The preprint entitled:
"Comparing EEG/MEG neuroimaging methods based on localization error,
false positive activity, and false positive connectivity"
might be of interest to those working in the field of EEG/MEG neuroimaging.
The abstract can be found below.
Roberto D. Pascual-Marqui, PhD, PD
The KEY Institute for Brain-Mind Research, University of Zurich
Visiting Professor at Neuropsychiatry, Kansai Medical University, Osaka
EEG/MEG neuroimaging consists of estimating the cortical distribution
of time varying signals of electric neuronal activity, for the study
of functional localization and connectivity. Currently, many different
imaging methods are being used, with very different capabilities of
correct localization of activity and of correct localization of
connectivity. The aim here is to provide a guideline for choosing the
best (i.e. least bad) imaging method. This first study is limited to
the comparison of the following methods for EEG signals: sLORETA and
eLORETA (standardized and exact low resolution electromagnetic
tomography), MNE (minimum norm estimate), dSPM (dynamic statistical
parametric mapping), and LCMVBs (linearly constrained minimum variance
beamformers). These methods are linear, except for the LCMVBs that
make use of the quadratic EEG covariances. To achieve a fair
comparison, it is assumed here that the generators are independent and
widely distributed (i.e. not few in number), giving a well-defined
theoretical population EEG covariance matrix for use with the LCMVBs.
Measures of localization error, false positive activity, and false
positive connectivity are defined and computed under ideal no-noise
conditions. It is empirically shown with extensive simulations that:
(1) MNE, dSPM, and all LCMVBs are in general incapable of correct
localization, while sLORETA and eLORETA have exact (zero-error)
localization; (2) the brain volume with false positive activity is
significantly larger for MN, dSPM, and all LCMVBs, as compared to
sLORETA and eLORETA; and (3) the number of false positive connections
is significantly larger for MN, dSPM, all LCMVBs, and sLORETA, as
compared to eLORETA. Non-vague and fully detailed equations are given.
PASCAL program codes and data files are available. It is noted that
the results reported here do not apply to the LCMVBs based on EEG
covariance matrices generated from extremely few generators, such as
only one or two independent point sources.