Public Defense

Automated Subject-Specific Peak Identification
and Ballistocardiographic Artifact Correction
in EEG-fMRI

Sara Assecondi
Dep. Electronics and Information Systems
MEDISIP, Ghent University

Promotors: Prof. Dr. Ir. S. Staelens, Prof. Dr. P. Boon

The secret dream of a neuroscientist
is to map the human brain: WHERE and WHEN

personality
sensory
emotions

problem solving
reasoning
hearing
language

speech
vision

bla…. bla….
bla….

Introduction: general background 2

Brain mapping has useful applications

Pathologies
• Dyslexia • Epilepsy
(5% school aged children) (0.5-1 % of the population)
EL
DYS XIA

Cognition
• mind, reasoning, …
• perception, …
• intelligence, learning…


Nowadays we have reliable means
to localize brain functions

EEG exam fMRI exam

Take an image of the brain-
fMRI


What an ElectroEncephaloGram (EEG) is

100 billion

(100000000000)

Neurons communicate by means of electrical impulses: the brain is
characterized by an electrical activity that can be measured in μV
Signal amplitude
(μV)

Time
(ms)


EEG exam

EEG electrode
Signal amplitude
(μV)

Time (ms)

EEG exam:
Event Related Potentials (ERP)
Signal amplitude
μV ERP
(μV)

STIMULUS
- electric,
- auditory
- visual Time ms
(ms)
- cognitive...

Information in EEG and ERP
The latency of peaks , i.e. the time interval between the
stimulus and the occurrence of a peak, gives temporal
information about the sequence of events in the brain
μV
perception decoding comprehension
μV

.
.
.
ms stimulus ms
stimulus latency

Each peak is related to a specific stage Scalp
of the information process, through its map
latency

Basics of Magnetic Resonance Imaging (MRI)

Up to 70 % of body weight is made up by water

70%

O N
H
H H
1H nucleus
≈ S
Water has a spin
molecule and a magnetic moment

By making use of the magnetic
properties in brain tissues, an image
of the brain can be reconstructed

MRI exam

MR-scanner

strong
magnet Sagittal view

Radio-frequency
waves and
time-varying
magnetic fields
Axial view


MRI exam: MR-scanner
functional MRI

Activity:
on on on

off off off off

time
Activity:
• Endogenous (epileptic spikes)
• Exogenous (external stimuli)

Do we have everything we need?

personality
We have the means:
sensory
emotions • EEG μV
problem solving
reasoning hearing
language ms
speech vision
• fMRI

Two new important concepts:
• Temporal resolution
• Spatial resolution


Temporal resolution

The smaller the interval the higher the temporal resolution

μV μV μV

ms ms ms

EEG-ERP (ms) fMRI (s)

μV

ms


Spatial resolution

The finer the grid the higher the spatial resolution

EEG-ERP (cm) fMRI (mm)

Scalp map

EEG-fMRI achieves high temporal and spatial resolution

Invasive
0
Brain
PET
-1 EEG+ERP

Map -2 fMRI
NIRS
Log size (m)

-3
Column
Layer -4
Adapted from Cohen and Bookheimer, 1994
Neuron -5 Non-invasive
-3 -2 -1 0 1 2 3 4 5
Log time (s)

Take the advantage of both EEG and fMRI
to have, at the same time,
high temporal and high spatial resolution

What do we need for EEG-fMRI?

• MR-compatible EEG equipment

Amplifier

syncbox Power pack

Galvanic isolation

• MR scanner


EEG-fMRI exam

Scanner room Control room
EEG electrode

EEG wires
PC

amplifier

Activity:
on on on

off off off off
Adapted from Bénar, PhD thesis

How we approach the problem of
cerebral localization of brain functions
Integration of
EEG and fMRI

Time domain Time and spatial domain

Improving the temporal Data quality of simultaneous
localization of brain functions EEG-fMRI recordings
Quantitative and automated
identification of ERP peaks Data quality of simultaneous
ERP-fMRI recordings

Data interpretation
Effect of different recording
environments on the process
under investigation

Overview 19

Integration of
EEG and fMRI


ERP-fMRI recordings

Data interpretation
under investigation

Overview 20

The quantification of amplitudes and latencies
in ERPs may be challenging

PRE-LEXICAL LEXICAL POST-LEXICAL

N3 N4
N0 N1 N2

P0 P1 P4 P600
P2b
P2a

0ms 160ms 420ms 800ms

• Challenges • Information to retrieve
– Number of peaks – Latency of each peak
– Labelling – Amplitude of each peak
– Inter-subject variability – Label of each peak

Time domain: Automatic identification of ERP peaks 21

Features of peak quantification approaches

 Reliability
 Reproducibility, over subjects and over time
 Objectivity

Gold standard: expert clinician Automated methods: Peak-picking,
• Time consuming Dynamic Time Warping
• subjective • Fast
• Objective

N1 N1
μV

μV


P2

P2
stimulus ms stimulus ms


Method 1: Peak-picking is a search for extrema
in predefined time intervals

N3 N4
N0 N1 N2

P0 P1 P4 P600
P2b
P2a

Predifined
time interval

Example: Looking for the N3 peak Drawbacks
• Define the time interval
• Highly dependent on the
• N3 is a negative peak: defined intervals
search for a minimum
• Does not take into account
• Assign the N3 label the inter-subject variability
to the minimum found


Method 2: Dynamic time warping (DTW) is a
nonlinear mapping of two signals
The ERP is compared with a reference signal containing the
peaks of interest
Linear alignment Dynamic Time Warping

reference reference

subject subject

Time (ms) Time (ms)

   
Drawback A peak is always found: it may not be a peak


We proposed a method (ppDTW) that integrates
peack-picking and Dynamic Time Warping

Compared by means of DTW

REFERENCE SUBJECT

Sequence of
candidate peaks Peak-picking in a
time interval
centered at the
candidate peak
Measured latencies
and amplitudes


The reference signal must contain
all the peaks of interest
We computed the reference by interpolating mean
amplitudes and latencies derived from a normal population

Peak variability derived
Reference
from a normal population


Case study
(Assecondi et al., Clinical Neurophysiology 2009)

• Normal children and children diagnosed with
developmental dyslexia
• Reading related ERP

• Latencies automatically determined:
– Peak picking
– ppDTW

• Validation
– Comparison with the visual scoring of an expert clinician


Performance of the methods
on normal and dyslexic subjects
normal dyslexic
N0 N2 N3 N4
N1N2 N3 N4
P1 P2b P4
P2b P4 P600

ppdtw
ppdtw

visual scoring
visual scoring

peak picking
peak picking


We evaluate the methods by comparing
the automated scoring with the visual scoring
C = correct identifications
S = substitutions C S D I
D = deletions expert    
I = insertions method    

N1

precision recall F-score
Latency N1
1
C C 1 1
C S I C S D 2P 2R

Peak-picking 93% 80% 85%
PP-DTW 93% 86% 89%


Conclusion

We developed an automated peak detection method that
takes into account the inter-subject variability and a-priori
knowledge about the ERP (in the reference)

A-priori knowledge may be derived from literature,
hypothesis about the ERP experiment, expertise of the
clinician

The method is valuable with different ERPs, when huge
databases have to be measured or when the same subject
must be examined during a therapy or rehabilitation


Integration of
EEG and fMRI


ERP-fMRI recordings

Data interpretation
under investigation

Overview 31

EEG-fMRI achieves high temporal and spatial resolution

Invasive
0
Brain
PET
-1 EEG+ERP

Map -2 fMRI
NIRS
Log size (m)

-3
Column
Layer -4
Adapted from Cohen and Bookheimer, 1994
Neuron -5 Non-invasive
-3 -2 -1 0 1 2 3 4 5
Log time (s)

EEG - fMRI implies the interaction of
different physical and physiological systems ARTIFACTS
(human being, EEG system, MR scanner)

Time and spatial domain: Data quality of simultaneous EEG-fMRI 32

An artifact is a contamination
of the data of interest
Suppose you want to listen to the radio while a bell is ringing

din
don

Source of noise o Data of interest
l i
d
e ? i
a
i s
s
n i
n
d
s
i i


Two main artifacts affect EEG-fMRI recordings

• Image acquisition artifact
caused by the RF-waves
and time-varying
gradients
~0.5 s
Very deterministic
BCG-peak
• Ballistocardiographic
artifact is a blood related
effect due to the high
static magnetic field R-peak

Only QUASI-deterministic
~1 s

The BallistoCardioGraphic artifact (BCGa)
R R R R R R R R
100 µV

10 seconds

• It depends on the proximity of the EEG electrodes to blood vessels
• It is synchronous to the heart beat and spread throughout the
heart beat and all over the scalp

Distribution of scalp arteries

Adapted from Gray, 1918

The BCG artifact can be removed
if an additive model is assumed

Noise and
Recorded other non-MR
signal = EEG + BCGa + related
artifacts

Estimate of the BCGa
that depends on the
algorithm used

subtraction BCGa
model


Intra-subject variability of the BCGa

Heart beat

-0.2 0 0.2 0.4 0.6

seconds


Inter-subject variability of the BCGa
S1 S2 S3 S4 S5 S6
channels

seconds

The BCG artifact removal algorithms must take the
inter- and intra-subject variability into account

Group 1: methods based on Average Artifact Subtraction
 Moving Average
 Weighted Average
 Selective Averaging based on clustering

Group 2: methods based on Blind Source Separation
 Optimal Basis Set (OBS)
 Independent Component Analysis (ICA)
 Canonical Correlation Analysis (CCA)


Blind Source Separation identifies the sources
generating the recorded signal

s1
Blind Cerebral
Source s2 sources
Separation s3
(BSS) Non-cerebral
x2 x3 X
s4 sources
x1
x4
constraints
s1
s2 s3 s4
BSS as an artifact removal technique is twofold:
• Step 1: Identification of sources
• Step 2: Selection of sources
(brain signal or artifact)

We proposed a BSS method that uses Canonical
Correlation Analysis to identify the sources

Step 1: Identification of sources Step 2: Selection of artifact sources

EEG epoch 1 EEG epoch 2 Criteria have to be met simultaneously:

- correlation of sources common to two
consecutive EEG epochs

- Sources must not be sinusoidal EEG
rhythms

Sources or
Canonical BSS-CCA
variates

time
.
. .
.
. .

Case study
(Assecondi et al., Physics in Medicine and Biology 2009)

• Patients affected by epilepsy
• Recording of 20 minutes of simultaneous EEG-fMRI
• Artifact removal
– AAS
– CCA
• Validation
– Frequency content at the harmonics of the ECG
– Global Field Power
– Signal Envelope


Comparison of cleaned and raw EEG

BSS-CCA
AAS
RAW


Conclusion

The BCG artifact has an intrinsic inter- and intra-subject
variability that make the performance of artifact removal
algorithms subject-dependent

We proposed a method to remove the BCG artifact that
deals with the intra- and inter-subject variability

The proposed method is an added value especially in those
cases where the AAS fails, because of the excessive intra-
subject variability of the artifact


Integration of
EEG and fMRI


ERP-fMRI recordings

Data interpretation
under investigation

Overview 47

The methodologies of analysis of ERPs and EEG
are intrinsically different

s s s s s

• Smaller amplitude
• Involve additional averaging
• Different signal-to-noise ratio

The method to remove the BCG artifact needs
modifications to select the non-cerebral sources

Time and spatial domain: Data quality of simultaneous ERP-fMRI 48

The proposed method is adapted to ERPs

Step 1: Identification of sources Step 2: Selection of artifact sources

Average BCG EEG epoch Criteria have to be met simultaneously:

- correlation of sources extracted from
the average BCG and the EEG epoch

- Sources that maximally contribute at the
same time to the artifact and to the EEG

Sources or
Canonical BSS-CCA Sources important for the EEG but not
variates
for the artifact are not removed

.
. .
.
. .

Case study
(Assecondi et al., submitted to Clinical Neurophysiology)

• Healthy volunteers
• Three tasks:
– Visual: Visual detection task (5 subjects)
– Cognitive: Go-nogo task (5 subjects)
– Motor: Motor task (7 subjects)
• In two situations:
– Outside the MR-scanner room (0T)
– Inside the MR-scanner, without scanning (3T)
• BCG artifact removal
– AAS (Average Artifact Subtraction)
– CCA (Canonical Correlation Analysis)


Three different types of stimuli were used

Detection GoNogo Motor
Left visual field No Go Left keypress

Central visual field Go Withhold

Right visual field Right keypress


In most cases both methods are able to recover
ERP time series
Detection task

0T

3T


BSS-CCA seems more effective to recover small
amplitude low frequency components

Motor task

AAS BSS-CCA

0T

3T


Integration of
EEG and fMRI


ERP-fMRI recordings

Data interpretation
under investigation

Overview 54

Do the very different environments in which the data are
recorded have an effect on the physiological process under
investigation?

ERP lab MR scanner
position sitting laying
Light dimmed dimmed
Noise no yes
Screen orientation In front mirrors
Magnetic fields no yes

Time and spatial domain: Effect of different environments 55

To disentangle effects of factors affecting EEG-fMRI
data, different situations must be compared

Effect of the static
field +position

Static
0T field

No MR-related artifacts BCG artifact


Case study
(Assecondi et al., submitted to Clinical Neurophysiology)

• Healthy volunteers
• Three tasks:
– Visual: Visual detection task (5 subjects)
– Cognitive: Go-nogo task (5 subjects)
– Motor: Motor task (7 subjects)
• In two situations:
– Outside the MR-scanner room (0T)
– Inside the MR-scanner, without scanning (3T)
• BCG artifact removal
– AAS (Average Artifact Subtraction)
– CCA (Canonical Correlation Analysis)


We found differences between
0T, 3T and cleaned data
• Amplitude decrease in 3T
• Latency increase in 3T
• Strong contamination of the data due to the artifact
• Difference in the scalp distribution
0T 3T After BCG removal

goP3
GoNogo task

Ipsi N1
Detection task


Reaction time: rt@3T-rt@0T
***
***
***
50 ***
***
*** *** ** * * **
*
0 - -
ms - subject
-
**
***
-50
- p>0.05
* p<0.05
*** *** ** p<0.01
*** p<0.001
-100

Leuven

-150 ***
Detection Go-Nogo Motor

Conclusion

The quality of average ERP is less sensitive to the method
used to remove the BCG. However, BSS-CCA seems to be
more effective with low frequency low amplitude
components and when less trials are available

Differences are found between different environment.
More subjects are needed to obtain robust results

Different situations must also be taken into account
(dummy scanner, actual fMRI acquisition)


Integration of
EEG and fMRI


ERP-fMRI recordings

Data interpretation
under investigation

Overview 61

Overall considerations and future prospects

• The localization of brain functions can be improved by taking into
account a-priori knowledge of the process and the inter- and intra-
subject variability

• New multimodal approaches, e.g. EEG-fMRI, offer a deeper insight
into how the brain works (great help by the availability of
commercial MR-compatible EEG systems)

Fundamental research Development of methods

• to understand the relation between • to improve the integration of
EEG and fMRI the two modalities and to
• to comprehend the effect of the actually benefit by the different
very different environments on EEG information extracted by
data different modalities

Overall considerations and future prospects 62

Automated Subject-Specific Peak Identification
and Ballistocardiographic Artifact Correction
in EEG-fMRI

Sara Assecondi

Dep. Electronics and Information Systems
MEDISIP, Ghent University

Promotors: Prof. Dr. Ir. S. Staelens, Prof. Dr. P. Boon

Thank you!

Related publications

• S. Assecondi, et al. Effect of the static magnetic field of the MR-
scanner on ERPs: evaluation of visual, cognitive and motor
potentials Submitted.

• S. Assecondi, et al. Automated identification of ERP peaks through
Dynamic Time Warping: an application to developmental dyslexia
Clinical Neurophysiology, DOI: 10.1016/j.clinph.2009.06.023.

• S. Assecondi, et al. Removal of the ballistocardiographic artifact
from EEG-fMRI data: a canonical correlation approach Physics in
Medicine and Biology. Vol. 54 (2). 2009. pp. 1673-1689

64

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