Introduction to ElectroEncephaloGraphy (EEG) and ... · Introduction to ElectroEncephaloGraphy (EEG) and MagnetoEncephaloGraphy (MEG). Outline zOrigin of EEG and MEG signals zEEG
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Christophe Grova Ph.DBiomedical Engineering DptNeurology and Neurosurgery DptMontreal Neurological Institute, McGill, UniversityCentre de Recherches Mathématiques
christophe.grova@mcgill.ca
NEUR 570 Human Brain Imaging BIC Seminar 2011
Introduction toElectroEncephaloGraphy (EEG) and MagnetoEncephaloGraphy (MEG).
Outline
Origin of EEG and MEG signals
EEG and MEG data acquisition
Source localization
Outline
Origin of EEG and MEG signals
EEG and MEG data acquisition
Source localization
A bit of history …
1st EEG human recording: Dr. Hans Berger in 1925
1st MEG human recording: Dr. David Cohen in 1972
Several brain imaging techniques
Temporal scale
FDG PET
Spa
tial s
cale
(in
mm
)
8
6
4
2
10
Anatomical MRI
1 ms 1 s 40 s static
SimultaneousEEG/fMRI
Hemodynamic brain response associated to
EEG activity
Glucose metabolism
Intra-cerebralEEG
recordings
Reference
Scalp topographies
SPECT
Brain perfusion
EEG64 electrodes
MEG275 sensors
Epileptic spike
Electric vs magnetic fields
Moving electric charges, an electric current, create a magnetic field B
Electric vs magnetic signals in the brain
Magneto-EncephaloGraphy (MEG):measures changes in magnetic fields on the scalp: sensitive to neuronal currentsElectro-EnphaloGraphy (EEG): measures differences of electric potentials on the scalp: sensitive to conduction (volume) currents
If there was air between the brain and the skull, no EEG could be measured on this subject, but it would be possible to measure MEG
The electro-magnetic dipole model
r’
Jp(r’)
Current dipole
r Measurement point
Electric potential (in free space)
Magnetic field (in free space)
Main generators or electro-magnetic scalp activity: pyramidal cells
Generation of a signal one can detect from scalp measurements
Synchronisation of potentiels post-synaptic potentials along the cortical surface
Organization of pyramidal cells along the cortical surface
Neuronal conduction vs volume conduction
Neuronal conduction: (primary currents)
– Action potentials (1ms), – Post synaptic potentials (10ms)– Active conduction.– Origin of MEG signals
Volume conduction:(secondary currents)
– The brain is a conductive medium– Instantaneous propagation of
electric fields– Passive conduction. – Origin of EEG signalsBaillet et al 2001
Magneto-encephalography (MEG)
Measures magnetic fields generated outside the head from neuronal currentsMagnetic and electric fields are perpendicularThere is no influence of the skull on the propagation of magnetic fieldsRadial sources do not contribute to MEG signals
Differences between EEG and MEG
Although the same neurophysiological processes generate EEG and MEG:
1. Magnetic fields are not distorted by resistance from skull and scalp ⇒ better spatial resolution in MEG?
2. Electrical and magnetic fields are oriented perpendicularly to each other
Hamalainen et al 1993
Differences between EEG and MEG cont
3. Scalp EEG is sensitive to both tangential and radial components of a current source in a sphere, while neuromagnetometers detect only its tangential components
- a radial dipole does not produce a magnetic field outside the sphere
David Cohen NMH/MIT
Differences in EEG and MEG cont
- Thus, MEG selectively measures activity in the sulci.
- EEG measures activity from both the gyri and the sulci (dominated by radial sources)
David Cohen NMH/MIT
Epileptic activity: complementarity btw MEG and EEG
Merlet et al 1997
Outline
Origin of EEG and MEG signals
EEG and MEG data acquisition
Source localization
Electro-encephalography (EEG)
10/20 System (19electrodes)
Measurement of scalp Electric potentials
• Measures potentials generated by volume conduction currents (from 19 to 256 electrodes)• Scalp potentials are attenuated and distorted by the skull (highly resistive)
Electro-encephalography (EEG)
10-20 system 19 electrodes
Background Signal EEG
256 electrodes
EEG during wakefulness
From Dr. Gotman BMDE501 lecture
EEG during sleep
From Dr. Gotman BMDE501 lecture
EEG during an epileptic seizure
From Dr. Gotman BMDE501 lecture
MEG measures magnetic fields related to brain activityfrom femtoTesla (10-15 T) to picoTesla (10-12 T)
Earth’s magnetic field: 4,710-5 T.
small magnetic field measurements lead to artifacts
MEG brain signals = hearing the noise of a pin falling on a sofa in a dance club !
(M. Hamalainen)
Noise sources
Challenges in MEG data acquisition
Brain magnetic fields are tiny (pT,10-12T):– MRI: 1,000,000,000,000,000 (=1T)– Earth magnetic field: 100,000,000,000– Magneto-CardioGram (MCG): 100,000– MEG signals: 1,000– Sensitivity of magnetometer: 10
Highly sensitive sensors: SQUID (Superconducting QUantumInterference Device) in liquid helium (-269ºC)Noise reduction:
– Magnetically shielded room– First order gradiometer to eliminate remote interference and record
only local field– Reference sensors
MEG is sensitive to head motion: head localization is requiredMEG setup costs approximately $3M
MEG Systems
CTF – 275 sensors 4-D – 148 or 248 sensors,
– www.4dneuroimaging.comNeuroMag – 306 sensorsKIT - KanazawaOther (Los Alamos)
An highly sensitive sensor of magnetic field (fT)
SQUID: Superconducting Quantum Interference Devices
invented in 1965 by James Edward Zimmerman and Arnold Silver at Ford Research Lab.
All the system requires superconducting state: Liquide Helium + Cryogenic Dewar
Environmental Noise Reduction
Shielded Room Gradiometers Reference sensors used to pick up noise(3rd order gradient of env. noise)
Opening a MEG device
Reference sensors
Passive noise reduction:
s
Power lines orother current lines
Movingmagnetic dipoles
Reference system
Magnetically shielded room(mu metal)
Magnetic sensors are subjected not only to the measured MEG signal S, but also to unwanted signals (environmental noise, signals from parts of brain not being measured and other body parts).
Active noise cancellation: coils + external magnetometer to compensate external noise
MEGsystem
(a)
shieldedroom
(b)
Noise cancellation coils
Active noise cancellation. (a) coil system in an unshielded environment; (b) coil system combined with a shielded room.
Synthetic noise cancellation: flux transformers estimating local gradient of magnetic field
Unshielded, B
Powerline
Shielded, B
5 fT rms/√Hz
Vibrationalnoise
Frequency (Hz)1 fT
10 fT
100 fT
1 pT
10 pT
100 pT
1 nT
10 nT
100 nT
1 µT
10 µT
0.01 0.1 1 10 100
Magnetometers
3rd-order gradiometer
(b)
Noi
se (
B o
r G
(3) ·d
1·d 2
·d3)
(rm
s/√H
z)
Unshielded, G
Powerline
Vibrationalnoise
Frequency (Hz)1 fT
10 fT
100 fT
1 pT
10 pT
100 pT
1 nT
10 nT
100 nT
1 µT
10 µT
0.01 0.1 1 10 100
1st-order gradiometers
Shielded, G
5 fT rms/√Hz
(a)
3rd-order grad.Noi
se (
G(1
) ·d o
r G
(3) ·d
1·d 2
·d3)
(rm
s/√H
z)
MEG is sensitive to head motions during data acquisition
Continuous head localization system
Three emitting coils usedfor continuous head localization:Nasion and Peri-auricular points
3D localization of the three localization coils, the head shape and EEG electrodes on the subject’s head
Use of a magnetic device (Polhemus) for 3D localization
Co-registration with the subject’sanatomy (skin surface segmented from anatomical MRI)
MEG sensors (red) +EEG electrodes (blue) Co-registered on skinsurface
EEG electrodes (blue) + Digitized Head Shape (red)Co-registered on skinsurface
Few EEG/MEG examples
Epileptic activity : interictal spikes
Interictal spikes are spontaneous activity generated by the brain without any clinical sign
Multimodal exploration is feasible
Intra-cerebral EEG recordings showed that interictal spike generators are rarely focal (Merlet I. et al. Clin. Neurophys. 1999)
A minimum brain activated area of 6 cm2 is needed to generate a spike on the scalp (Ebersole J. Clin. Neurophys. 1997), spike generators may also be quite more extended than 6 cm2
A minimum brain activated area of 3 cm2 is needed to generate a spike on MEG
EEG interictal spike
Epileptic spikes in EEG
Epileptic spikes in MEG
Generation of evoked activity: averaging will increase the Signal-to-Noise ratio
Average of the 20 trials
Trial 1
Trial 2
Trial 3
….
Visual evoked field / potential
Visual evoked field / potential (left stimulation)
MEG topography EEG topography Source localization
Somatosensory vs motor evoked field (potential)
90ms after left thumb pneumatic stimulation
20ms after left thumb
tapping
Somatosensory vs motor evoked field (potential)
90ms after left thumb pneumatic stimulation
20ms after left thumb
tapping
Outline
Origin of EEG and MEG signals
EEG and MEG data acquisition
Source localization
Is it possible to localize source of brain activity from scalp measurements ?
Source localization
?
• Forward problem = modelling : knowing where are the sources, computation of the EEG/MEG signals generated by these sources
?
• Inverse problem: estimation of sources of brain activity from EEG/MEG scalp recordings
Forward problem
• Knowing brain sources and a model of the head, one can compute corresponding electric potentials or magnetic fields generated on the scalp
?
Spherical model: analytical solution
Realistic models of the head
a : spherical model (analytical solution)b : realistic surface model (BEM) → BrainStormc : realistic volume model (FEM)
The Direct Problem is Relatively Simple
“We know the geometry and the electrical and magnetic properties of the brain, CSF, skull and scalp”The geometry is complex: we need a mathematically manageable representation of the head. The simplest is a sphere; the more complex is a realistic head model.The electrical properties are complex: electrical conductivities cannot be measured in vivo. Bone conductivity is quite variable and is the most important factor.Magnetic properties are more homogeneous across tissues (less influence of the bone)
The Inverse Problem
Given a distribution of electric potentials or magnetic fields at the surface of a volume conductor, where are the sources inside the volume giving rise to this distribution, and what are their characteristics?
Inverse problem
The Inverse Problem is Hopeless
There is an infinite number of distribution of sources inside a volume conductor that can give rise to the samepotential distribution at the surface of the conductor (Helmholtz, 1853).There is an infinite number of possible arrangement of sources inside the brain giving rise to a particular EEG or MEG signalThe inverse problem is hopeless in theory. It can only be solved with simplifying assumptions = constraints
Selection of the more appropriate model
One need to add assumptions in the model to be able to find a unique solution
Are they realistic ?
Models of the sources of brain activity
Equivalent current dipolenon-linearnb of sources ?what is an ECD ?
Distributed sourcesanatomical constraint linearp = 103 sources n= 102 electrodes ill-conditioned pbneeds regularization
Summary: any source localization method relies on some a priori assumptions
Number of generators well-knownECD approaches
Few decorrelatedsources, number unknownDipole scanningapproaches
Distributed network and/or extended sourcesDistributed sources approaches
Model of signal generation
M
JLead field (forward pb)
signal
Noise
Model of signal generation
M
J sources
Lead field (forward pb)
signal
Noise
Model of signal generation
M
J sources
Lead field (forward pb)
signal
Noise
Model of signal generation
M
J sources
Lead field (forward pb)
signal
Noise
Model of signal generation
M
J sources
Lead field (forward pb)
signal
Noise
Estimate of J using and Equivalent Current Dipole
Model of signal generation
M
J sources
Lead field (forward pb)
signal
Noise
Estimate of J using and Equivalent Current Dipole
Estimate of J constrained on The cortical surface
The Equivalent Current Dipole
If one assumes that the EEG or MEG signals are generated by one or a small number of dipole sources, then it is possible to solve the inverse problem.A dipole is a point source, defined by its location (3 parameters), its orientation (2 parameters) and its moment (1 parameter).Is the dipole model reasonable?
Estimation of the Equivalent Current Dipole
Several types of dipole models:– Moving dipole: Position ?, Orientation ?, Amplitude ?– Rotating dipole: fixed position, Orientation ?, Amplitude ?– Fixed dipole: fixed position, fixed orientation, Amplitude ?
Number of dipoles:– Estimation of the signal subspace (PCA)– Iterative approaches
Solving the inverse problem:– Non-linear optimisation to find dipole locations– Minimisation of residual variance:
M = G(Φ) .J + E G: Lead field matrixΦ: dipoles location and orientationJ: amplutide of the the dipoleE: Noise
Minisation of residual variance (RV)
RV = || M - G(Φ) .J ||2 / || M || 2
Total varianceVariance non explained by the model
RV is not a validation metric !!!A misleading solution can provide a very low RV
Is the Dipole Model Reasonable?
In primary sensory evoked experiments, particularly somatosensory and auditory, the dipole model appears justified (it has been validated by comparing the results of modeling to what we know about sensory physiology).In other situations, particularly in epilepsy, the dipole model requires validation.
Can the Dipole Model be Misleading?
YESIn most instances, it is possible to find a dipolar source that can model well a scalp distribution. This only indicates that the dipole is a possible source of the distribution. It does not prove that it is the source of the distribution.There are systematic errors caused by the fact that the source is likely to have a significant spatial extent.
Confidence intervals
The Dipole Model: Conclusions
Very powerful method to find intracerebral sources from a scalp recording.Only valid if its underlying assumptions are correct (that sources are dipolar).Appears to localize well sources of primary sensory activity.Localizes reasonably well the maximum of extended sources of epileptic activity, although secondary (small amplitude) sources are probably less reliable.No information on extent of actual source.Main limitation: number of dipoles
Solving the inverse problem using distributed sources
BJGM +⋅=Inverse pb: LINEAR, but ill-posed.• Under-determined equation system:
102 measures 104 dipole sources (=unknown)
• Lead field G = ill-conditioned
Regularization is needed to find a solution(requires a priori assumptions)
Classical assumptions
Solution of minimum energy: Minimum NormHamalainen et al. Med Biol. Eng. Comput. 94
Solution maximum spatial smoothness: LORETAPascual-Marqui et al. Int. J. Psychophys. 94
Minimum Norm Estimate
LORETA: maximum of spatial smoothing
Anatomical constraints: sources distributed on the cortical surface
Dale A., Sereno M., 1993. J. Cogn. Neurosci. 5, 162– 176.
Extraction of a distributed sources model from an anatomical MRI
3D T1-weighted MRI acquisition: Matrix = 170x256x256, voxel = 1mmTR = 22 ms, TE = 9.2 ms
Automatic segmentation of the cortical surface = White Matter/Grey Matter interfaceBrainvisa: http://www.brainvisa.info
Regularization
Inverse problem= ill posed problem (10,000 sources vs 100 sensors)
No unique solutionThe problem needs regularization (assumptions)
1. Minimum norm (MN): minimum of energy (may be weighted W). Minimise ||M-GJ||2 + α ||WJ||2
2. LORETA : maximum of smoothness.
Minimise ||∆J||2 under the constraint M = GJwhere ∆ is a discrete spatial Laplacian operator
3. MEM : maximum entropy of the mean
Minimum Norm Estimate within the Bayesian Framework (1/5)
Linear distributed model for source localization
M: n x t signal on the n scalp sensors G: n x p forward model of Gain matrixJ: p x t current density distribution on the sources along the cortical surfacep >> n
Bayes Law:
Minimum Norm Estimate within the Bayesian Framework (2/5)
Bayes Law:
A priori distribution of the sensor noise E:Gaussian distribution with null mean andAssumption of uncorrelated noise
Data likelihood:
A priori distribution of the source distribution J:Gaussian distribution with null mean and
Minimum Norm Estimate within the Bayesian Framework (3/5)
Bayes Law:
Maximum likelihood solution
Data fit term
Regularization hyperparameter
Weighted Min. Energy constraint
Minimum Norm Estimate within the Bayesian Framework (4/5)
Bayes Law:
Maximum likelihood solution
This solution depends on the regularization hyparameter α
α can be estimated using the L-curve technique
Minimum Norm Estimate within the Bayesian Framework (5/5)
Maximum likelihood solution
Estimation of the regularization hyparameter α using the L-curve technique
Examples of source localisation of an epileptic spike using anatomical constraints
fMRI activation for a similar spike
Regularizing the ill-posed linear model: M = GJ +E
2. Maximum Entropy on the Mean (MEM)
Data fit: set of all distributions pJ(J)explaining the data on average
pJ(J)
µJ(J) Reference distribution = a priori information
Relative entropy
p*J(J)MEM
solution
MEM solution is the one with maximum µ−entropy, i.e., the one that makes the least assumption regarding missing informationPrior information on J:
Parcelling of the cortical surface in K parcels:
Validation Results: 4th order spatial extent (14 cm2)
Gold Standard:
Temporo-Radial Source
MN : AUC = 0.78 MEM : AUC = 0.93 LORETA : AUC = 0.99Gold Standard:
Temporo-Tangential Source
MN : AUC = 0.79 MEM : AUC = 0.93 LORETA : AUC = 0.96
Grova C, Daunizeau J, Lina JM, Benar CG, Benali H, Gotman J. Neuroimage. 2006 Feb 1;29(3):734-53.
Validation Results: MEG source localization (1/2)
Chowdhury R. et al, Proc. of HBM 2010 conference
Application of model evidence for model comparison: Henson et al. Neuroimage 2009
Validation: comparison between MEM sourcelocalization and intracranial EEG recordings
Lina et al, IEEE TBME Sumitted
Summary: any source localization method relies on some a priori assumptions
Number of generators well-knownECD approaches
Few decorrelatedsources, number unknownDipole scanningapproaches
Distributed network and/or extended sourcesDistributed sources approaches
• Requires a good knowledge of the signal to be localized• Requires statistics (SPM-like, non parametric)• Requires the comparaison of several methods
Take home messages
Complementarity between EEG and MEG: – MEG signals are not distorted by the skull (better spatial accuracy)– MEG can see only tangential sources whereas EEG can see tangential and radial
sources– MEG data acquisition is challenging: tiny magnetic fields in a noisy environment
EEG/MEG source localization:– Any source localization requires an a priori model of the underlying sources– Main models:
Equivalent current dipoleDipole scanning approachesDistributed sources (along the cortical surface)
– Model comparison or model selection is required (hypothesis test)Multimodal data fusion:
– There is no one to one correspondance between EEG source, MEG source and BOLD response: COMPLEMENTARITY
– EEG/MEG sources can be associated either with BOLD activation or deactivation– EEG/MEG signals require synchronization of neuronal activity,
the BOLD signal does not
Suggested Bibliography
Niedermeyer’s ElectroEncephalography: Basic Principles, Clinical Applications and Related Fields. 6th Edition, Ed. D.L. Schomer and F.H. Lopes da Silva. Wolters Kuwer, Lippincott Williams and Wilkins 2011.MEG: an introduction to methods. Eds. P.C. Hansen, M.L. Kringelbach and R.Salmelin Oxford University Press 2010.
Review PapersBaillet S., Mosher J., Leahy R., 2001. Electromagnetic brain mapping. IEEE Signal Process. Mag., 14–30.Hamalainen, M, Hari, R, Ilmoniemi, R, Knuutila, J, Lounasmaa, O (1993). Magnetoencephalography--theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys, 65: 1-93.Michel C., Murray M., Lantz G., Gonzales S., Spinelli L., Grave de Peralta R., 2004. EEG source imaging. Clin. Neurophysiol. 115, 2195– 2222.
• 275 magnetometers (MEG sensors)
• 64 simultaneous EEG
• all channels sampled @ up to 12kHz
• multimodal stimulus presentation (video, audio, somesthetic, ...)
• audio, video subject monitoring
• operates in upright or supine positions
• located in the Neuro’s new extension
MEG system @ The Neuro
MEG @ The Neuro:more information
NEW COURSE offered in January 2012: BMDE 610 Functional Neuroimaging fusion
Registration is now open on Minerva for Winter 2012 Space is limited, register ASAP !!!
Acknowledgements
Montreal Neurological Inst.Eliane KobayashiM.L. Jones, M. AiguabellaM. Sangani, D. Rosenberg,M. Porras-Betancourt
The Multimodal Functional Imaging Lab.(Multi-FunkIm)Christophe Grova R. Chowdhury, Y. Potiez,A. Machado, T. Hedrich, A. Blanc
Ecole de Technologie Sup.Jean Marc LinaA.S. DubarryE. Lemay S. Deslauriers
Collaborators: -MNI – EEG/fMRI studies: J. Gotman, F. Dubeau, an their team !
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