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The Influence of Different EEG References on Scalp EEG Functional Network Analysis During Hand Movement Tasks

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Although scalp EEG functional networks have been applied to the study of motor tasks using electroencephalography (EEG), the selection of a suitable reference electrode has not been sufficiently researched. To investigate the effects of the original reference (REF-CZ), the common average reference (CAR), and the reference electrode standardization technique (REST) on scalp EEG functional network analysis during hand movement tasks, EEGs of 17 right-handed subjects performing self-paced hand movements were collected, and scalp functional networks [coherence (COH), phase-locking value (PLV), phase lag index (PLI)] with different references were constructed. Compared with the REF-CZ reference, the networks with CAR and REST references exhibited more significant increases in connectivity during the left-/right-hand movement preparation (MP) and movement execution (ME) stages. The node degree of the channel near the reference electrode was significantly reduced by the REF-CZ reference. CAR and REST both decreased this reference effect, REST more so than CAR. We confirmed that the choice of reference would affect the analysis of the functional network during hand movement tasks, and the REST reference can greatly reduce the effects of the online recording reference on the analysis of EEG connectivity.
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ORIGINAL RESEARCH
published: 02 September 2020
doi: 10.3389/fnhum.2020.00367
Edited by:
Zhen Yuan,
University of Macau, China
Reviewed by:
Peng Xu,
University of Electronic Science and
Technology of China, China
Shiang Hu,
Joint China-Cuba Laboratory for
Frontier Research in Translational
Neurotechnology, China
*Correspondence:
Yuxia Hu
huyuxia@zzu.edu.cn
These authors have contributed
equally to this work and share first
authorship
Specialty section:
This article was submitted to
Brain Imaging and Stimulation,
a section of the journal
Frontiers in Human Neuroscience
Received: 19 March 2020
Accepted: 11 August 2020
Published: 02 September 2020
Citation:
Zhang L, Wang P, Zhang R, Chen M,
Shi L, Gao J and Hu Y (2020) The
Influence of Different EEG References
on Scalp EEG Functional Network
Analysis During Hand
Movement Tasks.
Front. Hum. Neurosci. 14:367.
doi: 10.3389/fnhum.2020.00367
The Influence of Different EEG
References on Scalp EEG Functional
Network Analysis During Hand
Movement Tasks
Lipeng Zhang1,2† ,Peng Wang1,2† ,Rui Zhang1,2,Mingming Chen 1,2 ,Li Shi3,4,
Jinfeng Gao1,2 and Yuxia Hu1,2 *
1School of Electrical Engineering, Zhengzhou University, Zhengzhou, China, 2Henan Key Laboratory of Brain Science and
Brain-Computer Interface Technology, Zhengzhou, China, 3Department of Automation, Tsinghua University, Beijing, China,
4Beijing National Research Center for Information Science and Technology, Beijing, China
Although scalp EEG functional networks have been applied to the study of motor tasks
using electroencephalography (EEG), the selection of a suitable reference electrode has
not been sufficiently researched. To investigate the effects of the original reference (REF-
CZ), the common average reference (CAR), and the reference electrode standardization
technique (REST) on scalp EEG functional network analysis during hand movement
tasks, EEGs of 17 right-handed subjects performing self-paced hand movements
were collected, and scalp functional networks [coherence (COH), phase-locking value
(PLV), phase lag index (PLI)] with different references were constructed. Compared with
the REF-CZ reference, the networks with CAR and REST references exhibited more
significant increases in connectivity during the left-/right-hand movement preparation
(MP) and movement execution (ME) stages. The node degree of the channel near the
reference electrode was significantly reduced by the REF-CZ reference. CAR and REST
both decreased this reference effect, REST more so than CAR. We confirmed that the
choice of reference would affect the analysis of the functional network during hand
movement tasks, and the REST reference can greatly reduce the effects of the online
recording reference on the analysis of EEG connectivity.
Keywords: EEG, common average reference, reference electrode standardization technique, scalp EEG functional
network, hand movement tasks
INTRODUCTION
Maintaining body movement ability is one of the most important functions of the brain, and
many researchers have devoted themselves to studying the mechanisms behind motor processes.
Electroencephalography (EEG) is a non-invasive approach with a high temporal resolution that is
widely used to study neural activity during motor tasks.
After Kornhuber and Deecke first discovered the ‘‘Bereitschaftspotential’’ (BP; Kornhuber
and Deecke, 1965; Shibasaki and Hallett, 2006), several studies on movement-related cortical
potentials (MRCP) have been reported (Tarkka and Hallett, 1991; Rong and Deecke, 1999;
Frontiers in Human Neuroscience | www.frontiersin.org 1September 2020 | Volume 14 | Article 367
Zhang et al. Influence Electroencephalogram References Functional Network
Berchicci et al., 2016). For simple movements, the BP
initiates for 1–2 s and abruptly begins to increase at 0.5 s
before the movement onset over the frontal-central areas
of the scalp (Rong and Deecke). The power variation of
the relevant frequency bands during movement has been
extensively studied (Pfurtscheller, 1990; Pfurtscheller et al.,
2003; Bai et al., 2005). Pfurtscheller (1990) studied the
event-related desynchronization/synchronization (ERD/ERS) of
various frequency bands associated with voluntary movement.
Their research demonstrated ERD over contralateral and
ipsilateral motor cortices during the movement preparation
(MP) and movement execution (ME). Recent developments in
functional connectivity research provided a new method for
neuroimaging (Biswal et al., 1995). Complex brain networks
provide some quantitative indicators for the topology of brain
networks and have become important tools for describing
anatomical and functional brain connectivity (Bullmore and
Sporns, 2009; Rubinov and Sporns, 2010). Although some
questions remain to be solved, this new method will help us
understand the mechanisms behind motor processes (Sporns
et al., 2004). Recently, brain network methods have been
utilized to analyze changes in network connectivity between
brain regions (Popovych et al., 2016; Fleck et al., 2018). Brain
connectivity analysis has also been used as a feature extraction
method (Li et al., 2018b).
The choice of the reference electrode is an inevitable issue
in EEG research. But there is still debate about which is the
most suitable reference (Yao et al., 2019). All signals of EEG in
each electrode are obtained as the difference between the electric
potentials in its location and in the location of the reference
electrode. However, it is impossible to find a neutral reference
on the scalp or body. To solve this problem, many studies have
aimed to find a relatively non-active point on the body surface
(Offner, 1950; Yao, 2001; Hagemann et al., 2001). Common
online recording references include the FZ, CZ, PZ, OZ left/right
earlobe, and nose (Andrew and Pfurtscheller, 1996; Bruder et al.,
1997; Bas¸ar et al., 1998; Essl and Rappelsberger, 1998; Hu
et al., 2018a). Offline re-references mainly include the linked
ears/mastoids (Garneski and Steelman, 1958), the common
average reference (CAR; Offner, 1950), the reference electrode
standardization technique (REST; Yao, 2001) and the unified
referencing framework (rREST; Hu et al., 2018b). The family
of EEG unipolar references including REF-Cz, CAR, and REST,
etc. has the ‘‘no memory’’ property (Hu et al., 2019). Because of
this, we can always re-reference the dataset. Although the brain
network method has been applied to the study of motor tasks, the
problem of selecting an appropriate reference electrode in EEG
research has not been solved (Popovych et al., 2016; Storti et al.,
2016; Li et al., 2018b). Similarly, the problem of the selection
of the reference electrode also exists in the motor imagination
(MI) that have similar pattern of brain networks with the ME
(Zhang et al., 2015; Li et al., 2018a, 2019). Recently, some
studies investigated how different reference choices influence
scalp EEG functional connectivity using simulated EEG data
(Chella et al., 2016; Huang et al., 2017). These studies all pointed
out that different references significantly alter the topography
of EEG connectivity patterns. Although the influence of the
reference electrode on MRCP has been studied (Hu et al., 2017),
its effect on the brain network of motor tasks has not been
fully discussed.
Hand movement tasks rely on multiple brain regions working
together and consist of motor preparation and motor execution
processes. The present study aimed to investigate the influence of
different references (REF-CZ, CAR, and REST) on brain network
analysis for motor tasks. First, we divided motor tasks into
two phases based on ERD in the beta band: motor preparation
and motor execution. Functional brain networks were then
constructed with different re-referencing schemes in the beta
band. Finally, we compared connectivity variations and the
graph-theoretic measures of functional brain networks during
motor tasks using different re-referencing schemes.
MATERIALS AND METHODS
Subjects
A total of seventeen right-handed healthy subjects (one female;
mean age 26 years, range: 23–29 years) were recruited from
Zhengzhou University. They had a normal or corrected-
to-normal vision, and none of them had a history of
motor or neurological disease. Every subject was informed of
the experimental procedure and signed a letter of consent
before the experiment. The study was approved by the local
ethics committee for the Protection of Human Subjects at
Zhengzhou University.
Procedure and Task
Each subject was comfortably seated in front of a computer
screen and remained idle, with their hands, forearms, and elbows
resting on the armrest of the chair. During EEG recording, each
subject was asked to try to avoid eye movement, swallowing, and
unnecessary limb movements.
As shown in Figure 1, subjects performed self-paced hand
movement tasks that required intervals of more than 5 s between
each task. Subjects were free to choose their left or right
hands for each movement, but the number of left-/right-hand
movements was approximately the same. They were instructed
not to count the number of seconds in an interval, and we
emphasized the importance of movement intention immediately
before performing the movement. The recording was done in ten
4 min long blocks with intermittent 2 min breaks.
FIGURE 1 | The timeline of a run. Each trial began in the idle stage, in which
the subject rested his or her hands, forearms, and elbows on the armrest of a
chair and relaxed his or her hands. The subject performed each hand
movement task after taking more than 5 s of idle time. After the completion of
a task, the subject returned to the idle stage and prepared for the next task.
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Zhang et al. Influence Electroencephalogram References Functional Network
EEG Recording and Signal Preprocessing
EEG data were recorded by a Neuroscan NuAmps digital
amplifier system with 64 electrodes arranged in the standard
10–10 EEG configuration. All the brain regions were covered
by these electrodes. Two extended bipolar channels (BP3 and
BP4) were used to acquire the left and right arms’ EMG
(electromyography) signals. The EEG signals were acquired at a
sampling rate of 250 Hz with the REF-CZ as an online recording
reference, and the impedance of all electrodes was less than 5 k.
The REF-CZ was located between the CZ and CPZ electrodes.
Data preprocessing consisted of two parts: EMG data and EEG
data preprocessing. EMG data were used to obtain the onset time
of each hand movement for each trial. The EMG data were band
filtered by a basic finite impulse response filter with respective
cutoff frequencies of 6 and 50 Hz. We then calculated the energy
of the filtered data and set the proper threshold to detect the
onset time of movement. Next, we checked the records visually
to remove instances of false recognition. Finally, we recorded the
onset times in a TXT file (Hu et al., 2017).
The EEGLAB toolbox was used to preprocess and analyze the
EEG data (Delorme and Makeig, 2004). First, we imported the
time labels of movement onset into the data. Next, the raw EEG
data were visually inspected for eye-blinks and muscle artifacts.
Then, testing with CAR was conducted using the reref function
from the EEGLAB toolbox, and testing with REST was conducted
using the rest_refer function1.
To extract the frequency band of interest, the data were
band-pass filtered in 13–30 Hz. Then, taking the EMG onset as
0 ms, all EEG data of the three references were segmented into
5,000 ms epochs ranging from 3,000 ms before to 2,000 ms after
the onset of movement [i.e., (3,000, 2,000) ms].
For examining the ERD patterns in the beta band, we used
the FieldTrip toolbox for time-frequency analysis (Oostenveld
et al., 2011). Multitaper method (MTM) based Hanning tapers
were used to calculate the time-frequency representations (TFRs)
of power (Thomson, 1982). Time-frequency representations
(TFRs) were calculated using the data with the REST reference
due to the theoretical advantages of the REST reference
(Yao et al., 2005). As shown in Figure 2, our results
showed that ERD was observed in the central areas of
the contralateral hemisphere as early as about 1,000 ms
before movement onset and continued to enhance. ERD
was also observed soon after over the central area on the
ipsilateral hemisphere, and its distribution became bilaterally
symmetrical from 0 ms concerning movement onset. At about
1,000 ms, weak ERD was observed only on the ipsilateral
hemisphere, and ERS begins to appear in the contralateral
hemisphere. The results of the ERD distribution in the beta
band are consistent with previous research (Pfurtscheller and
Aranibar, 1977; Bai et al., 2005). To compute functional
network changes, the preprocessed signals were divided into
three stages according to the ERD change pattern: idle stage
[(3,000, 2,000) ms], MP stage [(1,000, 0) ms], and
ME stage [(0, 1,000) ms].
1http://www.neuro.uestc.edu.cn/REST/
Brain Network Construction
This study used the following most commonly used connectivity
measurement methods.
(1) Coherence: The coherence, also referred to as the magnitude
squared coherence or coherence spectrum, between two
signals is their cross-spectral density function (Pereda et al.,
2005). Coherence is a widely-used measure for characterizing
linear dependence between a pair of stochastic processes, as
well as a quantitative measure of their phase consistency, and
may be viewed as the equivalent measure of cross-correlation
in the frequency domain. It is defined as
COHxy(f)=
Sxy(f)
2
Sxx(f)Syy (f)(1)
where Sxy(f) is the cross-spectrum of the signals x(t) and
y(t), and Sxx(f) and Syy (f) are their respective self-spectra. The
COH ranges between 0 and 1: 0 COH 1.
(2) Phase-Locking Value: The phase-locking value (PLV) makes
use only of the relative phase difference (Lachaux et al.,
1999). It is defined as
PLV =1
K
XK
K=1exp(jθ(tk))
(2)
where tis the time, and θ(tk) is the phase difference Ø1(tk) –
Ø2(tk) PLV measures the variability of this phase difference
at t. If the phase difference varies little across the trials, PLV
is close to 1; it is close to zero otherwise.
(3) Phase Lag Index: The phase lag index (PLI) was introduced
by Stam et al. (2007), aiming to deal with the problem of
volume conduction and active reference electrodes in the
assessment of functional connectivity. The central idea is to
discard phase differences that center around 0 mod π. It is
defined as
PLI =
sign [1Ø(tk)]
(3)
The PLI ranges between 0 and 1: 0 PLI 1. A PLI of
0 indicates either no coupling or coupling with a phase
difference centered around 0 mod π. A PLI of 1 indicates
perfect phase locking at a value of Ø different from 0 mod
π. The stronger this non-zero phase locking, the larger
the PLI.
We adopted the above-mentioned three methods to construct
scalp functional networks for the idle stage, MP stage, and ME
stage of left-/right-hand movement tasks. For every method,
we calculated the connectivity value between per electrode
pairs and stored all the connectivity values in the adjacency
matrix in which the rows and columns are arranged in order
of channel number. Then, the adjacency matrix G of each
subject has been obtained by averaging the adjacency matrices
of all trials for left-/right-hand movement networks from
each reference and stage. Finally, 18 adjacency matrices of
functional networks of the left-/right-hand movement were
calculated from the data of three stages with three references for
each subject.
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Zhang et al. Influence Electroencephalogram References Functional Network
FIGURE 2 | The ground-average event-related desynchronization (ERD) patterns in the beta band (13–30 Hz) during the left-/right-hand movement. The baseline
was 3 to 2 s. Blue indicates ERD, yellow indicates event-related synchronization (ERS). Left, Left-hand movement; Right, Right-hand movement.
Network Measures
Graph theory provides a quantitative analysis method
for us to analyze complex brain networks. Commonly
used network parameters can be roughly divided into
local parameters (node degree, clustering coefficient, local
efficiency, and so on) and global parameters (characteristic
path length, global efficiency, and so on). Node degree
is the most fundamental network measure, and most
other measures are ultimately linked to the node degree
(Bullmore and Sporns, 2009). The characteristic path length
is a commonly used global parameter, and most other
global parameters are related to it. Therefore, this article
selected the two parameters of node degree and characteristic
path length.
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Zhang et al. Influence Electroencephalogram References Functional Network
(1) Degree: the degree of a node is the number of connections
involving it (Sporns et al., 2004). It can be defined as follows:
Di=Xj6=iAij (4)
where Diis the degree of node i, and Aij represents the
connectivity between node iand node j.
(2) Characteristic path length: path length is the minimum
number of edges that must be traversed to go from one node
to another. The average length of the shortest paths between
per pairs of nodes is the characteristic path length. Thus,
it is also called the shortest path length. It can be defined
as follows:
L=1
N(N1)Xi,jN,i6=jdij (5)
Where dij is the shortest path (geodesic) between iand j. Note
that dij =for all disconnected pairs i,j.
Statistical Analysis
To find out which edges had significantly increased connectivity
during the MP and ME stages compared to the idle stage,
the three networks of the left-/right-hand movement were
constructed with different references. To get the average
adjacency matrix for the three stages, we averaged all subjects’
adjacency matrices at each stage. Then the averaged adjacency
matrices of the idle stage were subtracted from the averaged
adjacency matrices during MP and ME stage. All elements in
these difference matrices greater than 0.02 are considered to have
significantly increased. To avoid the consideration of spurious
interactions, a one-tailed paired t-test was used to test the
differences in connectivity of all the subjects between two stages.
The sample size of the t-test was 17 since we had 17 subjects.
To reduce the false-positive rate, statistical tests were corrected
with the BHFDR method (Benjamini and Hochberg, 1995). If the
BHFDR-corrected significance level of an edge was less than 0.05,
we considered the corresponding connectivity to have increased
significantly and retained the edge, otherwise discard this edge.
To compare the network parameters of functional networks with
different references, the two-sample paired t-test was used to test
the differences in degree and characteristic path length between
three references (REF-CZ vs. CAR, REF-CZ vs. REST, and CAR
vs. REST). The sample size of the t-test was 17.
RESULTS
Effect of Reference Choice on COH, PLV,
and PLI Networks Analysis in Hand
Movement Task
The number of edges with significantly increased connectivity
during the MP stage and ME stage is shown in Table 1 (P<0.05,
BHFDR). A significant increase in connectivity can be observed
in COH and PLV networks. The networks with the REF-CZ
reference has far fewer edges than the networks with CAR and
REST reference during the ME stage. The networks with REST
reference has slightly more edges than the networks with CAR
reference in both the stages of MP and ME. There was almost no
TABLE 1 | The number of edges with significantly increased connectivity during
the movement preparation (MP) stage and movement execution (ME) stage.
Connectivity Movement stage The number of edges with
significantly increased connectivity
(P<0.05, BHFDR)
REF-CZ CAR REST
COH MP (L) 21 6 17
ME (L) 5 60 86
MP (R) 11 16 25
ME (R) 7 19 46
PLV MP (L) 28 24 30
ME (L) 10 85 99
MP (R) 10 21 38
ME (R) 2 46 65
PLI MP (L) 0 0 0
ME (L) 0 0 0
MP (R) 0 0 1
ME (R) 0 0 0
significant increase in connectivity in the PLI networks, which
may reflect that non-zero lag phase synchronization between
almost all electrode pairs does not increase significantly during
MP and ME stages in the beta band.
Figure 3 shows the edges of connectivity significantly
increased compared to the idle stage in the PLV and COH
networks with different references during left-/right-hand
movements (P<0.05, BHFDR). For the REF-CZ reference,
the connections between the contralateral respective areas of
the motor cortex can be observed during the MP stage, but
there are few connections between these areas during the ME
stage. For the CAR and REST references, not only can be
observed the connections between the contralateral respective
areas of the motor cortex during the MP stage, but also the
connections between the bilateral respective areas of the motor
cortex and the somatosensory cortex area during the ME stage.
The connections between the respective areas of the motor cortex
areas of the networks with REST reference were more than the
CAR reference network. The networks with CAR reference show
some non-motor cortex activities. Also, the left-hand movement
had significantly more significant connectivity increases than the
right-hand movement, which may be related to the fact that
our subjects were all right-handed. Ipsilateral motor cortical
activation could be due to additional internal effort required
for left-handed movement, which is more complex than right-
handed movement (Dhamala et al., 2003). This phenomenon
may also be explained as the left-brain dominance for motor
planning in humans (Sabate et al., 2004).
Effect of Reference Choice on Network
Measures
To examine the differences in the local network properties
of different reference networks, we calculated the degree
distribution of the brain network of 17 subjects. The topographic
map of the average nodal degree of the 17 subjects during the
left-hand movement execution (ME) stage is shown in Figure 4.
For the COH and PLV networks, the degree distribution of
networks with REF-CZ reference is different from CAR reference
and REST reference. Especially the degrees of the parietal near
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Zhang et al. Influence Electroencephalogram References Functional Network
FIGURE 3 | The significantly increased edges (P<0.05, FDR corrected) compared to the idle stage during the movement preparation (MP) stage and movement
execution (ME) stage in the beta band (13–30 Hz) from 17 subjects. The thickness of the line indicates the increase in connectivity. (A) Coherence (COH) network
changes pattern during left hand movement. (B) COH network changes pattern during right hand movement. (C) Phase-locking value (PLV) network changes
pattern during left hand movement. (D) PLV network changes pattern during right hand movement.
the reference electrode is very small. The degree distribution
of networks with CAR and REST reference is similar, and the
degree is mainly higher in the frontal. For the PLI networks,
the degrees near the reference electrode of network with the
REF-CZ reference is only slightly lower than the CAR and REST
references. Similar results were obtained in the networks of the
MP stage and ME stage of both the left and right hand.
The number of nodes with significant differences is shown
in Table 2 (P<0.05, t-test). For the COH and PLV networks,
the degrees of many nodes of networks with different references
are significantly different. The networks with REF-CZ reference
differ greatly from networks with the CAR and REST reference,
while the difference between the CAR and REST reference
networks is relatively small. For the PLI network, only a
few nodes have significant differences between networks with
different references.
For global network properties, we calculated the characteristic
path length of the brain network of the 17 subjects. By
comparing the characteristic path lengths among different
reference networks, no significant differences were found. The
average characteristic path length of the left-hand ME stage is
shown in Figure 5.
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Zhang et al. Influence Electroencephalogram References Functional Network
FIGURE 4 | The topographic map of node degree during ME. The black
crosses mark the location of the original reference REF-CZ.
DISCUSSION
To find a better reference electrode for hand movement task
studies based on EEG, we constructed the functional networks
of ten subjects with REF-CZ, CAR, and REST references. We
studied the influence of the reference choice on the results of
network analysis and graph theory indicators.
Many studies based on brain imaging technology had found
that the interactions of cortico-motor networks, including the
primary motor cortex (M1), the supplementary motor area
(SMA), the premotor cortex (PMC), and parietal cortex, are
strengthened during the MP stage and ME stage, and the
networks showed contralateral advantage (Jiang et al., 2004;
Wheaton et al., 2005; Wu et al., 2011; Popovych et al., 2016).
The interaction of these areas during the MP was related to the
planning of movement, and similar interaction could also be
observed in motor imagery (MI; Sabate et al., 2004; Hanakawa
et al., 2008). SMA and PMC were responsible for the selection,
planning and some aspects of motor control for movement or
the direct control of some movements and more involved in
planning or preparing for movement than M1 (Weinrich and
FIGURE 5 | The mean (±SD) values of the characteristic path length of
functional networks with different references.
TABLE 2 | The number of nodes with significant differences in node degree
during the MP stage and ME stage.
Connectivity Movement
stage
The number of significantly
different nodes (P<0.05, t-test)
REF-CZ REF-CZ CAR
vs. CAR vs. REST vs. REST
COH MP (L) 43 43 25
ME (L) 45 42 26
MP (R) 42 43 21
ME (R) 45 43 27
PLV MP (L) 40 41 21
ME (L) 43 41 22
MP (R) 41 41 21
ME (R) 45 44 23
PLI MP (L) 2 1 2
ME (L) 1 0 1
MP (R) 1 1 1
ME (R) 0 0 1
Wise, 1982; Schluter et al., 1998; Picard and Strick, 2003; Nguyen
et al., 2014). The motor cortex showed stronger connectivity
during the ME stage than the MP stage. M1, which was
responsible for the execution of movement, played an important
role in movement networks during the ME stage (Jiang et al.,
2004). However, areas such as PMC and SMA also contributed to
the execution of movement. One study showed that the coupling
parameters among SMA, PMC and M1 increased with recovery
and predicted a better outcome in stroke patients suffering
from hand motor deficits (Rehme et al., 2011). Besides, the
parietal and sensory cortices were also activated during hand
movements (Okuda et al., 1995; Christoph et al., 2001), and
showed increased cortical coupling with the PMC and SMA
(Wheaton et al., 2005).
We confirmed that the choice of reference electrode
significantly influences the network analysis results. For the
REF-CZ reference, we could not observe obvious synchronized
activity between the respective areas of the motor cortex during
the ME stage where the M1 activity dominates, most network
connectivity increases are overwhelmed by reference effects. The
main reason for this phenomenon is that the M1 is close to
REF-CZ reference, and the electrical activity could be conducted
to the reference channel. The amplitude and phase information
of the channel signals in M1 are lost, resulting in low measured
synchronization and difficulty in distinguishing its changes.
Therefore, the original REF reference is not a good choice. Both
the CAR and REST references achieve much better results than
the REF-CZ reference. The significant increase in connectivity
between the respective areas of the motor cortex (i.e., PMC, SMA,
and M1) can be observed from these two reference networks,
and a clear contralateral advantage can be observed. Compared
with the CAR reference, the REST reference network shows more
pronounced activity in the motor cortex. The networks with CAR
reference show a lot of occipital activities. This phenomenon
has not been supported by relevant electrophysiological evidence
during voluntary movement and our experiments did not involve
any visual stimuli. It may show that the performance of the REST
reference is more similar to real brain activity than CAR on scalp
EEG functional network analysis.
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Zhang et al. Influence Electroencephalogram References Functional Network
For graph theory indicators, our results show that the local
properties of the network are also influenced by different
references. Beta rhythm is associated more with motor cortical
function, and tend to show maximum power in the frontal
and central areas (Hari and Salmelin, 1997). As mentioned
above, the synchrony between the respective areas of the motor
cortex will increase during the movement task. However, the
degrees of networks of these areas with REF-CZ reference is
small because the reference electrode is too close to the active
areas. Since the degree of any node is related to all nodes in
the network, the overall degree distribution will be affected by
the reference effect. This reference effect is greatly reduced in
the network with CAR and REST reference. As for the global
property of the network, the results among different references
are not significantly different. The characteristic path length,
as a global network attribute, reflects the overall information
transmission efficiency of the network (Bassett and Bullmore,
2006). Although different references may significantly affect the
connectivity between some nodes, they are still not enough to
greatly change the overall performance of the network.
For all three connectivity measures, different connectivity
calculation methods are affected by reference choice. In
this study, due to the lack of meaningful PLI results, the
connectivity results of PLI could not be compared. The degree
distribution of PLI networks is least affected by the reference.
This was expected because PLI only retains non-zero lag
correlation that is more likely to reflect the true correlations
of underlying sources and discards zero-lag correlation that
may be affected by volume conduction and reference effects
(Stam et al., 2007). Thus, the PLI is theoretically much less
affected by the influence of common sources and active reference
electrodes. However, there are the risks of missing functionally
meaningful correlations at zero lag in this approach. Compared
to COH, PLV uses only phase information and no amplitude
information. Therefore, PLV is slightly less affected by reference
effects than COH.
In previous studies of the effect of reference electrodes on RP,
the performance of the CAR reference and the REST reference
has similar results (Hu et al., 2017). In this study of the influence
of reference electrodes on motor functional brain networks,
REST references have shown superior performance to CAR. This
also illustrates that the phase information used in functional
network connectivity calculations is more sensitive to the impact
of reference effects than the amplitude information.
CONCLUSIONS
On the one hand, our study provides evidence for the reference
selection in the construction of the scalp EEG network during
movement tasks. On the other hand, the results further confirm
that the EEG reference plays an important role in data analysis in
neuroscience. For the study of movement Scalp EEG functional
networks, it is very important to choose the appropriate reference
electrode. Our results show that the reference has a great
impact on the connectivity changes of functional networks and
graph theory indicators in hand movement tasks. Researchers
may obtain different conclusions when functional networks
are constructed with different references. In particular, online
recording references located near the motor areas can lead to
large biases. According to our conclusion, the REST and CAR
reference can greatly reduce the effect of the online recording
reference location, while REST may be slightly better than CAR.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by the local ethics committee for the Protection of
Human Subjects at Zhengzhou University. Participants provided
their written informed consent to participate in the study.
AUTHOR CONTRIBUTIONS
LZ: conceptualization, methodology, software, and
writing—reviewing and editing. PW: conceptualization,
methodology, software, and writing—original draft preparation.
RZ: conceptualization, methodology, and data curation.
LS: investigation, funding acquisition, and supervision. JG:
investigation, supervision, and validation. MC: investigation,
and supervision. YH: conceptualization, project administration,
funding acquisition, investigation, and supervision.
FUNDING
This work was supported by the National Natural Science
Foundation of China #61603344 and 61803342, the Key Project
at Central Government Level #2060302, Program for Science and
Technology of Henan Province of China #182102210099, Science
and Technology Project of Henan Province #202102310210, and
Key Project of Discipline Construction of Zhengzhou University
#XKZDQY201905.
ACKNOWLEDGMENTS
We thank LetPub www.letpub.com for its linguistic assistance
during the preparation of this manuscript.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
The reviewer PX declared a past co-authorship with one of the authors RZ to the
handling Editor.
Copyright © 2020 Zhang, Wang, Zhang, Chen, Shi, Gao and Hu. This is an
open-access article distributed under the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited and that the
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
with these terms.
Frontiers in Human Neuroscience | www.frontiersin.org 10 September 2020 | Volume 14 | Article 367
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