Upload
others
View
1
Download
0
Embed Size (px)
Citation preview
General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
You may not further distribute the material or use it for any profit-making activity or commercial gain
You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Downloaded from orbit.dtu.dk on: Dec 08, 2021
Forward Models can be Inferred from EEG Data
Hansen, Sofie Therese; Hauberg, Søren; Hansen, Lars Kai
Publication date:2016
Document VersionPublisher's PDF, also known as Version of record
Link back to DTU Orbit
Citation (APA):Hansen, S. T., Hauberg, S., & Hansen, L. K. (2016). Forward Models can be Inferred from EEG Data. Postersession presented at 22nd Annual Meeting of the Organization for Human Brain Mapping, Geneva, Switzerland.
Forward Models can be Inferred from EEG DataSOFIE THERESE HANSEN ([email protected]), SØREN HAUBERG ([email protected])
AND LARS KAI HANSEN ([email protected])Department of Applied Mathematics and Computer Science,
Technical University of Denmark
MOTIVATIONAccurate 3D EEG imaging is contingent on a suitable forward model [1, 2].The forward model describes the propagation path of the EEG sources tothe EEG sensors [3]. Forward models are estimated based on head geome-try and conductivity assumptions thus requiring subject-specific information.We propose an alternative: Learn a forward model based on the EEG data ofthe subject and a data-driven prior over forward models.
FORWARD MODEL REPRESENTATION
ElectrodesScalp
OutermskullInnermskull
Cortex
Highmconductivity
Lowmconductivity
PCAmrepresentationmofmforwardmmodelmcorpus
MRImscans BEMmheadmmodel1 2
3
InfermamforwardmmodelmformamnewmsubjectmusingmEEGmdata
Generatemforwardmmodelsmwithmdifferentmconductivities
Segment
1. Structural scans of 16 subjects are obtained from the multi-subject mul-timodal neuroimaging dataset [4].
2. The sMRIs are for each subject segmented into cortex, skull and scalp.3. Each head model is combined with different skull:soft tissue conductiv-
ity ratios, generating 100 forward models for each subject. The forwardmodel corpus is decomposed using principal component analysis.
FORWARD MODEL INFERENCE
Candidate sources
Candidate sources
Am
plitu
de [V
] TheGfreeGenergyGisGanGapproximationGofGtheGmodelGevidenceG[5].GItGquantifiesGtheGcomplexityGandGdataGfitGofGtheGmodel.WeGobtainGtheGfreeGenergyGfromGtheGVariationalGGarroteG[6,G7]GandGuseGitGtoGinferGaGsuitableGforwardGmodel.
SIMULATION RECOVER A FORWARD MODEL FOR A NEW SUBJECT
Localization.error150
100
50
0[mm]
3200
3100
3000
Free.energy
forward.modelPredicted
modelTest.forward
F.-measure
0
1
0.8
0.6
0.4
0.2
1
Anterior Posterior Sou
rce.
activ
ity.[a
.u.]
True1
0
Time.samples5 15 2510 20
Estimated1
0
Time.samples5 15 2510 20
Source.distribution,.true.and.estimated
Tes
t.sub
ject
Tra
inin
g.su
bje
cts
Forward model PCA prior, Template head, Subject sMRI, Subject sMRI,build using: EEG data template σ template σ true σFree energy 2994 3192 3057 2956MSE 0.63 0.55 0.93 0.61F1-measure 1 0 0.44 0.5Localization error 0 19.6 mm 5.3 mm 2.9 mm
REAL EEG DATA - FACE PERCEPTION TASK
Free5energy
Sum5of5m
PCA5forward5model5with5minimum5free5energy
Free5energy
Time5after5stimulus5[s]
Sou
rce5
ampl
itude
5[a.u
.] 1
0.5
0
-0.5
0.1 0.15 0.2
Time5after5stimulus5[s]
Sou
rce5
ampl
itude
5[a.u
.] 1
0.5
0
-0.5
0.1 0.15 0.2
-1200
-1400
-1600
-1800
-2000
Forward5model5build5using5sMRI5and5template5conductivity
CONCLUSIONThe proposed framework provides simultaneous estimation of a for-ward model and the EEG sources.Forward models can be inferred for new subjects without subject-specific geometry and conductivity. Instead inference is based on aforward model representation and the recorded EEG of the new sub-jects.
Inferred forward models provides similar source distributions aswhen using forward models having structural information.
ACKNOWLEDGEMENT
The work was supported in part by the Novo Nordisk Foundation Interdisciplinary Synergy Pro-gram 2014 [‘Biophysically adjusted state-informed cortex stimulation (BASICS)’] and the Otto Møn-sted Foundation (STH), the Danish Research Council for Natural Sciences (SH), and the DanishInnovation Foundation (LKH).
REFERENCES
[1] Akalin Acar, Z., Makeig, S. (2013). Effects of forward model errors on EEG source localization. Brain Topography, 26(3), 378-396.[2] Stahlhut, C., Mørup, M., Winther, O., Hansen, L.K. (2011) Simultaneous EEG source and forward model reconstruction (sofomore)
using a hierarchical bayesian approach. Journal of Signal Processing Systems, 65(3).[3] Hallez, H. et al. (2007). Review on solving the forward problem in EEG source analysis. J. Neuroeng. and Rehabilitation, 4(46), 1-29.[4] Wakeman, D.G., Henson, R.N. (2015). A multi-subject, multi-modal human neuroimaging dataset. Scientific Data, 2, 150001.[5] Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., Penny, W. (2007). Variational free energy and the Laplace approximation.
NeuroImage, 34(1), 220-234.[6] Kappen, H.J., Gomez, V. (2014). The variational garrote. Machine Learning, 96(3), 269-294.[7] Hansen, S.T., Stahlhut, C., Hansen, L.K. (2013) Sparse Source EEG Imaging with the Variational Garrote, 3rd Int. Workshop on Pattern
Recognition in Neuroimaging.
For more information check out: Hansen, S.T., Hauberg, S., Hansen, L.K. (2016).Data-driven forward model inference for EEG brain imaging. NeuroImage, inpress. doi:10.1016/j.neuroimage.2016.06.017