Spatial projection as a preprocessing step for EEG source reconstruction using spatiotemporal Kalman filtering.
الباحث الأول:
Laith Hamid
الباحثين الآخرين:
Ali Al Farawn
, Isabelle Merlet
, Natia Japaridze
, Ulrich Heute
,
Ulrich Stephani
, Andreas Galka
, Fabrice Wendling
and Michael Siniatchkin
المجلة:
IEEE - Conference
تاريخ النشر:
None
مختصر البحث:
Abstract— The reconstruction of brain sources from non-
invasive electroencephalography (EEG) or magnetoencephalog-
raphy (MEG) via source imaging can be distorted by in-
formation redundancy in case of high-resolution recordings.
Dimensionali…
Abstract— The reconstruction of brain sources from non-
invasive electroencephalography (EEG) or magnetoencephalog-
raphy (MEG) via source imaging can be distorted by in-
formation redundancy in case of high-resolution recordings.
Dimensionality reduction approaches such as spatial projection
may be used to alleviate this problem. In this proof-of-principle
paper we apply spatial projection to solve the problem of
information redundancy in case of source reconstruction via
spatiotemporal Kalman filtering (STKF), which is based on
state-space modeling. We compare two approaches for incorpo-
rating spatial projection into the STKF algorithm and select the
best approach based on its performance in source localization
with respect to accurate estimation of source location, lack of
spurious sources, computational speed and small number of
required optimization steps in state-space model parameter es-
timation. We use state-of-the-art simulated EEG data based on
neuronal population models, for which the number and location
of sources is known, to validate the source reconstruction results
of the STKF. The incorporation of spatial projection into the
STKF algorithm solved the problem of information redundancy,
resulting in correct source localization with no spurious sources,
and decreased the overall computational time in STKF analysis.
The results help make STKF analyses of high-density EEG,
MEG or simultaneous MEG-EEG data more feasible.
