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Neuroinform (2010) 8:285–299
DOI 10.1007/s12021-010-9086-6
Open Database of Epileptic EEG with MRI
and Postoperational Assessment of Foci—a Real
World Verification for the EEG Inverse Solutions
Piotr Zwolin´ski · Marcin Roszkowski ·
Jaroslaw Z˙ygierewicz · Stefan Haufe ·
Guido Nolte · Piotr J. Durka
Published online: 25 September 2010
© The Author(s) 2010. This article is published with open access at Springerlink.com
Abstract This paper introduces a freely accessible
database http://eeg.pl/epi, containing 23 datasets from
patients diagnosed with and operated on for drug-
resistant epilepsy. This was collected as part of the
clinical routine at the Warsaw Memorial Child Hos-
pital. Each record contains (1) pre-surgical electroen-
cephalography (EEG) recording (10–20 system) with
inter-ictal discharges marked separately by an expert,
(2) a full set of magnetic resonance imaging (MRI)
scans for calculations of the realistic forward models,
(3) structural placement of the epileptogenic zone,
recognized by electrocorticography (ECoG) and post-
surgical results, plotted on pre-surgical MRI scans in
transverse, sagittal and coronal projections, (4) brief
clinical description of each case. The main goal of
P. Zwolin´ski · M. Roszkowski
Memorial Child Hospital, Warsaw, Poland
P. Zwolin´ski
e-mail: pz@meddata.pl
M. Roszkowski
e-mail: m.roszkowski@czd.pl
J. Z˙ygierewicz (B) · P. J. Durka
Department of Physics, University of Warsaw,
Warsaw, Poland
e-mail: jarekz@fuw.edu.pl
P. J. Durka
e-mail: durka@fuw.edu.pl
S. Haufe
Berlin Institute of Technology, Berlin, Germany
e-mail: stefan.haufe@tu-berlin.de
G. Nolte
Fraunhofer FIRST, Berlin, Germany
e-mail: guido.nolte@first.fraunhofer.de
this project is evaluation of possible improvements of
localization of epileptic foci from the surface EEG
recordings. These datasets offer a unique possibility
for evaluating different EEG inverse solutions. We
present preliminary results from a subset of these cases,
including comparison of different schemes for the EEG
inverse solution and preprocessing. We report also a
finding which relates to the selective parametrization
of single waveforms by multivariate matching pursuit,
which is used in the preprocessing for the inverse solu-
tions. It seems to offer a possibility of tracing the spatial
evolution of seizures in time.
Keywords Epilepsy · SVD · MMP · MUSIC ·
Beamformer · EEG · MRI
Introduction
How EEG Inverse Solutions can Help Epileptology
Electroencephalography is the most important exam-
ination in clinical decisions related to initiation of
therapy, its continuation and all other clinical activ-
ity in epileptology (Loddenkemper and Kotagal 2005;
Rosenow and Luders 2001). Unique and specific elec-
tric pathology underlying the paroxysmal discharges in
different types of epilepsies gives a powerful tool to
clinical evaluation and therapy planning (Karbowski
1990; Pillai and Sperling 2006). Especially, long-term
monitoring, as the Videometric method introduced by
Penin (1968), allows registering such activity on the
long time-span periods, potentially including epileptic
fits.
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286 Neuroinform (2010) 8:285–299
In certain drug-resistant types of epilepsy, removal
of the epileptogenic zone is the only curative therapy.
Methods of automatic scoring of spikes (e.g. Koffler
and Gotman 1985), as well as mapping of amplitude
or its derivates (e.g. Wong 1998), are helpful, but
not sufficient in localization of the epileptogenic zone
and its activity. It is still very difficult to delineate
the epileptogenic zone without invasive (intracranial)
EEG registration. This is complicated and invasive so
limits application of these methods in clinical practice
(Rosenow and Luders 2001). Modern diagnoses of any
kind tend to be less invasive. One of the major goals
of this study is to reduce the need of invasive meth-
ods in delineation of the epileptogenic zone by using
epileptogenic activity localization methods based on
the EEG measurement—or, at least, to clearly identify
the practical and theoretical limits of this approach.
How this Database can Help the Development
of EEG Inverse Solutions
Localization of brain electrical activity estimated from
the scalp (EEG) is a challenging problem and draws
the ongoing attention of many researchers in the field.
There are many parameters influencing the results, like
tissue geometry and electrode locations used for the
forward model, noise characteristics etc. An even more
severe confounder is the fact that the inverse solution
is not unique without making additional assumptions
about the sources. Lacking the ground truth in almost
all real cases the proper choice of assumptions has been
under debate for decades.
In general, the various methods can be split into
three groups: a) explicit models with fewer parameters
than unknowns, b) regularized dipole field reconstruc-
tions, and c) beamformers. The most established model
is the N dipole model, assuming that the electric po-
tential is generated by a few pointlike activities (Scherg
and Ebersole 1993). Generalizations include multipole
expansion, useful for focal but not necessarily pointlike
sources (Irimia et al. 2009) or well defined patches of
activation (Cao et al. 2006). For dipole field recon-
structions the brain activation is estimated over the
entire brain or cortical surface using various additional
assumptions. The assumptions typically constraint the
norm of the dipole fields (Molins et al. 2008) or the
norm of the derivative of the field, e.g. LORETA
(Pascual-Marqui et al. 2002) to regularize the solution.
Recently, nonquadratic regularizations have become
popular, since they combine the focality assumption
with estimates of the entire brain activity (Ou et al.
2009; Haufe et al. 2008). Beamformers can be con-
sidered as an intermediate between N dipole models
and dipole field reconstructions. The source activity is
estimated at each brain location as a linear combination
of the sensor data, such that actvity from a dipole at that
location is recovered exactly and all other contributions
are maximally suppressed (Veen et al. 1997; Sekihara
et al. 2005).
The above examples of inverse methods cover only a
tiny fraction of established variants. The saying goes,
that there are as many methods as scientific groups
working in this area. The main reason for this diversity
is a) that for each solution it is possible to construct,
i.e. simulate, a problem where this solution is optimal,
and b) that the factual localization, that could serve as
a reference for evaluation of accuracy of a solution, for
real data is rarely known. Thus we believe that a set of
relevant real data examples with ground truth known to
reasonable accuracy may substantially shed light on the
open problem which of the numerous methods should
be recommended.
The Database
As the first step towards a solution to the problems
discussed in the previous section, we created a database
which can be used for both:
– Evaluation of the currently available EEG Inverse
Solutions performance as an auxiliary tool for at
least rough localization of the epileptic foci
– Comparing stability and accuracy of the plethora of
different EEG Inverse Solutions.
The database contains records of 23 patients with
severe epilepsy, mostly caused by different organic le-
sions. The patients are aged 1–18 (mean 12±5 years).
Summary data for the patients is presented in Table 1.
This data was collected during the routine of the Warsaw
Memorial Child Hospital. All selected patients were
diagnosed with and operated on for drug-resistant
epilepsy. The subjects presented in the database were
selected from a total of 140 cases screened. The crite-
ria for the inclusion was that the data are sufficiently
unambiguous to proceed with the surgical removal
of the epileptogenic zone. In each case, surgery was
performed with ECoG which enabled precise localiza-
tion of the epileptogenic region. If there was resected
tissue, its pathology was extensively examined. As a
result, the database contains the most unambiguous
cases available, which corresponds well both of the aims
formulated above.
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Neuroinform (2010) 8:285–299 287
Table 1 Summary information about cases available in the database
Subject Description Duration of Number of
the recordings inter-ictal events
CHIMIC Male, 9 y.o. , with severe form of epilepsy of temporal lobe origin 40′12′′ 4
FRAANN Female, 17 y.o., with severe form of temporal lobe epilepsy 28′24′′ 3
HRADAW Male, 12 y.o., with drug-resistant generalized seizures 23′0′′ 4
JANPAT Female, 10 y.o., with severe form of temporal lobe epilepsy 20′03′′ 4
JANPRZ Male, 5 y.o., with severe form of epilepsy since 1 y.o. 32′36′′ 4
JATKAM Female, 14 y.o., with severe temporal lobe epilepsy caused by very unique 20′00′′ 7
multiple pathology
GILPAU Female, 14 y.o., with severe frontal lobe seizures 20′03′′ 5
GREOSK Male, 13 y.o., with intermittent epilepsy of temporal lobe 20′09′′ 3
JERKAT Female, 15 y.o., with severe epilepsy and moderate cognitive impairment 20′03′′ 5
KOSPAW Male, 16 y.o. , with occasional epileptic fits and mild cognitive impairment 20′03′′ 3
KOTLUK Male, 17 y.o., with refractory epilepsy of temporal origin 20′58′′ 2
KROMIC Male, 16 y.o., with severe epilepsy and mild cognitive impairment 20′06′′ 5
LADJAN Male, 10 y.o., with drug resistant epilepsy of frontal origin 22′24′′ 5
MARPAW Male, 16 y.o., with drug resistant epilepsy of temporal origin 26′12′′ 6
MATPAW Male, 15 y.o., with generalized seizures and parietal lobe tumor 20′24′′ 3
MEZLUK Male, 4 y.o., with severe epilepsy of left temporal lobe 0′59′′ 2
NASNAD Female, 1 y.o., with severe infantile spasm and focal seizures due to large FCD 22′25′′ 4
NIZMAR Male, 12 y.o., with motor seizures and frontal lobe tumor 21′27′′ 3
NOWJON Male, 15 y.o., with typical picture of temporal lobe epilepsy 20′03′′ 3
PAKDAM Male, 13 y.o., with gross, disseminated FCD of right hemisphere and 1h12′35′′ 4
contralateral hemiparesis and mild cognitive impairment
SIEGRZ Male, 18 y.o., with typical MTLE 20′03′′ 4
SNOKAC Male, 3 y.o., with drug resistant epilepsy and right lobe FCD 20′03′′ 6
STAMIC Male, 15 y.o., with epilepsy due to right frontal lobe tumor 20′28′′ 5
A screenshot of a sample record from the database,
displayed in the Web interface, is given in Fig. 1. Each
database record includes:
EEG: Clinically relevant EEG epochs collected during
the pre-surgical period. The data consists in each
case of 20–70 minutes of continuous inter-ictal
EEG recording, containing inter-ictal discharges
which are representative of symptomatology and
are in spatial relation with the structural pathol-
ogy. During the recording the subject was resting.
Data was recorded using the 10–20 system (with
19 electrodes), stored in European Data Format
(EDF) (Kemp et al. 1992). The Ag/AgCl elec-
trodes 10 mm diameter were used with impedance
<5k�. Two EEG systems were used for record-
ings: DigiTrack EEG System by Elmiko (sam-
pling frequency 250 Hz), and Medelec-Profile
system by Medelec, Oxford Instruments (sampling
frequency 256 Hz). For both apparatuses, filters
were set to pass frequency band 0.5–70 Hz; ad-
ditionally, 50 Hz notch filter was applied. The
hardware referencing was to electrode Fpz. The
data contains no detailed fiducial documentation.
Marked epileptogenic waveforms: Scanned printouts of
EEG epochs containing the epileptogenic struc-
tures explicitly marked by the epileptologist.
MRI: MRI recordings containing a T1, T2 or fluid
attenuated inversion recovery (FLAIR) weighted
brain scans with morphologic substrate of the
epilepsy (mostly cortical dysplasias, dysplastic tu-
mors etc.). In some cases there is also a scan with
the gadolinium (GAD) contrast. The images were
collected by Siemens Sonata 1.5T scanner. The
data is stored in the Digital Imaging and Com-
munications in Medicine (DICOM) format. The
name of the scan’s folder indicates its weighting.
The scans have the following resolutions:
T1: 512 × 512 pixels, pixel spacing 0.4687 mm,
slice thickness 1.2 mm,
T2: 256 × 256 pixels, pixel spacing 0.9375 mm,
slice thickness 2.5 mm.
Reference region: Recognized structural placement of
the epileptogenic zone marked on pre-surgical
MRI scans in transverse, sagittal and coronal pro-
jections. The placement was verified by ECoG
and post-operational results.
Case description: Textual description of each case, con-
taining demographic data of each patient, short
anamnesis of epilepsy, essential symptomatology
of epileptic fits, additional necessary diagnostic
tests results, and a concordance report for the
epilepsy surgery decision.
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288 Neuroinform (2010) 8:285–299
Fig. 1 Illustrative screenshot of one record from the data-
base. Links Download binary EEG file and Download
binary MRI file lead to the corresponding files. The pictures
provide links to PDF files with scans of: printouts of the EEG
epochs with epileptic structures marked by epileptologist (left),
and scans of 3 MRI sections per each patient with the location of
foci, verified by ECoG and post-operational results, marked by
pen on the printout (right)
Personal information was removed from this data,
cases are identified by six-letter codes.
Example Analysis
This section presents preliminary results from analysis
of only a few cases from the database—more extensive
examination of these findings is an ongoing work.
Methods
The accuracy of any source localization methodology
relies heavily on the adequacy of every step in the
data processing chain. These steps can be divided into
the construction of the physical head model, the sig-
nal preprocessing, and the actual source localization
technique. In the following paragraphs we will describe
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Neuroinform (2010) 8:285–299 289
what is needed to obtain a realistic EEG forward
model and outline a number of preprocessing/feature
extraction techniques and inverse solutions that are
appropriate for localization of spike-like activity.
EEG Forward Modeling
For the underlying physical model, also known as the
forward model, it is common to assume a standard
head geometry. However, for children no such template
exists, and the deviation from a normal adult brain
can be particularly large when dealing with long-term
epilepsy patients. The repository provided here facili-
tates a more realistic modeling of the relation between
brain sources and the EEG in these cases. This is
because the patient’s individual head geometry can be
taken into account. The steps necessary for building
such an individual model involve:
1. Segmentation of the MR image into homogeneous
volumes representing skin, skull and brain tissues
2. Computation of the linear forward mapping in the
obtained three-shell head model.
These steps were carried out here using (1) Curry 5.0
(Neuroscan) with segmentation resolution of 1mm and
(2) a Matlab (The Mathworks) implementation of the
semi-analytic leadfield expansion procedure described
in (Nolte and Dassios 2005) using a model grid size of
5 mm and the following conductivies of the layers: skin
0.33 S/m, skull 0.004125 S/m and, brain 0.33 S/m. This
three layer model is a standard one, and contains the
biggest effect, namely the very different conductivities
of the skull on the one hand and the skin and brain
on the other. In principle, one could also include cere-
brospinal fluid (CSF). The problem is that the brain is
highly folded which would render the calculation inac-
curate for the available resolution of the MRI segmen-
tation. The EEG signals were measured in the standard
10-20 system (Niedermeyer and Lopes da Silva 2004).
In the computation of the forward problem the stan-
dard positions of the electrodes are first transformed to
match the size and orientation of the individual head
model and later their positions are fine-tuned such that
the electrodes are put exactly on the scalp.
Signal Preprocessing
The quality and relevance of the estimate of activity lo-
calization, obtained from the inverse solutions, depends
on the preprocessing of the data that is used as the input
to the inverse solution method (Durka et al. 2005). We
present results obtained for three types of input:
1. Raw signal form selected epochs,
2. Topography of the first component of singular
value decomposition (SVD) of the signal of the
selected epochs,
3. Topography of the structures from multivariate
matching pursuit (MMP) decomposition selected by
epileptologist as relevant to the epileptic activity.
Singular Value Decomposition inter-ictal spikes strongly
contribute to the EEG, that is, they account for a huge
amount of variance in the epochs containing them.
Thus epileptic activity can be most easily extracted
by searching for EEG components with high-variance.
This is achieved by Singular Value Decomposition
(SVD), which is an orthogonal transformation of a
multivariate signal into uncorrelated components. The
EEG signal with Nc channels and T samples can be
represented as T × Nc matrix x. The SVD is obtained as:
x = USV� (1)
where:
U is the T × Nc matrix containing Nc normalized
principal component waveforms (U�U = I),
S is Nc × Nc, diagonal matrix of components ampli-
tudes (singular values),
V is a Nc × Nc matrix mapping components to orig-
inal data such that Vi, j is the contribution of jth
component to ith channel; VTV = I.
Hence, the columns of V with highest corresponding
singular values span a subspace of maximal signal en-
ergy. Sources contributing to this subspace can be lo-
calized using, e.g., the MUSIC algorithm (see MUSIC
in Section “Inverse Solutions”). In the SVD implemen-
tation used in this study the components are sorted
in the descending order of their singular values. The
projection Pi, j of the jth component to ith channel can
be computed as
Pi, j = U·, j S j, j
(
Vi, j
)
� (2)
Matching Pursuit Algorithm Matching pursuit (MP),
proposed in Mallat and Zhang (1993), is a suboptimal
solution to the NP-hard problem of finding an optimal
representation of a signal in a redundant dictionary
of functions D. To analyze multichannel EEG data,
multivariate matching pursuit (MMP) was used—a ver-
sion of the matching pursuit algorithm operating on
multichannel signals. MMP can be realized in a variety
of ways, depending on the constraints used to select
related time-frequency waveforms (atoms) in different
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