Download - Classic EEG (ERPs)/ Advanced EEG
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