Content uploaded by Alfonso García-Monge
Author content
All content in this area was uploaded by Alfonso García-Monge on Apr 27, 2023
Content may be subject to copyright.
ID
: 114695
Recibido
: 2022-12-28
Revisado
: 2023-01-18
Aceptado
: 2023-02-23
OnlineFirst
: 2023-05-30
Publicación final: 2023-07-01
García-Monge, A., Rodríguez-Navarro, H., & Marbán, J. (2023).
Potentialities and limitations of the use of EEG devices in educational
contexts.
[Potencialidades y limitaciones de la usabilidad de dispositivos
EEG en contextos educativos]. Comunicar, 76
.
https://doi.org/10.3916/C76-2023-04
Potentialities and limitations of the use of EEG devices in educational
contexts
Potencialidades y limitaciones de la usabilidad de dispositivos EEG
en contextos educativos
Dr. Alfonso García-Monge
Associate Professor, Area of Physical Education, University of Valladolid (Spain)
ORCID: https://orcid.org/0000-0002-4535-5628
EMAIL: alfonso.garcia.monge@uva.es
Dr. Henar Rodríguez-Navarro
Associate Professor, Departament Pedagogy, University of Valladolid (Spain)
ORCID: https://orcid.org/0000-0001-6993-8441
EMAIL: henar.rodriguez@uva.es
Dra. José María Marbán-Prieto
Associate Professor, Department of Didactics of Mathematics, University of Valladolid (Spain)
ORCID: https://orcid.org/0000-0002-6561-6784
EMAIL: josemaria.marban@uva.es
Abstract
Wireless electroencephalography (EEG) devices allow for recordings in contexts outside the laboratory. However, many
details must be considered for their use. In this research, using a case study with a group of third-grade primary school
students, we aimed to show some of the potentialities and limitations of research with these devices in educational settings.
Several balances are apparent in the development of these experiences: between the interests and possibilities of the
research teams and the educational communities; between the distortion of life in the classrooms and the opportunities
for collaboration between academia and practice; and between the budget and the ease of preparing the equipment and
the usefulness of the collected data. Among their potentialities are the knowledge that they allow access to different
cognitive and emotional processes, and the learning opportunity represented by the links between researchers and
educational communities. Life in the classrooms is interrupted by these types of experiences, but this can be a cost that
facilitates more integrated future developments that benefit teaching and learning processes.
Resumen
Los nuevos dispositivos de electroencefalografía (EEG) inalámbricos permiten realizar registros en contextos fuera del
laboratorio. Sin embargo, para su utilización hay que tener en cuenta Very distractings detalles. En este trabajo, a partir
de un estudio de caso instrumental con un grupo de escolares de tercer curso de Educación Primaria, se pretende mostrar
algunas potencialidades y limitaciones de la investigación con estos dispositivos en contextos educativos. Se aprecian
varios equilibrios en el desarrollo de estas experiencias: entre los intereses y posibilidades de los equipos de investigación
y las comunidades educativas; entre la distorsión de la vida en las aulas y las oportunidades de la colaboración entre la
academia y la práctica; y entre el presupuesto y la facilidad de preparación de los equipos y la utilidad de los datos
recogidos. Entre sus potencialidades encontramos el conocimiento al que permiten acceder sobre diferentes procesos
cognitivos y emocionales, y la oportunidad de aprendizaje que suponen los nexos entre investigadores y comunidades
educativas. La vida en las aulas se ve interrumpida por este tipo de experiencias, pero ello puede suponer un coste que
facilite desarrollos futuros más integrados que beneficien los procesos de enseñanza y aprendizaje.
Palabras clave / Keywords
Neuroeducación, electroencefalografía, mediciones neurofisiológicas, educación primaria, contexto educativo, estudio de
caso.
Neuroeducation, electroencephalography, neurophysiological measurements in education, primary education, elementary
school, educational contex, case study.
1. Introduction
The development of new portable devices for recording electroencephalographic signals (EEG) has opened
up the possibility of transferring studies on brain activity from laboratory conditions to real contexts.
Considering the situated and culturally constructed nature of learning (Brown et al., 1989), the interest in
studying brain functioning in everyday educational contexts is understandable. To study the complexity of
cognitive processes requires environments in which the maximum number of variables can be isolated.
However, laboratory recordings limit the extrapolation of results concerning more natural conditions (Shamay-
Tsoory & Mendelsohn, 2019).
The potential of these devices, the incipient studies in educational, commercial, or artistic settings, or the very
publicity of the companies that commercialize them have generated many expectations in researchers and
educators, but, as Xu et al. (2022) point out, the feasibility of using these methods with schoolchildren, including
the technical and pragmatic challenges associated with data quality, have not been sufficiently addressed. The
need to deploy "real-world neuroscience" (Matusz et al., 2019; Shamay-Tsoory & Mendelsohn, 2019) clashes
with limitations when developing studies in educational settings (Janssen et al., 2021).
In order to analyze the potential and limitations of using this technology in school contexts, we propose this
instrumental case study whose results may be helpful to researchers or educators who have considered the
use of EEG in their investigations or as support for their educational interventions.
1.1. Key concepts
We will briefly review some concepts about EEG. There are different types of sensors to pick up polarity
changes (Hajare & Kadam, 2021). Not all of them are equally accurate. Basically, we find wet electrodes that
need some electrolytic substance to facilitate conductivity (gel or saline solution), those whose sensors pick
up the signal without preparation (dry electrodes), and dry electrodes whose reception is facilitated by a
solution (semi-dry). Wet electrodes have better signal quality (Lau-Zhu et al., 2019), but if the collection time
is prolonged, they can be affected by sensor dehydration. In general, dry electrodes reduce device placement
times, but artifacts may affect their signal more (Shad et al., 2020).
The aim with the positioning of each electrode is to collect information on activity in the area of location, so the
more electrodes placed on the scalp, the more detailed information can be obtained from a greater number of
areas. The quality of the collected signal also depends on the sampling rate of the device (number of samples
collected from a continuous signal in one second). A low sampling rate will retain many fragments of the emitted
signal and make it easier to study.
The information provided by the EEG signal can be studied in different ways. Time-domain studies can be
performed under laboratory conditions with control of the stimuli presented. In situations where a prolonged
measurement is made, a quantitative analysis of some wave characteristics would be appropriate. One form
of quantification comes from studying spectral power characteristics in different frequency bands with
functional significance (Basar et al., 1999). Changes in the power spectrum of these bands (delta, theta, alpha,
beta, and gamma) would serve as neuromarkers of specific brain activities. These correlations between certain
characteristics of the frequency spectra and different cognitive or emotional processes and states would allow
guiding the analysis of the data obtained in real educational contexts. Some examples are presented in Table
1.
Table 1. Neural correlates of some mental processes
State
Neuromarkers
Example of studies
Attention
Increased beta and gamma
frequencies; decreased alpha
frequencies
Grammer et al. (2021)
Approach or rejection
Frontal alpha asymmetry
Coan & Allen (2004)
Emotional activation
(BetaF3+BetaF4)/(AlfaF3/AlfaF4)
McMahan et al. (2015)
Cognitive load
Theta/alfa ratio
Antonenko et al. (2010)
Engagement
Beta/theta+alpha ratio
Pope et al. (1995)
It is essential to understand that these markers indicate correlation, not causation. They are not precise
markers that identify underlying brain processes. Each neuromarker has been obtained with a particular
population, a particular type of recording, or specific processing and feature extraction. Varying any parameter
(e.g., equipment with which EEGs are collected, type of experimental situation or context, ages of participants,
type of pre-processing...) may affect the meanings of these markers.
1.2. EEG in educational contexts
The accessibility of low-cost EEG devices has meant that some studies have appeared in the last decade
investigating their application in different educational contexts. Compared to fields such as marketing or video
games, research is still limited (Xu & Zhong, 2018). Table 2 presents an overview of recent studies.
Table 2. EEG studies in school contexts
Authors
Topic
Sample
Device
Signal processing
Dikker
et al. (2017)
Teacher-student and
student-student brain
synchrony
12 students (age: 16-18)
Simultaneous record.
Naturalistic situation
EEG 14
channels
(128Hz)
Continuous signal
segmentation. Calculation of
spectral coherence between
channels (minimum of 30
segments) and between
participants (in 6 channels).
Bevilacqua
et al. (2019)
Teacher-student and
student-student brain
synchrony
12 students (age: 16-18)
Simultaneous record.
Naturalistic situation.
EEG 14
channels
(128Hz)
Continuous signal
segmentation. Calculation of
spectral coherence between
channels (minimum of 30
segments) and between
participants (in 6 channels).
Khedher
et al. (2019)
Assessment of
engagement and
cognitive load.
15 university students
Individual record. Semi-
naturalistic situation.
EEG 14
channels
(128Hz)
Continuous signal
segmentation. Power spectral
density (PSD) calculation.
Application of the ratio
beta/theta+alfa.
Dikker
et al. (2020)
Variations in alpha
power and peak alpha
throughout the school
day.
22 students (age: 17-18)
Simultaneous record.
Naturalistic situation.
EEG 14
channels
(128Hz)
Segmentation of continuous
data (occipital channels). Alpha
power spectra and individual
alpha frequency peaks.
Grammer
et al. (2021)
Variations in different
frequencies according to
attention states before
different instructional
activities (lecture,
videos, discussion...)
23 university students
Individual record. Semi-
naturalistic situation.
EEG 24
channels
(250Hz)
Power of different frequencies
of a segmented continuous
signal.
Vekety
et al. (2022)
Improvement of
mindfulness and
executive functions
through a
neurofeedback program.
31 shoolchildren (age:8-
12)
Individual record. Semi-
naturalistic situation.
EEG 4
channels
(250Hz)
Using EEG with a feedback app
for relaxation.
Xu et al.
(2022) Attention analysis
46 shoolchildren (age:6-
7)
Records in trios.
Semi-naturalistic
situation.
EEG 24
channels
(250Hz)
Analysis of alpha frequency
spectral density.
Matusz et al. (2019) suggest three categories to define research approaches concerning the degree of
"naturalism": controlled laboratory, partially naturalistic laboratory, and naturalistic research. Accordingly, it is
apparent from the review by Xu and Zhong (2018) that fully naturalistic works still need to be made available.
Few studies integrate EEG technology into the regular classroom setting by simultaneously placing devices
on all participants. Studies with university students are dominant. The number of studies with schoolchildren
is minimal. Sample sizes are small. Many studies use low-cost devices with less than five dry sensors, which
limits the reliability of the data. The high cost of quality devices, their lengthy preparation, or the accessibility
of samples with child populations could be some of the explanations for the limitations shown in this current
picture.
This case study explores these aspects, analyzing the potential and limitations of using EEG devices in school
contexts.
2. Material and method
Since the purpose of this study was to analyze the potential and limitations of the use of this technology in
school contexts, to inform researchers or educators who have considered the use of EEG in their studies, or
as support for their educational interventions, an instrumental case study was chosen (Stake, 2010). From
Stake's constructivist ontology, the methods are inductive and flexible, discovery and interpretation co-occur,
the starting point is flexible initial conceptual frameworks, and the objective is understanding the phenomenon
through interpretation and decreasing the distance between researchers and participants. The process
followed is summarized in Figure 1.
Figure 1. Methodological process
The following issues (tensions) were taken as a starting point:
- Ease of use/quality of the recording.
- Potential of the data/possibilities and limitations of application in an educational context.
Based on these issues, the following guiding questions were posed:
- What pre-intervention aspects need to be taken into account?
- What are the advantages and disadvantages of each type of device?
- How do participants experience these interventions?
- What problems are posed by using these devices in educational contexts?
- What are the ethical implications of these investigations?
2.1. Context and participants
The study was conducted with a group of 17 third-grade primary schoolchildren aged between 8 and 9 years
(10 girls and 7 boys) in a regular school with which the researchers collaborate. The schoolchildren knew part
of the research team since they participated in weekly activities with them.
The design of the school tasks on which the recordings were made was agreed upon with the group's teachers,
considering their concerns. In order to reduce the impact on the students when introducing these devices in
the classroom and to take the opportunity for the students to understand better neuronal functioning, a week
before the start of the study, a workshop was held in which the group was introduced to different aspects of
brain activity, using playful neurofeedback devices. The study was carried out with the permission of the
school, the informed consent of the families, and the approval of the University Ethics Committee.
2.2. Procedure
Given the limited number of devices (three of each type) and to avoid disrupting the normal development of
the classes, a semi-naturalistic intervention was chosen (Matusz et al., 2019). A classroom adjacent to the
group's regular classroom was used to develop the study. The students were summoned to the room in trios
or pairs. While the different devices were placed, the students were reminded of some details about brain
activity collection through EEG, as explained in the previous workshop.
After placing the devices and confirming the correct reception of the signals, two baseline recordings were
made (2' with eyes closed and 2' with eyes open looking at a point in the central part of a blank sheet of paper).
From the baseline recording the electroencephalographic activity of the students listening to different
explanations about mathematical applications and performing some arithmetic tasks was collected. After using
each device, the students were asked to answer questions about the comfort of the device, discomfort in its
preparation, and interference in attending to, or performing the proposed tasks. The devices were placed
randomly with each group of participants to avoid possible fatigue accumulation effects. The average recording
duration with each group was 48'.
2.3. Instruments
Four EEG devices (three units of each) were used to compare their possibilities and limitations in a school
context:
- Brainlink Pro: headband with two dry electrode contact sensors in the frontal area. The sampling rate is 512
Hz. Bluetooth sends the signal to the computer, which is collected thanks to Lucid Scribe software, which can
be exported as a CSV file for pre-processing in EEGLab (Delorme & Makeig, 2004) or Medusa (Santamaría-
Vázquez et al.,2023).
- Emotiv Epoc: 14-channel EEG device with sensors requiring a saline solution to facilitate conduction and
with a sampling rate of 128 Hz. The sensors are mounted in fixed positions on a plastic structure. The signal
is sent wirelessly to the computer, which is collected by TestBench software, which can be exported as an
EDF file for further pre-processing and analysis. It has been used in numerous investigations (Williams et al.,
2020a).
- Epoc Flex: 32 channels with passive Ag/AgCI sensors (EasyCap) mounted on a neoprene cap allowing a
choice of mounting positions. A gel provides conductivity. The sampling rate is 128Hz. The amplifier placed in
the cap wirelessly sends the signal to the computer. It is collected through an online application (Emotiv Pro)
from which the data can then be downloaded in CSV or EDF formats. Their validation is reported in the study
by Williams et al. (2020b).
- The Muse (InteraXon) device has 4 channels of dry contact electrodes that collect data with a sampling rate
of 250Hz from the frontal and temporoparietal areas. The signals, sent via Bluetooth, can be collected on a
Tablet using the Mind Monitor application, which can be exported as a CSV file for further processing. Some
research has validated it (Krigolson et al., 2017).
The assessments of the participating schoolchildren were collected through a questionnaire and informal
interviews. Following previous work (Zerafa et al., 2018), the questionnaire asked them about their sensations
during device placement (Preparation: "very long", "long", "good", "very good"); the comfort of the device
(Comfort: "very uncomfortable", "somewhat uncomfortable", "comfortable", "very comfortable") and the
possible interference of the device in the tasks performed (Distraction: "very distracting", "somewhat
distracting", "I have not noticed"). The responses to the questionnaire could be nuanced and expanded upon
through interviews. A research diary was also used to record aspects of the project design, tasks and
procedures, agreements with the school’s teaching staff, interviews, informal exchanges with teachers and
students, and critical incidents, difficulties, and details in the development of the experience.
2.4. Data analysis
EEG signals were compared before and after pre-processing with EEGLab. High-pass (0.5Hz) and low-pass
(45 Hz) IIR Butterworth filters were applied in the pre-processing. For the data obtained from the Muse device,
a 50Hz notch filter was also applied (Emotiv devices integrate this filter for electrical signal interference in the
electroencephalographic signal). The data were cleaned of artifacts with a first visual inspection, after which
an algorithm for artifact subspace reconstruction was applied to discard channels muted for more than 5
seconds or with high-frequency noise of more than 4 deviations. Next, the data were re-referenced by
computing the average reference (CAR). Finally, independent component analysis (ICA) was applied, and
components dominated by non-neural sources (artifacts) were discarded.
For the analysis of the qualitative sources, open coding (Glaser & Strauss, 2006) was carried out in Atlas.ti,
guided by the orienting questions and seeking the multi-referentiality of the data (among three researchers).
A first-order theoretical analysis was then carried out, constructing interpretations and translating descriptive
codes into theoretical categories (Shkedi, 2004), supported by contrasting the concepts with other authors
(Shkedi, 2004). Through comparison and contrast among codes and with theoretical categories from the
literature, the data were integrated into axial coding (Glaser & Strauss, 2006) from which the following
categories and topics emerged:
- Organizational aspects: previous contacts, impact reduction, familiarity with participants, researcher-teacher
collaboration, expectations and reluctance, project, permissions, disruption of school life, space and time, and
human resources.
- Equipment possibilities and limitations: adaptation to different sizes, preparation time, signal quality, and the
limited number of channels.
- Participants' perspective: students' expectations, feelings about the preparation, comfort, distractions from
the task due to the device, and teachers' expectations.
- Ethical implications: disruption/integration into school life, learning opportunity, data sensitivity, benefits.
3. Analysis and results
3.1. Organizational aspects
Since there is a change in the routine of the school with new people and equipment, maintaining prior contact
is recommended. In the case studied, as commented by the teachers involved, the history of collaboration
between the researchers and the school facilitated the latter's openness to new proposals. In particular, a
proposal such as this one for using EEG in the classroom can generate curiosity and reticence. It generates a
favorable disposition in most of the teaching staff, in most families, and in the student body. However, placing
devices associated with electricity, pathology, or "access to the mind" creates misgivings among some
teachers and families. For this reason, the project needs to be explained in detail. The teachers recognized
that the trust generated in previous collaborations facilitated openness to this explanation.
Obtaining permissions from the school and families takes time and requires, as mentioned, a detailed and
pedagogical explanation of the project. In this case, some families did not give their permission. This would be
a problem in the case of fully naturalistic projects implementing this technology in conventional classrooms.
This type of experience involves harmonizing schedules and spaces. School agendas are usually tight, and it
is difficult to find time when some students can leave the regular classroom for another activity. In addition,
there is a free space available in the schools only sometimes during data collection. In this case, the experience
was postponed for almost two months until a suitable week was found.
For ethical and practical reasons, it is interesting to agree with the school's teachers in the design of the
experience, considering their concerns and the educational program. This way, the proposal will be better
adjusted to the students and the teacher's programming. The arrival of between 4 and 8 researchers at the
school also impacts school life.
The experience generates many expectations in the students. In the weekly meetings during the previous
months, the students, enthusiastic, constantly asked us about the moment of "putting on the caps" and "reading
their thoughts". Some asked with amusement if they were going to get electricity. We interpret that familiarity
with the students allows them to open up and share their concerns. This familiarity of the students with some
of the researchers also had repercussions on the state of the children when they took the tests. Schoolchildren
say that trust makes them feel more at ease during the installation of the equipment or the development of the
tests. This is important when, for example, we want to analyze anxiety states when faced with the proposed
school tasks. The tension generated by the discomfort of the experimental situation could distort the data
obtained. The students recognized that the previous workshop with playful activities on brain activity helped to
generate a favorable disposition towards the devices, interest in the topic, to reassure them about the
"experiments", and to get to know and approach the research team (9 researchers who would later participate
in the data collection took part in the workshop). The students said the workshop allowed them to understand
details about topics they had studied (functioning of the nervous system and neuronal activity).
The use of several EEG devices simultaneously involves a lot of human resources. At least one person for
each device to install it, synchronize it with the computer receiving the signal, ensure a good connection
throughout the test, and record and save the different parts of the experiment (the preparation of a device with
more channels is facilitated by the intervention of two people). In addition, it is interesting to have another
person responsible for the questionnaires and the development of the different tests proposed to the students
and one responsible for taking note of possible incidents and recording the experience.
The schedules for the research are adapted to the availability of the students; therefore, the collection of
information is not continuous, and the members of the research team must spend hours at the school that they
can use to organize the information, clean the devices or prepare the new data collection. In any case,
combining the team's availability with the school's timetable is another limiting aspect.
3.2. Device possibilities and limitations
The devices used have a wide range of adaptability to varying head sizes and shapes. Brainlink has a mobile
strap system with easily adjustable Velcro. Hard plastic straps attach the Emotiv Epoc electrodes to the
device's body. These straps adapt to different head shapes, although it must be verified that the sensors make
contact with the same areas on all participants. With the Epoc Flex, caps of two sizes (size 50 and 54) were
used according to head size. The flexibility of the neoprene makes it fit perfectly. Muse has an adjustable
plastic headband that adapts to various head sizes.
The setup time varies mainly depending on the speed or problems with the connection between the EEG
device and the device receiving the signal. BrainLink setup time averaged 2'21" and the most extended delays
were due to pairing via Bluetooth with the computers receiving the signal. The average installation time of the
Emotiv Epoc was 7'02", and the main problems derived from contact with the scalp of the sensors in those
participants with curly or very thick hair. Problems were also caused by the sponges that facilitate contact
falling off or the sensor terminals unscrewing from their holders. Epoc Flex, with 32 channels with gel, had an
average preparation time of 10'15", but the longest delays were not due to the application of the gel (M=6'56"),
but to problems with the wireless connection or opening the online application for data collection. Finally, Muse
had an average preparation time of 2'45", especially due to finding good contact on the sensors behind the
ears.
Because the devices are wireless, care must be taken to establish the connection between the EEG and the
receiver by separating one device from the other so that recognition errors do not occur. The quality of the
recorded signal varies among the different devices. Table 3 shows a visual example of the signals before and
after pre-processing.
Table 3. Comparison of device signals
Device Example of devices
in context
Example of raw
signal
Example of
frequency
spectrum without
pre-processing
Example of pre-
processed signal
Example of
frecuency
spectrum after pre-
processing
Brainlink
Muse
EmotivEpoc
EpocFlex
The signals that record the highest number of artifacts (distortion of the signal by other sources such as
movement, blinking, pulsation, or the field generated by the electric current) are those of Muse. The placement
of its contact sensors on the forehead and behind the pinnae means they are greatly affected by blinking and
jaw contractions. Table 3 shows that their frequency spectrum is somewhat unusual, with ups and downs in
power and many differences among channels and high powers for high frequencies.
The case of Brainlink is similar. Its two front contact sensors are susceptible to blinking and facial movements.
Similarly, the frequency spectrum does not resemble the usual one for EEG waves.
The poor fixation of the Emotiv Epoc's sensors means that it picks up several periods of contact loss and is
very sensitive to head movements. Before pre-processing, the spectrum of its signal presents a picture closer
to the usual spectrum of an EEG signal. The Epoc Flex gives the best signal quality (less affected by artifacts).
Its better fixation to the head, and the better connectivity provided by the gel, result in fewer artifacts.
During pre-processing, the smallest number of signal segments that had to be deleted was in the Epoc Flex
recordings. This is important to subsequently segment the signal and perform different analyses of cortical
activity in the tasks proposed in the class. By applying an automatic artifact rejection method (Artifact subspace
reconstruction, ASR), several BrainLink, Muse, and Emotiv Epoc device channels were automatically
suppressed. Since these are devices with few channels, suppressing any of them cannot be compensated for
by interpolating the measurements in nearby channels, and the loss of information will prevent measurements
of different mental processes.
Using EEGLab's ICALabel tool (which shows the probability that a component captures brain activity or other
artifacts), it was found that components of recordings with BrainLink, Muse, and Emotiv Epoc were strongly
affected by muscle sources.
An inverse relationship between preparation time and the number of electrodes was observed, but limiting the
number of electrodes has consequences: fewer channels with fixed positions will not allow access to many
neural correlates, and source recognition is less feasible or accurate.
3.3. Participants’ perspective
As mentioned above, the students' expectations were very high, and after the experience, they all wanted to
repeat it. This attitude is relevant to understanding that they likely tended to value the devices positively. Table
4 shows the results of their responses to the questionnaires on their feelings about the preparation, the comfort
of the devices, and the possible distractions they generated during the tasks. The table shows the number of
responses given and the average scores.
Table 4. Results of the questionnaire on sensations with the devices
BrainLink
Mean
SD
Preparation
Very long: 0
Long: 0
Good: 6
Very Good: 11
3,647
0,477
Comfort
Very uncomfortable: 0
Somewhat uncomfortable: 5
Comfortable: 3
Very combortable: 9
3,235
0,876
Distraction
Very distracting: 0
Somewhat distracting: 4
Not noticed: 12
2,75
0,433
Emotiv Epoc
Preparation
Very long: 1
Long: 3
Good: 4
Very Good: 9
3,235
0,94
Comfort
Very uncomfortable: 0
Somewhat uncomfortable:3
Comfortable: 7
Very Comfortable: 7
3,235
0,729
Distraction
Very distracting: 0
Somewhat distracting: 5
Not noticed: 12
2,705
0,455
Epoc Flex
Preparation
Very long: 2
Long: 3
Good: 1
Very Good: 11
3,235
1,112
Comfort
Very uncomfortable: 0
Somewhat uncomfortable: 1
Comfortable: 2
Very Comfortable: 14
3,764
0,545
Distraction
Very distracting:0
Somewhat distracting: 2
Not noticed: 15
2,882
0,322
Muse
Preparation
Very long: 0
Long: 1
Good: 2
Very Good: 14
3,764
0,545
Comfort
Very uncomfortable: 0
Somewhat uncomfortable: 2
Comfortable: 1
Very Comfortable: 14
3,705
0,665
Distraction
Very distracting: 0
Somewhat distracting: 2
Not noticed: 15
2,882
0,322
The students, in this case, waited patiently for the preparations. In some cases where the computer
connections failed, or the online application failed to open, and the process took longer, they commented that
they had become a little bored. With the Emotiv Epoc, the problems in the connection of some electrodes in
girls with thicker hair and the repositioning of contact sponges or electrodes led four participants to evaluate
the preparation of this device as long (3) or very long (1). In the case of the Epoc Flex, the problems of wireless
recognition of the device, or errors in accessing the data recording platform, lengthened the process and led
some schoolchildren to rate it as very long (2) or long (3).
Regarding comfort, in general, the sensations were good. The discomfort recorded came from the pressure
on the forehead of the BrainLink sensors (5 cases); the pressure of some sensors in the temporal region of
the Emotiv Epoc (3); some itching behind the ears of Muse (2); and a particular sensation of rubbing under the
chin by the fixing tape of the Epoc Flex (1 case). To avoid discomfort from the gel residue used in the Epoc
Flex, the hair was cleaned with alcohol and brushed afterward. Few participants felt distracted from tasks by
the devices. In some cases, they commented that they were careful not to move the device (Muse and Emotiv
Epoc) or not to move themselves to avoid introducing "noise" into the signal.
As for teachers' expectations about these experiences, they expected to learn details about their students'
responses to different tasks and the neural processes underlying learning, as well as to corroborate their
opinions about each student. In some cases, they had higher expectations of what can be researched in
practice. Moreover, they saw that it involved too great a deployment of means and people to be integrated into
the classroom. They had doubts about the feasibility of integrating these devices into conventional classrooms.
3.4. Ethical issues
The above findings carry several ethical implications. This type of experience disrupts school life. To reduce
this possible disruptive effect, it is essential to integrate them into the program and plan them according to
teaching criteria. The tests should be brief to avoid student fatigue and should not interfere with other school
activities.
For students, it is an opportunity to get in touch with devices, procedures, and knowledge that are difficult to
access.
Data are sensitive, and ensuring their confidentiality and security is essential. We are dealing with children’s
biological signal data, and it is important to follow all the protocols for data protection.
We understand that the child's benefit is the basic criterion to guide these experiences. If the research results
can help teachers better orient their educational practice and the experience enriches the participants, the
disadvantages will have been compensated for. Hence, the designs of these experiences allow access to
relevant information for teachers and students.
4. Discussion and conclusions
This case study aimed to analyze the possibilities and limitations of using EEG devices in school contexts to
inform researchers or educators who have considered using EEG in their studies or as support for their
educational interventions. The development of these experiences involves the interests of teachers,
schoolchildren, families, and researchers, which requires collaboration and advancing interdisciplinary
research (Katzir & Paré-Blagoev, 2006). This connection between research and educational practice can help
scholars better understand school reality by refining their research questions (Liu & Zhang, 2021). To the
educational community, it can show the potential of brain research (Mason, 2009). This entails seeking
partnership models based on analyzing teacher, student, and family demands (Howard-Jones et al., 2016; Liu
& Zhang, 2021) that can better fit these experiences into educational programming. Likewise, it entails
relationships of mutual trust (Liu & Zhang, 2021) forged over time. In any case, the costs of deploying material
and human resources and adjusting schedules must be considered.
Regarding the devices, the results on the adaptability and comfort of the devices used align with previous
studies with other age groups (Zerafa et al., 2018). Similarly, previous studies warn of the sensitivity to the
movement of equipment such as the Emotiv Epoc, but not so for Muse's sensitivity to blinks or facial
movements (Krigolson et al., 2017). The quality of the recordings with the Epoc Flex align with previous studies
(Browarska et al., 2021). No references to delays caused by connectivity or access problems to the data
collection platforms were found.
The limitation of the number of electrodes in some devices is a problem for accurate source modeling (Akalin-
Acar & Makeig, 2013). It reduces the processes to be studied and the possible analyses (Lau-Zhu et al., 2019).
Looking at Table 1 about some possible neural correlates, it will be understood that with 2 or 4 channels, it is
difficult to analyze many cognitive processes. To generalize these correlates around an age group and to be
able to simplify the number of electrodes, a process would be needed in which, after a recording with a large
sample and a wide coverage of the scalp, signal classification could be performed (through machine or deep
learning) that would allow the development of applications that classify new signals from data generated by
devices with few sensors (Craik et al., 2019).
Beyond the organizational or technical aspects, we find the ethical implications. The potential of this work lies
in benefitting educators with a better understanding of the processes underlying their proposals and the effects
of their work, thus facilitating educational situations that are better adjusted to the characteristics and needs
of the students. However, as Rose and Abi-Rached (2014) explain, we must not lose sight of the fact that
emerging neurotechnologies increase the risk of using the brain as a "biopolitical resource", promoting
processes of optimization and competitiveness. Williamson (2018) also warns of the dangers of
"neurogovernance" that aspires to "scan" the brain in order to "sculpt" specific capabilities. The political
dimension of education is well known, and its objectives and implications should be considered in this type of
research.
The development of immersive experiences in education entails a series of tensions that must be carefully
navigated. These tensions involve striking a balance between the interests and possibilities of research teams
and educational communities, as well as between the potential distortion of classroom life and the opportunities
for collaboration between academia and practice. In addition, there is a need to balance the budget and ease
of preparation of research teams with the usefulness of the data collected.
Currently, the extension of these experiences to entirely naturalistic settings is limited by the costs of necessary
devices and human resources. However, ongoing efforts to expand the scope of these experiences hold
promise for generating a robust body of knowledge that can inform future applications. As sensors continue to
improve and device costs potentially decrease, it may be possible to broaden the reach of these experiences
for the benefit of education.
Authors’ contribution
Idea, A.G-M.; Literature review, A.G-M., H.R-N.; Methodology, A.G-M., H.R-N., J.M.M-P.; Data analysis, A.G-M., H.R-N.,
J.M.M-P.; Results, A.G-M., H.R-N., J.M.M-P.; Discussion and conclusions, A.G-M., H.R-N-N., J.M.M-P.; Editorial (original
draft), A.G-M., H.R-N., J.M.M-P.; Final revisions, A.G-M.; Project design and sponsorships, A.G-M., H.R-N., J.M.M-P.
Institutional support
Department of Didactics of Musical, Plastic and Corporal Expression (University of Valladolid), Department of Pedagogy
(University of Valladolid), Department of Didactics of Experimental Sciences, Social Sciences and Mathematics (University
of Valladolid).
References
Akalin-Acar, Z., & Makeig, S. (2013). Effects of forward model errors on EEG source localization. Brain topography,
26(3), 378-396. https://doi.org/10.1007/s10548-012-0274-6
Antonenko, P., Paas, F., Grabner, R., & Van-Gog, T. (2010). Using electroencephalography to measure cognitive load.
Educational Psychology Review, 22(4), 425-438. https://doi.org/10.1007/s10648-010-9130-y
Basar, E., Basar-Eroglu, C., Karakas, S., & Schürmann, M. (1999). Oscillatory brain theory: A new trend in neuroscience.
IEEE engineering in medicine and biology magazine: the quarterly magazine of the Engineering in Medicine &
Biology Society, 18(3), 56-66. https://doi.org/10.1109/51.765190
Bevilacqua, D., Davidesco, I., Wan, L., Chaloner, K., Rowland, J., Ding, M., Poeppel, D., & Dikker, S. (2019). Brain-to-
brain synchrony and learning outcomes vary by student-teacher dynamics: evidence from a real-world classroom
electroencephalography study. Journal of Cognitive Neuroscience, 31(3), 401-11.
https://doi.org/10.1162/jocn_a_01274
Browarska, N., Kawala-Sterniuk, A., Zygarlicki, J., Podpora, M., Pelc, M., Martinek, R., & Gorzelanczyk, E.J. (2021).
Comparison of smoothing filters' influence on quality of data recorded with the emotiv EPOC Flex brain-computer
interface headset during audio stimulation. Brain sciences, 11(1), 98. https://doi.org/10.3390/brainsci11010098
Brown, J.S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher,
18(1), 32-42. https://doi.org/10.3102/0013189X018001032
Coan, J.A., & Allen, J.J. (2004). Frontal EEG asymmetry as a moderator and mediator of emotion. Biological Psychology,
67(1-2), 7-50. https://doi.org/10.1016/j.biopsycho.2004.03.002
Craik, A., He, Y., & Contreras-Vidal, J.J. (2019). Deep learning for electroencephalogram (EEG) classification tasks: A
review. Journal of Neural Engineering, 16(3), 031001. https://doi.org/10.1088/1741-2552/ab0ab5
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including
independent component analysis. Journal of neuroscience methods, 134(1), 9–21.
https://doi.org/10.1016/j.jneumeth.2003.10.009
Dikker, S., Haegens, S., Bevilacqua, D., Davidesco, I., Wan, L., Kaggen, L., McClintock, J., Chaloner, K., Ding, M., West,
T., & Poeppel, D. (2020). Morning brain: Real-world neural evidence that high school class times matter. Social
Cognitive and Affective Neuroscience, 15(11), 1193-1202. https://doi.org/10.1093/scan/nsaa142
Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, J., Michalareas, G., Van Bavel, J.J.,
Ding, M., & Poeppel, D. (2017). Brain-to-brain synchrony tracks real-world dynamic group interactions in the
classroom. Current Biology, 27(9), 1375-80. https://doi.org/10.1016/j.cub.2017.04.002
Glaser, B., & Strauss, A. (2006). The discovery of grounded theory. Aldine Transaction.
Grammer, J.K., Xu, K., & Lenartowicz, A. (2021). Effects of context on the neural correlates of attention in a college
classroom. NPJ science of learning, 6(1), 15. https://doi.org/10.1038/s41539-021-00094-8
Hajare, R., & Kadam, S. (2021). Comparative study analysis of practical EEG sensors in medical diagnoses. Global
Transitions Proceedings, 2(2), 467-475. https://doi.org/10.1016/j.gltp.2021.08.009
Howard-Jones, P.A., Varma, S., Ansari, D., Butterworth, B., De Smedt, B., Goswami, U., Laurillard, D., & Thomas,
M.S.C. (2016). The principles and practices of educational neuroscience: Comment on Bowers (2016). Psychological
Review, 123(5), 620-627. https://doi.org/10.1037/rev0000036
Janssen, T.W.P., Grammer, J.K., Bleichner, M.G., Bulgarelli, C., Davidesco, I., Dikker, S., Jasińska, K.K., Siugzdaite, R.,
Vassena, E., Vatakis, A., Zion-Golumbic, E., & van Atteveldt, N. (2021). Opportunities and Limitations of Mobile
Neuroimaging Technologies in Educational Neuroscience. Mind, Brain and Education, 15(4), 354-370.
https://doi.org/10.1111/mbe.12302
Katzir, T., & Paré-Blagoev, J. (2006). Applying cognitive neuroscience research to education: The case of literacy.
Educational Psychologist, 41(1), 53-74. https://doi.org/10.1207/s15326985ep4101_6
Khedher, A.B., Jraidi, I., & Frasson, C. (2019). Tracking students’ mental engagement using EEG signals during an
interaction with a virtual learning environment. Journal of Intelligent Learning Systems and Applications, 11(1), 1-14.
https://doi.org/10.4236/jilsa.2019.111001
Krigolson, O.E., Williams, C.C., Norton, A., Hassall, C.D., & Colino, F.L. (2017). Choosing MUSE: Validation of a low-
cost, portable EEG system for ERP research. Frontiers in Neuroscience, 11, 109.
https://doi.org/10.3389/fnins.2017.00109
Lau-Zhu, A., Lau, M.P.H., & McLoughlin, G. (2019). Mobile EEG in research on neurodevelopmental disorders:
Opportunities and challenges. Developmental Cognitive Neuroscience, 36, 100635.
https://doi.org/10.1016/j.dcn.2019.100635
Liu, Y., & Zhang, Y. (2021). Developing sustaining authentic partnership between MBE researchers and local schools.
Mind, Brain, and Education, 15(2), 153-162. https://doi.org/10.1111/mbe.12280
Mason L. (2009). Bridging neuroscience and education: A two-way path is possible. Cortex, 45(4), 548-549.
https://doi.org/10.1016/j.cortex.2008.06.003
Matusz, P.J., Dikker, S., Huth, A.G., & Perrodin, C. (2019). Are we ready for real-world neuroscience? Journal of
Cognitive Neuroscience, 31(3), 327-338. https://doi.org/10.1162/jocn_e_01276
McMahan, T., Parberry, I., & Parsons, T.D. (2015). Evaluating player task engagement and arousal using
electroencephalography. Procedia Manufacturing, 3, 2303-2310. https://doi.org/10.1016/j.promfg.2015.07.376
Pope, A.T., Bogart, E.H., & Bartolome, D.S. (1995). Biocybernetic system evaluates indices of operator engagement in
automated task. Biological Psychology, 40(1-2), 187-195. https://doi.org/10.1016/0301-0511(95)05116-3
Rose, N., & Abi-Rached, J. (2014). Governing through the brain: Neuropolitics, neuroscience and subjectivity. The
Cambridge Journal of Anthropology, 32(1), 3-23. https://doi.org/10.3167/ca.2014.320102
Santamaría-Vázquez, E., Martínez-Cagigal, V., Marcos-Martínez, D., Rodríguez-González, V., Pérez-Velasco, S.,
Moreno-Calderón, S., & Hornero, R. (2023). MEDUSA©: A novel Python-based software ecosystem to accelerate
brain-computer interface and cognitive neuroscience research. Computer methods and programs in
biomedicine, 230, 107357. https://doi.org/10.1016/j.cmpb.2023.107357
Shad, E.H.T., Molinas, M., & Ytterdal, T. (2020). Impedance and noise of passive and active dry EEG electrodes: a
review. IEEE Sensors Journal, 20(24), 14565-14577. https://doi.org/10.1109/JSEN.2020.3012394
Shamay-Tsoory, S.G., & Mendelsohn, A. (2019). Real-life neuroscience: An ecological approach to brain and behavior
research. Perspectives on Psychological Science, 14(5), 841-859. https://doi.org/10.1177/1745691619856350
Shkedi, A. (2004). Second‐order theoretical analysis: A method for constructing theoretical explanation. International
Journal of Qualitative Studies in Education, 17(5), 627-646. https://doi.org/10.1080/0951839042000253630
Stake, R.E. (2010). Qualitative research: Studying how things work. Guilford Publications. https://bit.ly/3J0mmNf
Vekety, B., Logemann, A., & Takacs, Z.K. (2022). Mindfulness practice with a brain-sensing device improved cognitive
functioning of elementary school children: An exploratory pilot study. Brain Sciences, 12(1), 103.
https://doi.org/10.3390/brainsci12010103
Williams, N.S., McArthur, G.M., & Badcock, N.A. (2020a). 10 years of EPOC: A scoping review of Emotiv’s portable EEG
device. BioRxiv. https://doi.org/10.1101/2020.07.14.202085
Williams, N.S., McArthur, G.M., de-Wit, B., Ibrahim, G., & Badcock, N.A. (2020b). A validation of Emotiv EPOC Flex
saline for EEG and ERP research. PeerJ, 8, e9713. https://doi.org/10.7717/peerj.9713
Williamson, B. (2018). Brain data: Scanning, scraping and sculpting the plastic learning brain through neurotechnology.
Postdigital Science and Education, 1, 65-86. https://doi.org/10.1007/s42438-018-0008-5
Xu, K., Torgrimson, S.J., Torres, R., Lenartowicz, A., & Grammer, J.K. (2022). EEG data quality in real‐world settings:
Examining neural correlates of attention in school‐aged children. Mind, Brain, and Education, 16(3), 221-227.
https://doi.org.ponton.uva.es/10.1111/mbe.12314
Xu, J., & Zhong, B. (2018). Review on portable EEG technology in educational research. Computers in Human
Behavior, 81, 340-349. https://doi.org/10.1111/mbe.12314
Zerafa, R., Camilleri, T., Falzon, O., & Camilleri, K.P. (2018). A comparison of a broad range of EEG acquisition devices–
is there any difference for SSVEP BCIs? Brain-Computer Interfaces, 5(4), 121-131
https://doi.org/10.1080/2326263X.2018.1550710
