MarkWoolrichDiegoVidaurreAndrewQuinn

Romesh AbeysuriyaRobertBecker

OHBAM/EEGAnalysisWorkshop

WorkshopSchedule

• Tuesday• Session1:Preprocessing,manualandautomaticpipelines• Session2:Taskdataanalysisinsensorspace(subjectlevel)• Session3:Taskdataanalysisinsourcespace(subjectlevel)

• Thursday• Session4:Taskdataanalysisgrouplevel• Session5:Connectivityanalysisinsourcespace,subjectandgrouplevel• Session6:Hidden-Markov-Modeling inrestandtaskdata

Tuesday’sSchedule

09:30Session1lecture10:00CoffeeBreak10:10Session1practical11:30Session2lecture12:00Lunch(notprovided)13:00Session2practical14:20CoffeeBreak14:30Session3lecture15:00Session3practical16:30Finish

OSLPreprocessing

RobertBeckerOSLworkshopOHBA,Oxford

25.04.17

OSL=OHBA’ssoftwarelibrary

OSLiswritteninmatlabbutusesalsoanumberofdifferenttoolboxes:

• SPM• fieldtrip• FSL• osl-core(incl.OPT,OAT,ROInets,GLEAN,HMM-MARetc)• Utilities• …ProvidescompletepipelinetoprocessandanalyseyourMEGdata

MEGbasics:Howwemeasure

• ElektaNeuromagsystemhas306helium-cooledSQUIDsensors:102radialmagnetometersand204planargradiometers• Magnetometersmeasuremagneticfieldstrengthperpendiculartosurfaceofsensors(z-axis),gradiometersmeasuremagneticfieldchanges(xandydirection)• HasalsoEEGrecordingcapabilities+ECGandEOG

MEGandartefacts:Whatwe(wantto)measure

This!

Session1:Overview

• Introduction:typicalMEGartefacts• ManualPreprocessing:• Usingthemaxfilter• Filteringanddownsampling• VisualInspectionofdata:OSLview• De-noisingdata:artefactrejectionbyusingindependentcomponentanalysis(AFRICA)

• AutomatedPreprocessing:• UsingOSL’spreprocessingtool(OPT)

MEGandartefacts:Commonsources

• Biologicalartifacts• Saccades,blinks,microsaccades

• Muscularartefacts• Heartbeat• Respiration

• Electrical/other• 50Hzlinenoise• Scannerartifacts(jumps,spikes)• Channelsaturation• MRImagnetisation• Subjectmovements

Strategiesfordealingwithartefacts

• Beforeandduringacquisition:• Avoidthem(eyeblinks,movementsetc.)!

• Post-acquisition:• Maxfilternoisesuppression• Filteroutproblematicfrequencies• Removingbadperiodsbyvisualinspection• UseICAtoremoveproblematiccomponents• -->themoreyouknowaboutthem,thebetter,e.gbyrecordingECGandEOG(seelater)

Manualpreprocessing:Maxfilter

• MaxfilterisproprietarysoftwareprovidedbyElektafortheirscanner,whichimplementsasignalspaceseparation(SSS)algorithmtoreduceexternalnoise(bout):

Maxfiltercanalsodo:

MovementcompensationAlignmentDetectbadchannelsDownsampledata

Manualpreprocessing:Filteringanddownsampling• Efficientforremovingoflow-frequencydriftsandline-noise

• Downside:Mightdistortyoursignalofinterest:• Taskdata:Phase,latencyofevokedfields• Restdata:Connectivitymeasures?

• à Conservativeuseoffiltering;alwaysinspectyourdataafterfiltering!

• Downsampling:optional

Manualpreprocessing:Visualinspection forbadperiods• Importantstepduringmanualpreprocessing

• But:Equallyimportanttodouble-checkperformanceduringautomaticpreprocessing!

• Downside:Completelossofrejecteddata(channels/periods/trials)

• WewilluseourOSLviewdataviewertodothis:

Manualpreprocessing:Visualinspectionwith OSLview

channelvariances

“BadEpoch”segments

savebutton

changechanneltype

datainviewingwindow

Rightclickinthisspacetosetdata

to“Bad”

Averagechannelvariance

Manualpreprocessing:AfRICA:ArtefactrejectionusingIndependentComponentAnalysis(AfRICA)

ICAisadata-drivenBBSalgorithmtosplitourMEGdata(Y)

intoanumberoftemporallyindependentcomponents(S)byidentifyingthemixingmatrix(A).

YNsamples

Nchanne

ls

ANcomponents

Nchan

nels S

Nsamples

Ncompo

nentsx=

HowICAworksNon

-Gaussianity

(kurtosis)

Numberofmixedsignals ↑

Thekeyingredient:AmixtureofsignalsisalwaysmoreGaussian thantheunderlyingsignals (akathecentrallimittheorem).

• Means:Bysearchingforthesetofmaximallynon-Gaussiansignalswecanreversethemixingprocessandrecoverourunknownsources.

HowICAworksNon

-Gaussianity

(kurtosis)

Numberofmixedsignals ↑

• Means:Bysearchingforthesetofmaximallynon-Gaussiansignalswecanreversethemixingprocessandrecoverourunknownsources.• DoesnotworkwithGaussiansources…

Sources(brainactivity)

Sources(linenoise)Mixture(Y)

Thekeyingredient:AmixtureofsignalsisalwaysmoreGaussianthantheunderlyingsignals(akathecentrallimittheorem).

AfRICA:Classifyingcomponents

• Now,havingindependentcomponents isgreat,butwhichofthesourcesareartefacts?

• AfrICAofferswaystoguideyou:• Visualinspection

• Timecourse,spectrum,topography• Correlationwithexternalsignals

• IfyouhaveacquiredECGorEOGthenAfRICAwillsorttheindepentcomponentsaccordingtotheirsimilarity

AfrICA:Userinterfacemetrics(e.g.kurtosis,ECG,

EOG,mains)

sensortopographies

Setcomponentasbad(orrevertto

good)

powerspectrum

AfrICA:Typicalcomponents

Cardiac component

Eye blink component

Mains (50 Hz) component

AfrICA:Removingcomponents

• Onceweknowthecontributionofthebadcomponents

𝑌"#$%& = 𝐴"#$%& ∗ 𝑆"#$%&toourdata,wecansimplyremovethem:

𝑌+,-". = 𝑌 − 𝑌"#$%&

• ThislinearoperationcanberewrittenasamatrixoperationfromYtoYclean• àWheneveryouwant,youcansimplytransformyouroriginaldataY intoYclean byapplyingthatmatrixmultiplicationonthefly,i.e.online,noneedforanewdataset!• That’scalledan‘onlinemontage’!

Youroutputafterrunningthemanualpipeline

• Youshouldhavecreatednewfileswithaprefixindicatingthepreprocessingperformedonthedata,.e.g

• ‘f’forfiltering• ‘d’fordownsampling• ‘A’fortheAFRICAdenoiseddata• ‘e’forepoching

• SamenamingconventionfortheautomatedOPTpipeline!

Automaticpreprocessing withOPT

• Wouldn’titbegreattodoallthepreviousstepsjustautomaticallyandleanback?

• OSL’spreprocessingtool(OPT)allowsyoutodothat!

OPT’sfullyautomatedpipeline:

OPTrunsthroughthefollowingpipelinesteps(anyofwhichcanbeoptionallyturnedoff):• ElektaNeuromagdata:Runsthe "DoubleMaxfilterProcedure"• ConversionofdataintoSPMformat• Downsampling• Filtering(hi-pass,notch,…)• Markingbadsegments• AutomatedAFRICAdenoising• Coregistration (neededifintendingtodosubsequentanalysisinsourcespace)• Epoching(Ifappropriate)• Automatedoutliertrialandchannelrejection

OPTdatainput

Datacanbeinputas:

• Either(onlyforElektaNeuromagdata):• - thefullpathoftherawfiffiles(pre-SSS)topasstotheMaxfilter• Or:• - thefullpathoftheinput filesthatwillbepassedtotheSPMconvertfunction(for ElektaNeuromagdatathiswillbepost-SSS.fif files• Or:• - thefullpathofthe(alreadyconverted)SPMMEEGfiles

RunOPT

Useosl_check_optcalltosetupanOPTstruct:• opt=osl_check_opt(opt);• Requiresonlylimitedmandatorysettings• Fillsotherfieldwithdefaultvalues(whichcanthenbeadjustedbeforerunning)

Useosl_run_opttorunanOPT:• opt=osl_run_opt(opt);

OPToutput

• Resultsarestoredinthedirectoryspecifiedinopt.dirname,witha‘.opt’suffix• opt=osl_run_opt(opt) returnsaresultsfield:opt.results

• Thiscontains:• opt.results.logfile (filecontainingthematlabtextoutput)• opt.results.report:(Webpagereportwithdiagnosticplots)• opt.results.spm_files: (listofSPMMEEGobjectfilesforthecontinuousdata,e.g.topassintoanOATanalysis)• opt.results.spm_files_epoched: (listofSPMMEEGobjectfilesfortheepocheddata,e.g.topassintoanOATanalysis)

Today’spracticals

• Practicals+dataareontheOSLWiki

• Practicalcorrespondstothislecturesandhas twoparts:

• 1)ManualPreprocessingPipeline

• 2)AutomatedPreprocessingPipeline(OPT)

Recommendedreading

• LookatandusetheOSLWiki!• IndependentComponentAnalysis(easy,butawholebook)

• IndependentComponentAnalysis– ATutorialIntroduction– JamesV.Stone

• fastICA&ICASSO(advanced)• Hyvärinen,A.,1999.Fastandrobustfixed-pointalgorithmsforindependentcomponentanalysis.IEEETrans.NeuralNetw.10(3),626–634.

• ICAde-noisinginMEG(relevant)• Mantini,D.,etal.2011.ASignal-ProcessingPipelineforMagnetoencephalographyResting-StateNetworks.BrainConnectivity,1(1),49–59.

• Iwantademo!• Finnishcocktailpartyhere:https://research.ics.aalto.fi/ica/cocktail/cocktail_en.cgihttps://research.ics.aalto.fi/ica/cocktail/cocktail_en.cgi