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Brain-EE: Brain Enjoyment Evaluation using Commercial
EEG Headband
Maher Abujelala
HERACLEIA Human-Centered Computing Laboratory
Department of Computer Science and Engineering
University of Texas at Arlington
maher.abujelala@mavs.uta.edu
Aayush Sharma
HERACLEIA Human-Centered Computing Laboratory
Department of Computer Science and Engineering
University of Texas at Arlington
aayush.sharma@mavs.uta.edu
Cheryl Abellanoza
HERACLEIA Human-Centered Computing Laboratory
Department of Computer Science and Engineering
Department of Psychology
University of Texas at Arlington
cheryl.abellanoza@mavs.uta.edu
Fillia Makedon
HERACLEIA Human-Centered Computing Laboratory
Department of Computer Science and Engineering
University of Texas at Arlington
makedon@uta.edu
ABSTRACT
Previous studies that involve measuring EEG, or
electroencephalograms, have mainly been experimentally-driven
projects; for instance, EEG has long been used in research to help
identify and elucidate our understanding of many neuroscientific,
cognitive, and clinical issues (e.g., sleep, seizures, memory).
However, advances in technology have made EEG more
accessible to the population. This opens up lines for EEG to
provide more information about brain activity in everyday life,
rather than in a laboratory setting. To take advantage of the
technological advances that have allowed for this, we introduce
the Brain-EE system, a method for evaluating user engaged
enjoyment that uses a commercially available EEG tool (Muse).
During testing, fifteen participants engaged in two tasks (playing
two different video games via tablet), and their EEG data were
recorded. The Brain-EE system supported much of the previous
literature on enjoyment; increases in frontal theta activity strongly
and reliably predicted which game each individual participant
preferred. We hope to develop the Brain-EE system further in
order to contribute to a wide variety of applications (e.g., usability
testing, clinical or experimental applications, evaluation methods,
etc.).
CCS Concepts
• Human-centered computing~Interaction design theory,
concepts and paradigms • Human-centered computing~User
studies • Human-centered computing~HCI theory, concepts and
models • Theory of computation~Active learning • Computing
methodologies~Machine learning algorithms • Mathematics of
computing~Statistical paradigms
Keywords
EEG; Headband; Enjoyment; Muse; Neurofeedback; BCI
1.!INTRODUCTION
Brain-Computer Interface (BCI) describes the neurofeedback
interface that allows users to communicate and express their
thoughts and feelings without talking or moving. Although BCI
applications are still very limited, the focus of BCI has expanded
over the recent years from helping diagnose mental disorders to
enhance human life in different aspects. For instance, BCI can
provide people with severe motor disabilities a new way to control
augmentative technologies, and help others relief, relax and
meditate. Since BCIs have access to the brain signals and can
estimate the human emotions, the interaction between human and
machine can be improved and adapting to the human preference
[16].
In this paper, we present the Brain-EE system, which can predict
engaged enjoyment when performing different tasks. We start
this paper with a short background of EEG signals used in BCI
systems (section 2). In section 3, we share related work of BCI
systems designed for daily life use. A detailed description of the
Brain-EE system is included in section 4. In section 5 and 6, we
discuss our findings and outline future steps for the Brain-EE
system.
2.!BACKGROUND OF EEG
Electroencephalogram (EEG) is a measure of the electrical
activity that typically occurs around the scalp as a result of brain
activity (i.e., cortical activity). There are five EEG frequencies
that are usually denoted in research: delta (1-4 Hz), theta (5-8 Hz),
alpha (9-13 Hz), beta (12-30 Hz), and gamma (30-50Hz) [18].
EEG can typically be seen in neuroscientific research, where
different activity patterns can reflect several types of phenomena.
For example, EEG is one of the most common tools used in the
study of sleep [2]. EEG studies often serve as important tools in
memory research [6]. Many EEG studies are done in conjunction
with other brain imaging techniques (like functional magnetic
resonance imaging, or fMRI) to identify the presence of abnormal
brain activity, such as with seizures or stroke [18] [19]. Some
studies use EEG to measure mood states; for instance, it has been
shown that increased cortical alpha and beta activity can reflect
stress during activities [1] [12] [13]. Also, frontal alpha activity
and increased theta correlations, but no change in frontal beta
activity, has been connected to measuring engaged enjoyment
during different types of activities [7] [9] [17].
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DOI: http://dx.doi.org/10.1145/2910674.2910691
Though EEG seems to have considerable versatility in helping
people understand how their brain activity may correlate with
certain states or activities, formal EEG methods in research
laboratories can be expensive and difficult to set up. For example,
formal EEG laboratories often use EEG caps that have many
recording sites (i.e., electrodes) that require the placement of gel
or paste on the scalp in order to ensure good connectivity for clean
recordings. Researchers require advanced knowledge of statistical
and practical techniques for running experimental studies. Data
analysis may need complex methods and special computing
requirements. These studies also often take a long amount of time
as they move from early stages (preparation, IRB review) to later
stages (data collection/screening/cleaning/analysis and formal
write-up). Finally, as with any experimental study, applicability
to everyday life is limited; the environment of the formal
laboratory often does not reflect the environment in which people
live their lives.
Lately, the consumer market has attempted to make use of the
great advances discovered using EEG in the last couple of decades
for a variety of uses. As such, EEG may be a useful tool for
measuring several types of user states, and current
experimentation and methodology are allowing for the expansion
of EEG use outside of the laboratory and into everyday life.
3.!LIFESTYLE EEG INSTRUMENTS AND
APPLICATIONS
The advances of sensor technology have led new cost- and power-
efficient devices to the market, making them available for a larger
population of developers and researchers. Nowadays, many
portable EEG devices are consumer-grade, low-cost devices that
are targeted for lifestyle applications. These products also often
rebrand EEG data with a simpler, easily-understood term:
neurofeedback, or NFB.
There are many companies that produce these low-cost NFB
devices. Vendors like InteraXon, NeuroSky, Emotiv, Melon, and
Versus provide some of these off-the-shelf, inexpensive devices
for consumers. The number of electrodes on these devices is
limited (i.e., 2-14 electrodes) compared to the clinical grade
devices (i.e., 16-32), their resolution is low, and the electrodes are
usually focused on a specific portion of the brain; however, these
devices are still appropriate for specific applications.
Many of these portable EEG devices are simple to set up; they
connect via Bluetooth to a smartphone, a computer, or a
microcontroller, where data can be analyzed directly. They use
dry electrodes that do not require intensive preparation or clean-
up, and these electrodes connect to the skin without the need for
any gel or paste. These portable, cost-effective devices may also
be used with related available EEG tools and open-source
platforms (e.g., OpenEEG, BCI2000, OpenViBE, and MuLES).
These changes have helped evolve EEG applications in both novel
and established fields [3]. For example, [10] has designed a
custom-made driver drowsiness detection system that uses an
open-source hardware platform with dry electrodes to send
drowsiness level to the driver’s smartwatch, and alert him/her
when it is unsafe to drive. As such, these changes allow vendors
to provide research tools that make their devices applicable to
newer, lesser explored methods [5].
One of these uses is detecting the psychological human state,
including but not limited to: happiness, enjoyment, pain, or
concentration. In [8], the Muse headband, from InteraXon, was
used to detect cold-induced pain using self-calibrating protocols
and various classification algorithms. Similarly, [11] has shown
the possibility of using the Muse headband to measure
concentration and relaxation.
Necomimi Brainwave Cat Ears, from NeuroSky, is a toy that uses
EEG signals from the frontal lobe of the brain to present four
mind states in real-time including: high relaxation,
focus/relaxation, high focus, and high interest [15]. The pose of
the friendly-looking ears on the headband changes according to
the current state of mind. Also, [14] mimics the concept of “light
bulb/idea” metaphor: mounted on the helmet is a light bulb that
illuminates when the sensors detect an increase in thinking and
focus.
Given that human emotions are traditionally qualitative and are
very difficult to estimate, these devices can supplement other
measures of emotions, like self-assessment (e.g., through Likert or
dichotomous scales) or psychological examinations. In [4],
researchers used activities that tested attention and executive
functioning (i.e., the Stroop test [21] and the Towers of Hanoi
[20]) as well as a questionnaire to assess the suitability of the
NeuroSky headset for measuring meditation and attention levels.
Further, in [17], game boredom and flow were estimated in
personalized training using Support Vector Machine (SVM)
analysis while playing different levels of Tetris games.
Based on this new line of research, we introduce an idea to use
one such consumer-grade EEG device (Muse) to measure user
enjoyment. Our system, the Brain-EE system, aims to measure
engaged enjoyment in such a way that we can predict participant’s
preferences for activities based on their EEG data. In the next
section, we explain the methods we used to evaluate user
enjoyment.
4.!SYSTEM DESCRIPTION
The Brain-EE system aims to measure participant enjoyment
through EEG activity as it differs during two games. To measure
EEG, Brain-EE uses the commercially available product, Muse.
The two games that were played by the users, and for which users
would rate their enjoyment, were Piano Tiles 2 and Soccer 2016.
Both games were viewed and played in tablet platform. To create
the user interface and to analyze the data, we used MATLAB.
Figure 1 gives a general overview of the system setup. The
following subsections go into greater detail about our methods.
Figure 1: System Overview
4.1!Participants
Fifteen total healthy participants were used in this study. All were
University of Texas at Arlington students (13 males, 2 female)
aged 20-35.
4.2!Equipment and Software
4.2.1!Hardware
The EEG device used in this experiment is the Muse headband
from InteraXan. It is an off-the shelf, low-cost EEG headband,
and it has been used in multiple studies [3, 5, 8, 11, 14]. It has
seven EEG electrodes (4 Channels and 3 References) (please refer
to Figure 2) and 3-axis accelerometer. The sample rate of the used
EEG signals is 10 Hz, whereas the sample rate of the
accelerometer is 50 Hz. All data are transmitted via Bluetooth to
a laptop running Mac OS Yosemite 10.10.5. Also, for this study,
Relax Melodies were used during baseline data collection
(“Relax” phase), and the games used in testing (“Game 1” and
“Game 2” phases) were run on Asus tablet running Android OS
4.0 .
4.2.2!Software
For our experiment, we used two different games that were
comparable in skill level (i.e., using a touchpad screen to
complete the game) but were polarizing in likability; specifically,
we used Soccer 2016 (less likability) and Piano Tiles 2 (more
likability). With the Muse headband, InteraXon provides Muse
SDK (i.e., Muse IO, MusePlayer, MuseLab). In our experiment,
we take advantage of MuseIO, which connects with Muse via
Bluetooth and acquires EEG data, accelerometer data, and other
data (e.g., jaw clenches, eye blinks). The MuseIO streams these
data back to a specific port on the PC in the form of OSC (open
sound control) messages. To retrieve the data, we used
MATLAB’s Instrumentation Control Toolbox, which lets
MATLAB connect directly to that specified port using a TCP/IP
protocol. Once the data are received in MATLAB, our MATLAB
GUI application filters, processes, and analyzes the data.
4.3!Interactive User Interface
Our interface is user-friendly, clear, and easy to navigate. First,
the GUI provides step-by-step instruction for proper completion
of setup and data collection. Also, it plots real-time graphs for
accelerometer data to check if data is fetched in real time, as well
as the “is_good” signals provided by Muse that show whether the
EEG sensors on the headband are well-connected to the
participant’s head. In our experiment, data were collected in
intervals of 120 seconds, with each collection phase terminating
automatically. Data are analyzed using an algorithm (discussed in
detail Section 4.6), and the final result of the analysis is shown
with a picture of the game that the participant is predicted to
prefer.
4.4!Procedure
In our experiment, after giving consent to participate in the study,
participants answered relevant demographic questions (e.g. age,
gender). Then, participants were fitted with the Muse headset.
Connectivity for all four channels (via “is_good” signals) was
displayed; proper connectivity was checked before proceeding to
the experimental phases. After proper connection with the Muse
was ensured, the baseline phase (“Relax”) was established.
During this time, participants were asked to close their eyes and
listen to calming white noise for 120 seconds, while their EEG
data were recorded. After this, participants were asked to fill out
a general mood survey. Next, the two active phases (“Game 1”
and “Game 2”) were established. During each of these phases, the
participants were asked to play one of two games (Soccer 2016 or
Piano Tiles 2) for 120 seconds, while their EEG data were
recorded. After Game 1 and Game 2 phases, participants were
asked to fill out general mood surveys and to rate the games. For a
walkthrough of the protocol, as well as an example of the user
interface, please refer to our video at
https://youtu.be/brFZ93Omq5U.
4.5!Data Collection
The EEG signals are obtained from the four Muse input
electrodes. Two input electrodes are located on the forehead, and
one input electrode above each ear as shown in blue in Figure 3.
Three reference electrodes are located in the middle between the
two input electrodes on the forehead as shown in green in Figure
3. These EEG signals are represented as the absolute band power
for the 5 standard frequencies commonly used in EEG
applications (delta, theta, alpha, beta, and gamma). Due to the
frequent disconnection of electrodes while moving the head, the
EEG signals are filtered based on the “is_good” variable provided
by the Muse. In case any electrode is not properly connected
during the experiment, all the recorded data during the period of
disconnection is omitted from analysis.
Figure 2: Muse headband.
Figure 3: Muse electrode locations by 10-
20 International Standards.
4.6!Classification Methods and Results
EEG data analyses, particularly those involved in experimental
literature, often require elaborate filtering and preprocessing
methods in order to make group comparisons (t-tests). However,
brain activity and structures can vary drastically from person to
person; this means that it can be hard to apply findings to the
general populations [8]. Our goal is to apply a classification
method that should work across these differences and for all
healthy people. Therefore, we did not use the actual EEG data
directly in our classification method. Instead, we used the t-test
results.
For each frequency band (e.g., alpha) we used the average of the 4
channels. By doing that, we had five arrays of frequency bands
data for each phase of the experiment (i.e., “Relax”, “Game 1”,
and “Game 2”). During analysis, we filtered out data that
reflected poor connectivity. Since this filtering process necessarily
varied by individuals, there would sometimes be unequal amounts
of data points for different phases of the experiment for different
participants. To ensure that we had an equal amount of data
points across phases and across participants, we omitted data
based on the start and end times of each of the phases of
experiments, and we ignored remainder data (for example, if the
Relax phase had 800 data points, and Game 1 had 1000 data
points, we would only use the first 800 data points of Game 1).
Then, we ran a t-test comparing Game 1 data and Relax phase
data, as well as a t-test comparing Game 2 data and Relax phase
data. By doing this, the data of each participant is simplified and
summarized in t-test results that represent the changes in EEG
between Relax vs. Game 1, as well as between Relax vs. Game 2
(one result each for delta, theta, alpha, beta, and gamma
frequencies, for each of the two comparisons).
Finally, the results of the t-tests were used to train a linear
regression to be able to predict which out of two options (Game 1
or Game 2) the participant engaged with more. After using the
data of the first 10 participants to train the linear regression, the
linear regression was run for the last 5 participants. Equation 1 is
the result of the linear regression, and it evaluates both of the
games based on the t-test results of all the frequency bands. The
game that yields a higher ! value is the game that is predicted to
be the game that the participant reports enjoying the most. When
this equation was tested on the last 5 participants, it resulted in
100% accuracy in identifying which games they preferred.
! " #$%$&'( # $%$()&* + $%$,'&- # $%$$.,/ + $%$$$01
# $%$$),2333333333333333333333333333333333333333333333333333333333456
4.7!Analysis of Results
In Section 2, we discussed that other studies have found that
frontal alpha activity and increased theta correlations, but no
change in frontal beta activity, has been connected to measuring
engaged enjoyment during different types of activities [7] [9] [17].
We find that our results in this experiment partly agrees with these
studies. Table 1 includes the coefficients of the linear regression
equation, and their multiplication with the average of t-tests for
each of the frequency bands for the training data that correspond
to either enjoying or not enjoying the game. In the calculation of
the coefficients of Equation 1, the ! in the linear regression is set
to 1 for the games that participants enjoyed and to 0 for the games
that the participants did not enjoy. With that in mind, we can see
that in the highlighted section of Table 1, where coefficients are
multiplied with the average t-test results of the participants, that
theta is significantly higher for the game that they enjoyed, and in
the algorithm, has a positive coefficient. This suggests that the
increase in theta reflects more enjoyment. Contrastingly, alpha
and delta are higher for the game that they enjoyed, but in the
algorithm, they have negative coefficients. This contradictory
result suggests that alpha and delta are less related with the
prediction of enjoyment. Finally, though beta and gamma change
very slightly, their coefficients seem to have little impact in
predicting enjoyment. Despite the fact that these remarks may not
apply to all participants, they hold potential for a significant
influence on enjoyment research.
Table 1: Summary of the t-test averages across all the
frequency bands.
t-test!of
Frequency
*
-
/
1
2
Coeff.(C)
-0.0136
0.0256
-0.0072
0.0009
-0.0032
Enjoy (E)
34.49
60.97
29.33
64.01
23.01
No Enjoy
(NE)
1.73
16.26
5.54
47.67
28.14
C×E
-0.4691
1.5608
-0.2112
0.0576
-0.0736
C×NE
-0.0235
0.4162
-0.0399
0.0429
-0.0900
“Coeff. (C)” represents the coefficients of the linear regression
equation. “Enjoy (E)” represents the average of t-tests of the
frequency bands corresponding to enjoying a game. “No Enjoy
(NE)” represents the average of t-tests of the frequency bands
corresponding to not enjoying a game. “C×E” and “C×NE”
represent the simple multiplication of the coefficients and the
corresponding t-test averages for enjoyment and no enjoyment,
respectively.
5.!DISCUSSION AND FUTURE WORK
Initial statistical analyses helped with the refinement of the Brain-
EE system. First, group data for rest vs. active conditions
matched what was expected: no changes in beta but increases in
frontal alpha (t(13774) = 27.22, p < .001) and theta (t(13774) =
62.69, p < .001) activity, with theta being the best predictor of
whether users reported liking vs. not liking a game (χ2(3) =
4912.79, p < .001, accounting for 67.8% of the variance).
However, early variability of the results (especially with regarded
to alpha and beta), as well as a desire to look for individual data
differences rather than group differences, led to the development
of the algorithm. This early variability in the EEG data might
suggest differences in the magnitude of enjoyment. For instance,
while one participant reported “yes” when asked if he liked the
game, he rated the game a 6/10, which may suggest he did not
strongly like the game. In the development stages of Brain-EE,
the purpose of the study was to detect enjoyment, rather than to
evaluate the participant’s preference between two tasks.
However, when the same method (equation 1) was used to detect
enjoyment, the accuracy of the results hovered around 60%. One
of the reason behind this low accuracy might be that participants
sometimes have difficulties deciding whether they like a task or
not, but it is easier for them to decide which of two tasks they
prefer. Also, further testing can include analysis of questionnaire
responses in conjunction with EEG data to see how user
perceptions correlate with their brain activity. In particular, data
can be grouped based on demographic information; for instance,
further research can see how more experienced gamers might
show a different trend in engagement and preference than less
experienced gamers.
Future work includes expanding the usability of the Brain-EE
system for other purposes. For example, the Brain-EE system can
be utilized in usability testing to see how participants may be
more engaged with different web designs, therapy games, movies,
or advertisements. Finally, we plan to provide the data and the
Brain-EE system as an open-source dataset to be used and
evaluated by other researchers.
6.!CONCLUSION
Currently, the Brain-EE system is able to measure EEG activity
that reflects how much a user is engaged in a given task, which
then allows for the comparison of which out of two given tasks
are preferred by the user. The Brain-EE system supported much
of the previous literature on enjoyment; increases in frontal theta
activity strongly and reliably predicted which game each
individual participant preferred. The Brain-EE system can be
adapted to measure user engagement and enjoyment for other
stimuli (e.g., usability testing for different products), or to
measure other mood states (e.g., stress). It is the Brain-EE team’s
aim to refine our system with the hopes of increasing adaptability
for a wide array of future uses, thereby giving more objective and
concrete methods of measuring subjective phenomena.
7.!ACKNOWLEDGMENTS
This work is supported in part by the National Science Foundation
under award number 1041637. Any opinions, findings, and
conclusions or recommendations expressed in this publication are
those of the authors and do not necessarily reflect the views of the
National Science Foundation.
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