Classic EEG (ERPs)/ Advanced EEG

Quentin Noirhomme

OutlineOutline

• Origins of MEEGOrigins of MEEG

• Event‐related potentials

i f d i i• Time‐frequency decomposition

• Source reconstruction

Before to startBefore to start

• EEGlabEEGlab

• Fieldtrip (included in spm)

Part I: OriginsPart I: Origins• EEG Discovered by Hans Berger in 1924• Non invasive measure of electrical brain activityy

Origins: MEGOrigins: MEG

• 19681968

OriginsOrigins

Baillet et al., IEEE Sig. Proc. Mag., 2001

Origins: PotentialsOrigins: Potentials

OriginsOrigins

Baillet et al., IEEE Sig. Proc. Mag., 2001

M/EEG vs. fMRIM/EEG vs. fMRI

Raw EEGRaw EEG

EEG in comaEEG in coma

Fp2‐T4

Burst Suppression Alpha coma Isoelectric

Fp2 T4

T4‐02

Fp2‐C4

C4‐02

Fp1‐T3

T3‐01

50 V

T3 01

Fp1‐C3

1 s 1 s 1 s

50 µV

50 µV 20 µV 20 µVC3‐01

Thömke et al. BMC Neurology 2005 5:14  doi:10.1186/1471‐2377‐5‐14

EEG in sleepEEG in sleep

http\\:www.benbest.com

EEG RhythmsEEG Rhythms

http://members.arstechnica.com/x/albino_eatpod/specific‐eeg‐states.gif

• Gamma : > 30 Hz

EEG eventsEEG eventsBurst

SpikesSpikes

http://members.arstechnica.com/x/albino_eatpod/specific‐eeg‐states.gif

Part II: Event‐Related potentialsPart II: Event Related potentials

Wolpaw et al., 2000

AveragingAveraging

Adapted from

 Tallon‐Bauudry and Bertrrand, 1999

Average potential (across trials/ subjects) relative to somespecific event in time

PreprocessingPreprocessing

1 Filtering1. Filtering

2. Segmentation

3 if j i3. Artifact rejection

4. Averaging

5. Baseline removal

FilteringFiltering

• Why filter?– EEG consists of a signal plus noise– Some of the noise is sufficiently different in frequency content from the signal that it can be suppressed i l b tt ti diff t f i thsimply by attenuating different frequencies, thus making the signal more visible• Non‐neural physiological activity (skin/sweat potentials)potentials)

• Noise from electrical outlets• Highpass filter to remove drift due to sweating, …• Notch filter to remove the line noise (50‐60Hz)• Low‐pass filter (often 30Hz for ERP)

SegmentationSegmentation

ArtifactsArtifacts

ArtifactsArtifacts

http://www.bci2000.org

ArtifactsArtifacts

http://www.bci2000.org

ArtifactsArtifacts

http://www.bci2000.org

ArtifactsArtifacts

http://www.bci2000.org

Artifact rejectionArtifact rejection

• Visual inspection of the dataVisual inspection of the data

• Thresholding (e.g., everything above 100µV)

S i i l h d• Statistical method 

• Independent component analysis – good for blinks and other visual artifacts

• Help if you have EOG and EMG channelsp y

• Do not trust automatic methods

AveragingAveraging

AveragingAveraging

• Assumes that only the EEG noise varies from trial to trialssu es a o y e o se a es o a o a

• But – amplitude and latency will vary

Averaging: effects of varianceAveraging: effects of variance

L t i ti bLatency variation can be a significant problem

AveragingAveraging

• Assumes that only the EEG noise varies from trial to trialssu es a o y e o se a es o a o a

• But – amplitude and latency will vary• S/N ratio increases as a function of the square root of the 

number of trials. • It’s always better to try to decrease sources of noise than 

to increase the number of trialsto increase the number of trials.

Baseline correctionBaseline correction

• Remove the mean of the recorded baselineRemove the mean of the recorded baseline (e.g., ‐200 ms to 0 ms)

• Variation in baseline duration can induce• Variation in baseline duration can induce change in potential amplitude

I di id ll f h l d• Individually for each electrode

• SPM does it automatically while segemting the data

Part III: Time‐frequency decompositionPart III: Time frequency decomposition

Adapted from Tallon‐Baudry and Bertrand, 1999

Evoked frequencyEvoked frequency

Adapted from Tallon‐Baudry and Bertrand, 1999

Induced frequency decompositionInduced frequency decomposition

Adapted from Tallon‐Baudry and Bertrand, 1999

Induced frequency decompositionInduced frequency decomposition

Adapted from Tallon‐Baudry and Bertrand, 1999

Time‐frequency decompositionTime frequency decomposition

Adapted from Tallon‐Baudry and Bertrand, 1999

Continuous Morlet waveletContinuous Morlet wavelet

http://amouraux.webnode.com/.

AnalysisAnalysis

• Grand mean ‐> Average across subjectGrand mean  > Average across subject

• Convert ERP or TF decomposition into  imagesfi t/ d l l l i– =>    first/second‐level analysis

• Source reconstruction – =>  first/second‐level analysis

1st Level Analysis1 Level Analysis

• select periods or time points in peri‐stimulus timeselect periods or time points in peri stimulus time Choice made a priori.

• sum over all time points

Part IV: Source reconstructionPart IV: Source reconstruction

From www.imt.uni‐luebeck.de, 2008

Source reconstructionSource reconstruction

1 Forward Model1. Forward Model

2. Inverse reconstruction

Forward modelingForward modeling• Electromagnetic head model• Reconstruct electrode signals from electricalReconstruct electrode signals from electricalcurrent in the head

Head modelHead model

Spherical approximation Realistic head modelSpherical approximation Realistic head model

• Boundary element method• Finite element method

SPM head modelSPM head model

Compute transformation T

TemplatesIndividual MRI

Templates

Apply inverse transformation T‐1

Individual meshIndividual mesh BEM mesh

Head modelHead model

• Electrode locationsElectrode locations

• Registration L d k b d– Landmark based

– Surface matching fiducials

• Leadfieldfiducials

Rigid transformation (R,t)

Individual MRI spaceIndividual sensor space

Inverse approachesInverse approaches

Dipole Distributed dipolesDipole Distributed dipoles

Least‐square or Beamforming More unknowns than data

Distributed approachDistributed approach

• Y = KJ+ EY KJ+ E• No unique solution!

P i i ( ||Y KJ||2 + λf(J) )– Priors:            min( ||Y – KJ||2 + λf(J) )• minimum overall activity

• Location• Location

• Smoothness

• Bayesian model comparison• Bayesian model comparison

ReferencesReferences

• Sylvain Baillet’s presentation at HBM 2008Sylvain Baillet s presentation at HBM 2008• SPM for dummies 0000‐2008 presentations• http://www bci2000 org• http://www.bci2000.org• Baillet et al., IEEE Sig. Proc. Mag., 2001M tt t Philli & F i t (2005) SPM• Mattout, Phillips & Friston (2005) SPM coursehttp://www.fil.ion.ucl.ac.uk/spm/course/slide05/ t/MEEG i ts05/ppt/MEEG_inv.ppt 

• SPM manual