Towards Successful Multi-user Brain-Computer Interface (BCI)
Gaming: Analysis of the EEG Signatures and Connectivity
Finda D. Putri, Hao Ding, Abdullah Garcia and Aleksandra Vuckovic
Center of Rehabilitation Engineering, University of Glasgow, Glasgow, U.K.
Keywords: EEG-based BCI, Multi-user BCI, BCI Gaming, Competitive Gaming, Social Interaction.
Abstract: The information related to the impact of multi-user BCI on cortical activity is still relatively limited. This
ongoing study performed a competitive multi-user BCI gaming that is based on alpha band operant
conditioning and explored the brain activity and connectivity during the most, and the least successful gaming
runs. Ten healthy adults were involved in three days of gaming experiments in pairs. Multi-channel paired t-
test found a significant decrease (p<0.05) of absolute alpha power in the frontal left hemisphere channels in
the dominant players during the most successful gaming compared to the baseline of the same group. This
decrease is associated with the frontal alpha asymmetry (FAA) that occurred in the leading players.
Connectivity estimation via partial directed coherence (PDC) was also performed, showing the deactivation
of brain networks during the successful gaming of the dominant players compared to their baseline which
might indicate the “networks switching” mechanism from resting state to a more-demanding cognitive task.
Different baseline connectivity patterns were also found in the group of dominant players compared to the
group of non-dominant players, suggesting the possibility of using baseline connectivity information as a
predictor of gaming performance.
1 INTRODUCTION
The development of BCI technology has increased
the interest of BCI application for entertainment
purpose, commonly formed as BCI games. Gaming is
a highly stimulating activity that induces different
kinds of cognitive responses, making it very
challenging, yet very appealing for BCI application.
More advanced BCI-gaming technology initiated
the emerging of multi-user BCI games. The general
requirement of a multi-user BCI has been described
as the involvement of two or more users with
integrated brain activity to a BCI application (Nijholt,
2015; Nijholt & Gürkök, 2013). Social interaction
tasks (i.e. cooperation and competition) are ideally
implemented in multi-user BCI paradigms (Bonnet,
Lotte & Lécuyer, 2013; Gürkök et al., 2013; Nijholt
& Gürkök, 2013), as it has been widely used in the
classic video gaming.
Multi-user BCI development is a complex
process. Several factors composed of technical
challenges like the BCI architecture design and the
classification accuracy to behavioral factors like the
effect of social interaction on BCI performance
require special attention. Multiple studies around
interactive multi-player BCI gaming have been
performed, mainly to test the BCI classification
accuracy using different types of control, e.g. Steady-
State Evoked Potential (SSVEP) (Cruz et al., 2017;
Gürkök et al., 2013), P300 (Korczowski et al., 2016;
Korczowski, Congedo & Jutten, 2015), motor
imagery (MI) (Bonnet et al., 2013), and the
combination of different paradigms such as SSVEP/
P300 with alpha power (Mühl et al., 2009). Some of
these studies have also reported the impact of gaming
interaction on the quality of BCI performance. For
example, Bonnet et al. using BrainArena, an MI-
based multi-user BCI game, which was presented as
a simple ball game, found that social interaction is not
necessarily compensating the quality of BCI
performance. Additionally, compared to the single
player setup, they reported that the users prefer multi-
player gaming due to the fun and motivational factors
(Bonnet et al., 2013).
However, contradicting results were found by
another study that investigated the different types of
game control (BCI control and classic mouse control)
and their implications on co-experience during a
collaborative BCI game called “Mind The Sheep!”
(Gürkök et al., 2013). They found that when using
BCI control, co-experience was reduced by
Putri, F., Ding, H., Garcia, A. and Vuckovic, A.
Towards Successful Multi-user Brain-Computer Interface (BCI) Gaming: Analysis of the EEG Signatures and Connectivity.
DOI: 10.5220/0008201400590065
In Proceedings of the 3rd International Conference on Computer-Human Interaction Research and Applications (CHIRA 2019), pages 59-65
ISBN: 978-989-758-376-6
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
59
collaborative interaction due to the users’ concern of
losing control if they were paying too much attention
to collaboration.
In the field of multi-user BCI gaming, the amount
of information related to the neurological impact of
social interaction is still relatively limited. A study by
Toppi et al. has explored the cortical changes and
effective connectivity in the brain of pairs of pilots
during a flight scenario inside the flight simulator
(Toppi et al., 2016). They found that in theta band (3-
7 Hz), there is a significant influence (p<0.05)
between the flight phase factor (take-off, cruising,
and landing) on connections’ density and efficiency,
and the involvement of frontal networks of all the
pilots. They also reported a significant increase
(p<0.05) of theta power in the frontal and parieto-
occipital areas of the co-pilots during take-off, and in
the frontal area of both pilots during landing. A
significant increase of parietal alpha power was also
found only in co-pilots during landing (the captain led
take-off and the co-pilot led the landing due to a
deliberate electrical failure applied to the captain’s
instrumentation). Although physical control was
applied instead of BCI control, this study has
successfully demonstrated the impact of interaction
and the role changes on the cortical activity during a
cooperative task that requires a high-degree of
concentration, which can be a useful reference for
interactive BCI gaming development, especially
when applying high concentration task.
The aim of the current study is to analyse the
electrophysiological changes of the brain during a
competitive multi-user BCI gaming that is based on
the alpha band, non-verbalised operant conditioning.
We analysed the cortical changes by measuring
relative alpha power during gaming and resting states.
Neuronal connectivity was estimated in both gaming
and resting states, specifically during the most and
least successful gaming runs. The following sections
of this paper will be organised as: the materials and
methods, the results, the discussion, and the
conclusion.
2 MATERIALS AND METHODS
2.1 Experimental Setup
Ten healthy able-bodied adults (mean age 26.9±4.14,
6 females and 4 males) participated in EEG
experiments where they were sorted into five pairs.
They signed the written consent form prior to the
experiments. Ethical permission was granted by the
University’s College Ethical Committee.
The application consisted of three main
components: MATLAB functions (MATLAB 2015a,
The Mathworks, Inc., USA), a Simulink model and a
JAVA (version 1.8.0) Graphical User Interface
(GUI). MATLAB worked as an entrance system for
the application and served as the connector and
controller of the Simulink model and JAVA GUI.
The JAVA GUI shown in Figure 1 displays two
bars at each side of the screen and a seesaw in
between. Each bar represents the fluctuation of the
percentage relative alpha power (RA) to the power of
wider frequency band of 2-30 Hz, with a moving
average window of 0.5 second provided from the
electrode Pz in both players. Scoring was achieved
when one player managed to increase the power
≥10% than the other to make their side of seesaw tilt
down, and hold it for at least 1 second. The bar
changes colour from blue to green whenever players
gain 1 point. Prior to gaming experiment, baseline RA
was measured from each player to set individual
thresholds, which were later used to calculate a
normalising coefficient (NC). NC was applied to the
input signal generated by the player with a higher
threshold in the pair. This was done in order to
equalise the initial conditions. Furthermore, these
coefficients were acquired by dividing the RA of the
player with the lower threshold (RA
Low
) by that of the
player with the higher threshold (RA
High
), explained
as follows:
NC = RA
Low
/ RA
High
(1)
This approach is expected to help the non-dominant
player to maintain their control over their bar even if
their opponent has significantly higher baseline alpha
power.
EEG signal was recorded by a g.USBamp (g.tec
medical engineering GmbH., Austria) amplifier. The
EEG electrodes arrangement was set following the
standardised 10-20 EEG electrode placement system
(Homan, Herman & Purdy, 1987). The impedance
was kept below 5 kΩ. Linked ear reference was used
and FCz was used as the ground. Sampling frequency
was set to 256 Hz. Online band-pass filter was set
between 0.5 and 60 Hz (and a notch filter at 50 Hz)
using 5
th
order infinite impulse response (IIR) digital
Butterworth filter within the g.USBamp.
During the EEG experiments, two players were
seated next to each other in front of one screen. They
were instructed to compete with each other by
increasing the power bar located on their side of the
screen and to ‘push down’ the seesaw such that it
would be heavier towards their side.
Each pair performed three experimental sessions
on three separate days, where each session consisted
CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications
60
of six sub-sessions (5 minutes each). Pre- and post-
gaming eyes-open baseline were recorded in a relaxed
state for 2 minutes in every session. For the first two
sessions, EEG was recorded from only Pz electrode,
and for the third session, 32 and 16 electrodes were
used by the dominant (D) and the non-dominant (ND)
group of players respectively, where the dominance
was decided based on the highest average scoring
performance in the previous two sessions.
Figure 1: Competitive gaming interface. Users have to
increase their power bar ≥10% than the other for at least 1
second to score 1 point.
2.2 Data Analysis
All EEG data were analysed using EEGLAB (UC San
Diego, SCCN, USA) toolbox in MATLAB. All EEG
data were band-pass filtered between 2 and 30 Hz.
For single-channel data, visual inspection and manual
noise removal were performed, and for multi-channel
data, independent component decomposition was
performed by using an Independent Component
Analysis (ICA) algorithm implemented in EEGLAB
for further noise removal.
The average power spectrum analysis was
estimated by using Welch’s method (Welch, 1967),
with a 50% overlap of 4 second long windows.
Individual RA at Pz was analysed at a group and
individual level. Spatial distribution of power in all
conditions was estimated based on multi-channel
EEG recording. Paired t-test analysis was applied to
multi-channel EEG data to test statistical significance
in absolute power between conditions.
PDC, a multivariate measurement of directional
causality in the frequency domain, was calculated to
estimate brain connectivity. PDC from i to j can be
defined as:
𝜋
𝑖𝑗
(
𝑓
)
=
𝐴
𝑖𝑗
(𝑓)
𝑎
𝑗
(
𝑓
)
𝑎
𝑗
(𝑓)
(2)
Where 𝐴
𝑖𝑗
(𝑓) is the i,j-th element of A(f), a matrix of
frequency domain transformed model coefficient, and
𝑎
𝑗
(𝑓) is the j-th column of matrix A(f) (Baccalá &
Sameshima, 2001). PDC values were measured from
10 representative electrodes and the significant PDC
values were estimated by using asymptotic statistic (p
< 0.05) and False Discovery Rate (FDR) correction (p
< 0.05) was applied for multiple comparisons.
3 RESULTS
Scoring results were used to measure the BCI
performance of the users. From all three sessions (in
a total of 30 gaming sub-sessions per session for all 5
pairs), on average, group D won all the time in the
first and second session and won only 80% (24 sub-
sessions) in the third session (group ND won 6 sub-
sessions). Results in Figure 2 show the average
percentage of individual gaming RA along with the
average scoring performance, from all sessions. The
bars with an asterisk represent the players with higher
baseline RA, where NC was applied to their RA
during gaming (these players received feedback of
their normalised RA instead of their real RA). Our
results suggest that higher baseline RA does not
always reflect better performance. Players with lower
baseline RA can still win the game. It also shows that
daily adjustment of the baseline RA was necessary, as
in some pairs, different players had larger/smaller
baseline RA on different days.
In multi-channel data, two gaming conditions
were selected based on the highest/lowest scores of
the last session. Table 1 shows the scores of the
highest and the lowest scoring gaming sub-sessions in
both groups during the last experimental session.
Based on this measure, the multi-channel analysis
was grouped into the highest scoring D, lowest
scoring D, highest scoring ND, and lowest scoring
ND. We then categorised highest scoring D as “the
most successful gaming” and the lowest scoring ND
as “the least successful gaming”.
Paired t-test (p<0.05) of the multi-channel data
across subjects (FDR correction applied) found
significant decrease of absolute alpha power only in
the most successful gaming compared to their
baseline, specifically in the frontal electrodes of the
left hemisphere (FP1, AF3, FC5, and FC3), as seen in
Figure 3. There is no statistical significance found in
the theta and beta bands in all conditions and groups,
showing the selectivity of EEG power modulation in
the alpha band only. Although there is no significant
increase found on the training electrode, Figure 3
Towards Successful Multi-user Brain-Computer Interface (BCI) Gaming: Analysis of the EEG Signatures and Connectivity
61
shows that spatially source of high alpha was reduced
around Pz during successful gaming.
Figure 4 shows the estimated PDC in the alpha
band, which reflects the connections among networks
during baseline of both groups, and during the most
successful gaming condition, highest scoring D (red
square), and its counterpart- the other player on that
particular session, and during the least successful
gaming, lowest scoring ND (blue square), and its
counterpart. Ten electrodes covering four brain
cortical areas were chosen as the representative nodes
(i.e., F3, Fz, F4, C3, Cz, C4, P3, Pz, P4 and Oz). Our
observations found higher connectivity during
baseline in group D (players with higher baseline
RA), which are indicated by the higher estimated
PDC values and the more complex connected
networks, in contrast, the baseline of the group with
lower RA (Figure 4 top right) shows relatively lower
estimated PDC values and less connected networks.
Table 1: The highest scoring (Hi) and the lowest scoring
(Lo) gaming sub-sessions (SS) in both groups.
Pair
D
ND
Lo
Hi
Lo
1
Score
134
6
1
SS
6
5
1
2
Score
54
96
38
SS
4
4
2
3
Score
76
61
33
SS
5
4
3
4
Score
50
72
50
SS
3
5
4
5
Score
105
38
17
SS
5
2
1
During the most successful gaming (red square),
our results show that the connectivity is decreasing
(from baseline state) in terms of the PDC values and
the number of network connections, and the
remaining networks from baseline are found around
the frontal electrodes. The ND counterpart of the most
successful gaming shows increased connectivity
around frontal and central areas, specifically in the
left hemisphere (F3 Fz and C3 Cz) with an
emerging connection from C3 to P3 compared to their
baseline state. Similar to their counterparts, occipital
connectivity is decreasing compared to baseline. This
ND group is just the counterpart of the most
successful gaming, not necessarily depicting the
highest scoring condition for the group ND.
For the least successful gaming (blue square),
compared to their baseline, the number of
connections found are low, with the only strong
remaining connection found from F3 to Fz. The D
counterpart of the least successful gaming, shows
higher PDC values in the parieto-occipital
connectivity, compared to their baseline and the
highest scoring D. In contrast to the most successful
gaming, group D of this particular condition shows
more complex connectivity, reflecting that this is not
their successful gaming performance, despite still
dominating group ND during gaming.
Figure 2: (a) Mean and standard deviation of individual
gaming RA (%) at Pz from all sessions. The bars with
asterisks show normalised RA, which used as the feedback
to the players with higher baseline RA, and the bars without
asterisks show real RA. (b) Mean and standard deviation of
scores from all users from all sub-sessions.
Figure 3: Paired t-test analysis shows a significant decrease
of alpha in some frontal electrodes during the highest
scoring gaming sub-sessions compared to baseline before
gaming for group D.
CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications
62
Figure 4: PDC estimation results show connectivity in the
alpha band in ten electrodes during baseline (top) and the
highest scoring D (red square) and its counterpart
representing the most successful gaming (middle), and the
lowest scoring ND (blue square) and its counterpart
(bottom).
4 DISCUSSION
The present study aimed to examine the effect of
competitive gaming on brain cortical activity and
neuronal connectivity. The results from the average
of the individual gaming RA and the average scoring
suggest that higher average RA (both in baseline and
during gaming) does not directly produce higher
scores. We found a case where the winning player had
a lower average RA during gaming. One reason for
this is that during gaming, players with higher
baseline RA received feedback on normalised, i.e.
lower RA than their original RA. In order to score,
timing is essential, as someone can only score if they
manage to hold their bar higher for at least 1 second.
Thus, in this case, the losing player was not able to
sustain high alpha power for a relatively long period,
though on average, their RA might be high. BCI
control requires skill and training is necessary to help
the users obtaining and maintaining that skill. In this
setup, the players were not only expected just to
increase their RA, but also to control its duration and
to play based on the opponent’s feedback which is
changing over time.
Our multi-channel spectral analysis shows a
significant decrease of frontal alpha power during the
most successful gaming performed by the dominant
players. It has been known that naturally, alpha power
tends to decrease during task-engagement (Bazanova
& Vernon, 2014) and frontal alpha suppression has
been reported during interactive synchronized finger-
tapping task of two subjects, particularly stronger in
the leading subjects, reflecting their higher cognitive
investment during the synchronized action
(Konvalinka et al., 2014). However, in our results,
significant activation only occurred in one side of the
hemisphere, indicating that this suppression might be
related to the frontal alpha asymmetry (FAA) which
is defined as the different frontal alpha activity
between hemispheres (Davidson et al., 1990). FAA
has been associated with the motivation of
approaching and withdrawing behavior, to be
specific, if the left hemisphere is more activated, then
it is associated with approaching behavior rather than
withdrawing behavior (Coan & Allen, 2004), thus our
results might reflect the motivation to be engaged in
the task by the winning players. Furthermore, left
hemisphere activation in a social setting has been
associated with unsocial and anti-social behavior
(Hecht, 2014), which is in line with our competitive
setting, where the players were expected to play
against each other.
Connectivity estimation results in the alpha band
during successful gaming have demonstrated the
connectivity pattern changes, which consisted of the
deactivation of several network connections from
resting state to gaming performance. Previous
observations of the changing connectivity pattern
between resting state and cognitive task, have
indicated the involvement of three different brain
networks such as the default mode network (DMN),
the central executive network (CEN), and the salience
network (SN) (Goulden et al., 2014; Seeley et al.,
2007; Sridharan, Levitin & Menon, 2008). The DMN
has been defined as a group of networks which is
found to be more active when the task-engagement is
absent whereas the CEN is a group of network that is
activated when the brain is engaged in a specific
mental task (Greicius et al., 2003; Raichle et al., 2001;
Seeley et al., 2007). The SN, which comprised of the
ventrolateral prefrontal cortex, fronto-insular cortex,
and anterior cingulate cortex, is known as the
mediator network that helps the network switching
between task-free and task-engagement states,
between the DMN and the CEN (Goulden et al.,
2014). An alpha neurofeedback training was reported
Towards Successful Multi-user Brain-Computer Interface (BCI) Gaming: Analysis of the EEG Signatures and Connectivity
63
increasing the SN connectivity, where this increased
connectivity was also found to be negatively
correlated with mind-wandering task and resting
alpha rhythm (Ros et al., 2013), two conditions which
activate the DMN (Neuner et al., 2014; Simon &
Engström, 2015).
Our connectivity results also show different
connectivity patterns during baseline in different
groups. This difference might reflect the possibility
of using resting state connectivity to predict gaming
performance, where the similar idea has been
proposed in predicting the neurofeedback training
response in individuals with anxiety (Scheinost et al.,
2014).
In order to obtain more detailed connectivity
information, especially regarding the pattern changes
between the three major networks, more specific and
larger number of electrodes should be chosen for
connectivity analysis. Larger number of subjects and
different range of frequency bands are also required
to explore the different impact of interactive BCI
gaming in different frequency bands. An equal
number of electrodes for both players should be used
for the last gaming session to avoid bias by the
more/less dominant players.
5 CONCLUSIONS
Our study introduced a multi-user competitive BCI
game that is based on alpha operant conditioning. We
reported the preliminary results of the cortical
changes and connectivity from the most successful
gaming. Further development of this study will
include more participants and other social interaction
settings (i.e., collaborative), in order to explore the
brain activity and connectivity during the different
interaction tasks.
ACKNOWLEDGEMENTS
This study is funded by Indonesian Endowment Fund
for Education by the Ministry of Finance, Republic of
Indonesia.
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