CARDIAC CYCLE ARTEFACT REMOVAL IN

MAGNETOENCEPHALOGRAPHIC DATA OF PATIENTS WITH

DEEP BRAIN ELECTRODES

Implementation of Simultaneous Magnetoencephalographic and Local Field

Potential Recordings

Antje Bock

, Andrea A. K¨uhn

, Lutz Trahms

and Tilmann H. Sander

Department of Neurology, Charit´e Berlin, Campus Virchow, Augustenburger Platz 1, 13353 Berlin, Germany

Physikalisch-Technische Bundesanstalt, Abbestr. 2-12, 10587 Berlin, Germany

Keywords:

Magnetoencephalography, Deep brain stimulation, Local ﬁeld potentials, Cardiac cycle artefact, Principal

component analysis, Signal space projection, Coherence.

Abstract:

Simultaneous magnetoencephalography (MEG) and local ﬁeld potential (LFP) recordings in patients under-

going deep brain stimulation (DBS) for severe movement disorders is a promising technique both for clinical

applications and basic research. Recordings can be accomplished during the time interval between electrode

insertion and implantation of the stimulator while electrodes are externalised. At present, strong cardiac cycle

artefacts (CCA) are observed in the MEG signals around the area, where the disposable stainless steel electrode

wires leave the skull. The CCA refers to the remanent magnetic ﬁeld of those wires underneath the sensors,

which are moved by local pulsations of the blood vessels. Here, we demonstrate a new approach to partially

remove the CCA by applying principal component analysis (PCA) to an averaged CCA and subsequent signal

space projection (SSP) method. Further steps of analysis such as coherence calculations are less distorted after

SSP.

1 INTRODUCTION

Deep brain stimulation (DBS) offers the unique op-

portunity to directly record local ﬁeld potentials

(LFP) from the human basal ganglia (BG) (Silberstein

et al., 2003; K¨uhn et al., 2004). By implementing

simultaneous magnetoencephalography (MEG) and

LFP recordings in patients undergoing DBS, cortical

brain activity in terms of magnetic ﬁelds in addition to

electrical neuronal activity directly from the BG can

be measured. Results will give fundamentally new

insights about information encoding and processing

in the cortico-BG network. However, MEG record-

ings are very noisy due to the cardiac cycle artefact

(CCA). The CCA refers to local pulsations of the

blood vessels moving the weakly magnetised (exter-

nalisation) stainless steel electrode wires relative to

the MEG sensors (Litvak et al., 2010). The principal

component analysis (PCA) of the averaged CCA can

be calculated in order to identify the pattern and di-

mensionality of the artefact, which, in turn, can then

be eliminated from the MEG data by applying signal

space projection (SSP). Subsequent coherence calcu-

lations lead to improved topographic distributions.

2 METHODS

2.1 Patients and Recordings

Nine patients with Parkinson’s disease (PD) (2 fe-

males, mean age 55.89 ± 11.53 years) and eight pa-

tients suffering from dystonia (5 females, mean age

51.50 ± 8.38 years) were included in this study (17

in total). DBS electrodes were implanted bilater-

ally in the subthalamic nucleus (STN) in the PD pa-

tients or in the internal globus pallidus (GPi) in the

dystonic patients, respectively. During the time in-

terval (2 to 5 days) before connection of the elec-

trodes to a subcutaneous pulse generator, simultane-

ous LFP and MEG recordings can be accomplished.

To this end, patient’s heads were positioned in a 125-

channel whole head MEG system (KIT, Eagle Tech-

nology, Kanazawa, Japan). Rest recordings of 300 s

325

Bock A., Kühn A., Trahms L. and Sander T..

CARDIAC CYCLE ARTEFACT REMOVAL IN MAGNETOENCEPHALOGRAPHIC DATA OF PATIENTS WITH DEEP BRAIN ELECTRODES -

Implementation of Simultaneous Magnetoencephalographic and Local Field Potential Recordings.

DOI: 10.5220/0003706703250328

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2012), pages 325-328

ISBN: 978-989-8425-89-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Averaged CCA of a dystonic patient calculated by R peak alignment (ﬁeld strength indicated in Tesla).

duration were made while patients were asked not to

move and keep their eyes open. Simultaneously with

changes of the magnetic ﬂux, LFP signals were ac-

quired with a 32 channel low noise EEG ampliﬁer.

The DBS macroelectrodes in each hemisphere com-

prise 4 contacts, which are numbered 0, 1, 2, and 3

with 0 being the most caudal one. LPF signals were

recordedusing contact 0 of the left electrode as a com-

mon reference and then re-referenced to bipolar sig-

nals by subtracting adjacent contact pairs from each

other. Additionally, electrocardiograms (ECGs) were

recorded monopolarly with reference to the left deep

brain electrode contact 0. A ground electrode was

placed on the patient’s forehead. Data were sampled

at 2000 Hz and off-lineﬁltered between 5 and 120 Hz.

PD patients were recorded twice in one session, once

after overnight withdrawal from dopaminergic medi-

cation (OFF) and 30 to 60 minutes after intake of 200

mg of L-DOPA (ON).

2.2 Cardiac Cycle Artefact Removal

In MEG data, the heartbeat generates the cardiac

artefact (CA). The CA reﬂects the electrical current

within the heart muscle (Jousm¨aki and Hari, 1996),

which is a signiﬁcant contribution to the MEG even

though MEG is recorded at a distance of about 200

mm from the heart. In patients with externalised elec-

trode wires, a second artefact can be detected, which

is the cardiac cycle artefact (CCA) (Litvak et al.,

2010). The CCA refers to the time variable magnetic

ﬁeld due to local pulsations of the blood vessels mov-

ing the weakly magnetised externalisation wires rela-

tive to the MEG sensors. Both artefacts are best iden-

tiﬁed by ﬁnding the R wave, which is the deﬂection

of highest amplitude within the electrocardiographic

QRS complex. The QRS complex reﬂects the depo-

larisation of the heart’s right and left ventricles and

lasts about 100 ms. The CCA is of much higher am-

plitude than the CA as it has a technical origin, for

which reason the CA can be ignored within the scope

of this work. The R peak of the ECG signal has then

been used as a trigger for averaging the CCA in all

channels with trials of 600 ms length (300 ms before

and 300 ms after trigger onset).

As it can be assumed that the magnetic and elec-

tric ﬁelds propagate instantaneously, the averaged

CCA (aCCA) can be calculated by time-locking the

magnetic channels to the ECG. Figure 1 shows the

resulting topographic maps of all magnetic channels

(the number of the remaining channels of each patient

varied between 90 and 110, because saturated chan-

nels close to the artefact source have been sorted out)

for time epochs of 20 ms. The patient’s head in the

map is seen from above with sketched ears and nose

to denote its orientation. The little black dots indicate

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

326

the locations of the sensors. The aCCA is the pat-

tern that is located within the left hemisphere around

the fronto-parietal area, where the electrode wires are

placed in loops underneath the skin. The intensity of

the magnetic ﬁeld changes are color-coded and given

in Tesla. Positive values are shown in yellow, orange

and red color shades and represent sources, while neg-

ative blue-colored values show sinks. Arterial blood

ﬂow velocity is about 1 m/s in thick blood vessels,

therefore the temporal allocation of the aCCA pat-

tern differs from the one of the QRS complex and the

strongest aCCA signal appears later than the R peak

at 0 ms.

A common method to detect the dimensionality

of a multivariate data set is the PCA. PCA using the

covariance method is an orthogonal linear transfor-

mation into uncorrelated variables (principal compo-

nents) by calculating the eigenvalue decomposition of

a covariance matrix. The PCA of the CCA can now be

calculated in order to identify the pattern and dimen-

sionality of the artefact. Figure 2 shows all patients’

(m = 26) resulting set of the ﬁrst 30 eigenvalues in

decreasing order. The eigenvalues represent the en-

ergy of the eigenvectors. Eigenvalues have been stan-

dardised to the maximum eigenvalue being 1, as the

magnetisation of the electrode wires differs among

patients. A sharp bend at the ﬁfth eigenvalue can be

seen in all curves, indicating that the dimensionality

of the CCA is 5 ≤ dim

CCA

≤ 10 and fairly similar for

all data sets.

0 5 10 15 20 25 30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Index

Eigenvalue

Standardized Values

Figure 2: First 30 standardised eigenvalues (m = 26) of the

PCA.

Given that the PCA eigenvalues of the aCCA have

a similar dependence, it is reasonable to use the signal

space projection (SSP) method as proposed by Uusi-

talo and Ilmoniemi (Uusitalo and Ilmoniemi, 1997) to

suppress the most powerful CCA components from

the data set. For this, the PCA decomposition is

backprojected to the channel level after removing the

ﬁrst ﬁve principal components. If A

1,...,n

(90 ≤ n ≤

110) is the matrix containing the sorted eigenvectors

eigenvector

, eigenvector

, ..., eigenvector

, the data

set X

SSP

(t) after SSP equals

X(t)

SSP



(0)(0)(0)(0)(0) A

6,...,k



−1

raw

(t)

(1)

where X

raw

(t) is the raw data set.

2.3 Coherence between MEG and LFP

Channels

In order to map frequency-speciﬁc coupling between

cortical and BG activity, coherence has been applied.

Coherence (COH) is a frequency-indexed measure

quantifying the extent of two signals holding a consis-

tent phase difference (if a certain frequency is present

in both signals) and is deﬁned as follows:

COH( f) =



( f)s

∗

( f)i

( f)M

∗

( f)ihs( f )s

∗

( f)i



(2)

where M

( f) is the Fourier transform of a time do-

main MEG channel and s( f) the Fourier transform

of an LFP signal. Coherence has been calculated be-

tween all magnetic channels and one LFP electrode

contact each before and after SSP (Figure 4 and 3).

3 RESULTS

After CCA removal by SSP, topographic distributions

have changed and speciﬁcally spread out coherence

patterns below 10 Hz are removed (Figure 3). After

SSP, coherence between the STN and ipsilateral sen-

sorimotor and premotor areas are found in the beta

band (13 to 30 Hz) in most of the PD patients during

OFF state, such as the one shown in Figure 3. This

feature is less pronounced when PD patients are ON

dopaminergic medication. Some of the dystonic pa-

tients show a similiar coherence pattern between the

GPi and cortical areas. Not consistently, but in some

of the dystonic patients, coherence between cortical

prefrontal areas and the GPi in the alpha frequency

band (7 to 12 Hz) becomes apparent after CCA re-

moval (Figure 4).

4 DISCUSSION AND

CONCLUSIONS

The SSP approach is based on a priori spatial sepa-

ration of the CCA and cortical brain signals and the

topography of the CCA is quite well deﬁned, so this

way of artefact removal works adequately for our type

of magnetoencephalographic data. Our method is an

CARDIAC CYCLE ARTEFACT REMOVAL IN MAGNETOENCEPHALOGRAPHIC DATA OF PATIENTS WITH

DEEP BRAIN ELECTRODES - Implementation of Simultaneous Magnetoencephalographic and Local Field Potential

Recordings

327

Figure 3: Coherence before (top plot) and after (bottom

plot) SSP in a PD patient between all magnetic channels

and contact 2 of the right electrode in the STN for frequency

bins of 2 Hz size from 0 to 40 Hz.

Figure 4: Coherence before (top plot) and after (bottom

plot) SSP in a dystonic patient between all magnetic chan-

nels and contact 3 of the right electrode in the GPi for fre-

quency bins of 2 Hz size from 0 to 40 Hz.

alternative to optimised beamforming (Litvak et al.,

2010) with regard to artefact suppression. In contrast

to beamforming, SSP does not require an electrical

source model. CCA removal not only suppresses co-

herences that are caused by the CCA, but also uncov-

ers coupling between the basal ganglia and cortical

brain regions, which were not visible before SSP. The

advantage of beamforming is that the location of the

coherent current in the brain is found. PCA on aCCA

followed by SSP could therefore be used as a prepro-

cessor for beamforming.

Future work has to investigate the reasons for the

CCA inﬂuencing coherences and statistical testing of

coherence differences before and after SSP (Maris

et al., 2007).

ACKNOWLEDGEMENTS

We would like to thank K. Obermayer for helpful dis-

cussions and support. A. B. is supported by a fellow-

ship from Dr. Robert Leven und Dr. Maria Leven-

Nievelstein-Stiftung.

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