Abstract
Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bayesian approximation error (BAE) approach is exploited. In BAE, we first postulate a probability distribution for the skull conductivity that describes our (lack of) knowledge on its value, and model the effects of this uncertainty on EEG recordings with the help of an additive error term in the observation model. Before the Bayesian inference, the likelihood is marginalized over this error term. Thus, in the inversion we estimate only our primary unknown, the source distribution. We quantified the improvements in the source localization when the proposed Bayesian modelling was used in the presence of different skull conductivity errors and levels of measurement noise. Based on the results, BAE was able to improve the source localization accuracy, particularly when the unknown (true) skull conductivity was much lower than the expected standard conductivity value. The source locations that gained the highest improvements were shallow and originally exhibited the largest localization errors. In our case study, the benefits of BAE became negligible when the signal-to-noise ratio dropped to 20 dB.
| Original language | English |
|---|---|
| Pages (from-to) | 252-260 |
| Number of pages | 9 |
| Journal | NeuroImage |
| Volume | 188 |
| Early online date | 6 Dec 2018 |
| DOIs | |
| Publication status | Published - 1 Mar 2019 |
Keywords
- Bayesian inverse problem
- Electroencephalography
- Skull conductivity
- Source localization
- Uncertainty modelling
ASJC Scopus subject areas
- Neurology
- Cognitive Neuroscience
Cite this
Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity. / Rimpiläinen, Ville; Koulouri, Alexandra; Lucka, Felix; Kaipio, Jari P.; Wolters, Carsten H.
In: NeuroImage, Vol. 188, 01.03.2019, p. 252-260.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity
AU - Rimpiläinen, Ville
AU - Koulouri, Alexandra
AU - Lucka, Felix
AU - Kaipio, Jari P.
AU - Wolters, Carsten H.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bayesian approximation error (BAE) approach is exploited. In BAE, we first postulate a probability distribution for the skull conductivity that describes our (lack of) knowledge on its value, and model the effects of this uncertainty on EEG recordings with the help of an additive error term in the observation model. Before the Bayesian inference, the likelihood is marginalized over this error term. Thus, in the inversion we estimate only our primary unknown, the source distribution. We quantified the improvements in the source localization when the proposed Bayesian modelling was used in the presence of different skull conductivity errors and levels of measurement noise. Based on the results, BAE was able to improve the source localization accuracy, particularly when the unknown (true) skull conductivity was much lower than the expected standard conductivity value. The source locations that gained the highest improvements were shallow and originally exhibited the largest localization errors. In our case study, the benefits of BAE became negligible when the signal-to-noise ratio dropped to 20 dB.
AB - Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bayesian approximation error (BAE) approach is exploited. In BAE, we first postulate a probability distribution for the skull conductivity that describes our (lack of) knowledge on its value, and model the effects of this uncertainty on EEG recordings with the help of an additive error term in the observation model. Before the Bayesian inference, the likelihood is marginalized over this error term. Thus, in the inversion we estimate only our primary unknown, the source distribution. We quantified the improvements in the source localization when the proposed Bayesian modelling was used in the presence of different skull conductivity errors and levels of measurement noise. Based on the results, BAE was able to improve the source localization accuracy, particularly when the unknown (true) skull conductivity was much lower than the expected standard conductivity value. The source locations that gained the highest improvements were shallow and originally exhibited the largest localization errors. In our case study, the benefits of BAE became negligible when the signal-to-noise ratio dropped to 20 dB.
KW - Bayesian inverse problem
KW - Electroencephalography
KW - Skull conductivity
KW - Source localization
KW - Uncertainty modelling
UR - http://www.scopus.com/inward/record.url?scp=85058408352&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2018.11.058
DO - 10.1016/j.neuroimage.2018.11.058
M3 - Article
VL - 188
SP - 252
EP - 260
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
ER -