@inproceedings{d0569e03a74749b59bfe3b57af5de5fd,
title = "Bayesian modelling of skull conductivity uncertainties in eeg source imaging",
abstract = "Knowing the correct skull conductivity is crucial for the accuracy of EEG source imaging, but unfortunately, its true value, which is inter- and intra-individually varying, is difficult to determine. In this paper, we propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors related to erronous skull conductivity. We demonstrate the potential of the approach by simulating EEG data of focal source activity and using the dipole scan algorithm and a sparsity promoting prior to reconstruct the underlying sources. The results suggest that the greatest improvements with the proposed method can be achieved when the focal sources are close to the skull.",
keywords = "Bayesian modelling, Electroencephalography, Inverse problems, Skull conductivity",
author = "Ville Rimpil{\"a}inen and A. Koulouri and F. Lucka and Kaipio, \{J. P.\} and Wolters, \{C. H.\}",
year = "2017",
month = jun,
day = "13",
doi = "10.1007/978-981-10-5122-7\_223",
language = "English",
isbn = "9789811051210",
series = "IFMBE Proceedings",
publisher = "Springer",
pages = "892--895",
editor = "Hannu Eskola and Outi Vaisanen and Jari Viik and Jari Hyttinen",
booktitle = "EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017",
address = "Singapore",
note = "Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107 ; Conference date: 11-06-2017 Through 15-06-2017",
}