurrentresearchesandsomereportsfromtheotherinternationalcolleaguesinthefield,wedeeplydiscussthebasiccharacteristicsofEEGsignal,thedevelopmentandselectionofEEGmeasurementsystem,featureextractionandrecognitionmethodsofEEGsignal,andthenreviewEEG'sapplicationsinroboticsaswellasthefutureresearchtrendsinthispaper.
简介:Anewwaveletvarianceanalysismethodbasedonwindowfunctionisproposedtoinvestigatethedynamicalfeaturesofelectroencephalogram(EEG).TheexprienmentalresultsshowthatthewaveletenergyofepilepticEEGsaremorediscretethannormalEEGs,andthevariationofwaveletvarianceisdifferentbetweenepilepticandnormalEEGswiththeincreaseoftime-windowwidth.Furthermore,itisfoundthatthewaveletsubbandentropy(WSE)oftheepilepticEEGsarelowerthanthenormalEEGs.
简介:这研究的目的是根据EEG信号的复杂性措施的值识别大脑的函数和状态。30件正常样品和30件耐心的样品的EEG信号是镇定的。为未加工的数据基于预处理,为复杂性措施的一个计算程序被编译,所有样品的复杂性措施是计算的。吝啬的值和控制组的复杂性措施的标准错误作为0.33和0.10,并且正常的组作为0.53和0.08。当信心度是0.05时,为控制组的复杂性措施的正常人口平均数的信心间隔是(0.2871,0.3652),并且(0.4944,0.5552)为正常的组。正常样品和耐心的样品能清楚地是的统计结果表演由措施的价值区分了。在临床的药,结果能是是引用评估函数或状态,诊断疾病,监视大脑的康复进步。
简介:Thecombinationofelectroencephalogram(EEG)andfunctionalmagneticresonanceimaging(fMRI)isaveryattractiveaiminneuroscienceinordertoachievebothhightemporalandspatialresolutionforthenon-invasivestudyofcognitivebrainfunction.Inthispaper,werecordsimultaneousEEG-fMRIofthesamesubjectinemotionalprocessingexperimentinordertoexplorethecharacteristicsofdifferentemotionalpictureprocessing,andtrytofindthedifferenceofthesubjects’brainhemispherewhileviewingdifferentvalenceemotionalpictures.Thelatepositivepotential(LPP)isareliableelectrophysiologicalindexofemotionalperceptioninhumans.Accordingtotheanalysisresults,theslow-waveLPPandvisualcorticalbloodoxygenlevel-dependent(BOLD)signalsarebothmodulatedbytheratedintensityofpicturearousal.TheamplitudeoftheLPPcorrelatesignificantlywithBOLDintensityinvisualcortex,amygdala,temporalarea,prefrontalandcentralareasacrosspicturecontents.
简介:视频或电视录像脑电图(Video-EEG)监测是鉴别癫痫发作性质及类型的最有效的检查方法,也是国际上普遍采用的癫痫和癫痫综合征分类的重要依据之一.目前正在国内普及,其优势在于可以同步观察到病人发作症状和脑电图变化,实现了视频图像、声音和脑电三者的统一,对癫痫发作的鉴别诊断及分型均有难以取代的临床价值.目前报道的一些新的癫痫发作类型及综合征无不有赖于视频脑电图监测的证实.但是,如果没有监测到癫痫发作,与常规脑电图有何区别?过去有的报道仅"监测"数十分钟,而且90%以上未监测到发作,这就使这样一个现代化优势明显的设备成为只有经济效益的检查,失去其临床应用价值.对此,有关视频脑电图的临床应用价值做进一步探讨.
简介:InordertoexplorethecorrelationbetweentheadjacentsegmentsofalongtermEEG,animprovedprincipalcomponentanalysis(PCA)methodbasedonmutualinformationalgorithmisproposed.Aone-dimensionEEGtimeseriesisdividedequallyintomanysegments,sothateachsegmentcanberegardedasanindependentvariablesandmulti-segmentedEEGcanbeexpressedasadatamatrix.Then,wesubstitutemutualinformationmatrixforcovariancematrixinPCAandconducttherelevanceanalysisofsegmentedEEG.Theexperimentalresultsshowthatthecontributionrateoffirstprincipalcomponent(FPC)ofsegmentedEEGismorelargerthanothers,whichcaneffectivelyreflectthedifferenceofepilepticEEGandnormalEEGwiththechangeofsegmentnumber.Inaddition,theevolutionofFPCconducetoidentifythetime-segmentlocationsofabnormaldynamicprocessesofbrainactivities,theseconclusionsarehelpfulfortheclinicalanalysisofEEG.
简介:目的:了解长程数字化视频EEG与过度换气EEG的癎样放电规律.方法:评估52例颞叶癫癎患者长程数字化视频EEG与过度换气EEG的癎样放电特点.结果:过度换气EEG癎样放电检出率明显低于浅睡期EEG,差异具有极显著意义(P<0.01);但与清醒期和深睡期EEG癎样放电检出率比较差异无显著意义(P>0.05).结论:颞叶癫癎患者浅睡期EEG癎样放电率明显高于过度换气EEG,对颞叶癫癎患者进行睡眠EEG检测,有助于提高癎样放电的检出率.