Biometric Evaluation to Measure Brain Activity and Users

Experience Using Electroencephalogram (EEG) Device

Alaa Alkhafaji

1,4 a

, Sanaz Fallahkhair

and Ella Haig

School of Engineering, Computing, and Mathematics, University of Plymouth, U.K.

School of Computing University of Brighton, U.K.

School of Computing, University of Portsmouth, U.K.

Department of Computer Science, College of Science, Mustansiriyah University, Iraq

Keywords: EEG, Biometric Data, HCI, User Experience, Field Study.

Abstract: This paper presents an empirical study in the field to obtain preliminary insights evaluating the mobile

application using an electroencephalogram (EEG) device (i.e. EMOTIV Insight headset). EMOTIV is a device

to be worn on the head that monitors brain activity to further analyse them into meaningful data that can

inform the results of measuring the users’ experience in terms of six cognitive metrics which are: stress,

engagement, interest, focus, excitement and relaxation. A mixed methods approach was used adopting

questionnaire, automated biometric data using EMOTIV and observations. The results suggest that the

biometric data obtained from this device are reliable to some extent, but it is important to be combined with

qualitative data using observational method in order to make sense of the results into different dimensions.

This would help researchers, who are seeking a way to measure internal user experience both subjectively

and objectively. Additionally, the results suggest that participants’ experience was positive when used a

mobile app to receive information regarding heritage places in the field. Moreover, several implications and

challenge are outlined.

1 INTRODUCTION

User experience is a very important element when it

comes to introducing a new technology to users

(Dibeklioğlu et al., 2021). It is essential to measure

their experience as accurately as possible for a better

insight regarding the investigated aspect (Hassenzahl

& Tractinsky, 2006). User experience would inform

developers about the quality of a new product and

whether it will be used or not (Paul & Komlodi, 2014).

Thus, researchers should make sure the results of

such studies are accurate enough to draw conclusions

that assist in taking any decision regarding the

product, whether positive or negative.

Measuring user experience traditionally is

done using self-reported techniques (Law et al., 2009;

Vermeeren et al., 2010), which could not be useful in

providing subjective measures (Galindo & García-

Canseco, 2015). Additionally, it might cause a lack of

accuracy of the results as people tend to forget

things after a short period of time. The slight

https://orcid.org/0000-0001-8625-886X

https://orcid.org/0000-0002-5617-1779

inaccuracy in research results could cause, to some

extent, unreliability of studies (Bai & Fuglerud,

2018). That could hinder the process of research and

trust in its results. In addition, traditional methods do

not measure aspects such as engagement, stress and

focus (McNamara & Kirakowski, 2006).

The recent emergence of electroencephalogram

(EEG) devices that monitor brain activity provides a

complimentary tool to support the traditional methods

that measure user experience. These could potentially

be more accurate and include aspects that are not well-

considered (e.g. engagement, interest and focus)

(Galindo & García-Canseco, 2015; Heunis, 2016). As

experience usually is in users’ mind (Attfield et al.,

2011), capturing these from users’ brain directly would

support researchers when conducting field studies for

this purpose. Additionally, in many cases we would

need to acquire objective measure to reduce

evaluator/researcher effect. Therefore, objective

measure for testing such as biometric methods (i.e.

EEG) could be used. The EEG devices enable us to

Alkhafaji, A., Fallahkhair, S. and Haig, E.

Biometric Evaluation to Measure Brain Activity and Users Experience Using Electroencephalogram (EEG) Device.

DOI: 10.5220/0011600100003417

In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 2: HUCAPP, pages

101-108

ISBN: 978-989-758-634-7; ISSN: 2184-4321

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

101

collect objective feedback about users and their

experience. However, as this is yet immature in the

literature, there is no clear insight to which extent these

electronic data are reliable. This paper presents an

empirical study to provide a preliminary insight of this

question while measuring user experience in the field

when using a mobile app for information acquisition

about a cultural heritage site. The next section gives a

brief overview of similar studies.

2 RELATED WORK

Few studies were conducted using automated

approaches to measure users’ experience. The eye-

tracking method is one approach that is used to

measure user experience by monitoring users’ gaze

while experiencing the use of a technological device

(Amadieu et al., 2015; Pel et al., 2010; Poole & Ball,

2006). However, this approach measures only the

aspect of where users look with the aim of identifying

what catches the participants’ eyes, but not

experiences such as “engagement”, “focus” and

“interest”. It was also utilized in robotics (Aguiar et

al., 2016; Chowdhury et al., 2014).

Another approach is capturing brain signals of

participants with the aim of measuring experience.

This approach is yet immature in the literature; very

few studies were conducted using such an approach

(Balart-Sánchez et al., 2019; Holman & Adebesin,

2019; Kotowski et al., 2018; Šumak et al., 2017;

Vokorokos et al., 2012; Zhang et al., 2019). However,

none of them measured the accuracy of the biometric

data as the same time when measuring users

experience at outdoors. In outdoor settings, the

EMOTIV Insight device is light to carry and, easy to

use and set up, which is always preferable features as

the device does not need a considerable amount of

time and effort to be carried, set and used.

Hence, we know very little about the accuracy of

this approach, which otherwise could bring great

benefits in the field of human-computer interaction to

automatically measure user experience. This paper

presents a field study that was conducted to measure

users’ experience automatically using an EEG

EMOTIV Insight headset device. and reports the

results. The next section provides an overview of the

adopted methods and techniques.

3 METHODOLOGY

A mixed methods approach was adopted using three

research techniques, which are: questionnaire,

automated biometric data using EMOTIV Insight and

the MyEmotiv app (see Fig. 1), and observations. The

study took place in the outdoor setting of the Royal

Pavilion in Brighton, UK. The convenience sampling

method was used to recruit participants. Visitors of

the site were targeted, which were randomly chosen

to be kindly asked if they are happy to take part.

Seven participants responded positively and accepted

to take part in this study. Participants were asked to

use a mobile app that provides historical information

regarding the site in multiple modalities (e.g. audio &

pictures to see attractions back in time) while wearing

the device. Participants were asked to perform two

tasks using related features of the app: (a) listen to an

audio explanation about the attraction; (b) seeing how

the attraction appeared in the past (more details

regarding the heritage app are given in Section 4).

3.1 Participants

Seven participants took part in this study; all of them

were visitors at the royal pavilion in Brighton. Their

age ranged between 30 to 50; three were females and

four were males. In terms of their background, one

was Australian, one was German, and the remainder

were British. In terms of their occupation, there were

three lecturers, one teacher, one project manager and

one officer. Consent for using their photos was

obtained.

3.2 Methods

MyEmotiv records brain’s waves captured by the

device in the real time to be analysed later. EMOTIV

is a wireless headset device that monitors brain

activity and translate them into meaningful data via

MyEmotiv – impaired data (see Fig.1). It has four

semi-dry polymer sensors that are placed right on the

skull to capture brain activity. Additionally, it has

nine axis sensors, which help detecting head

movements (Duvinage et al., 2013; Heunis, 2016).

MyEmotiv consists of six metrics; a brief definition

of each one is given below:

The impaired data obtained from MyEmotiv was

transferred to MS Excel to prepare them for the

analysis phase. A simple statistical analysis performed

to obtain the average and STD for each category

amongst participants.

A questionnaire technique was used to in this

study to compare its results with the biometric data.

The questionnaire was designed based on the

cognitive metrics of the MyEmotiv to compare

the results of both techniques, which are interest,

HUCAPP 2023 - 7th International Conference on Human Computer Interaction Theory and Applications

102

Table 1.

Interest

Measures how much you like or

dislike something

Excitement

Measures your level of mental

arousal

Relaxation

Is your ability to switch off

and reach a calm mental state

Engagement

Measures how immersed you are

in what you are doing or

experiencing

Stress

Measures how comfortable you

are with the current challenge you

are facing

Focus

Is your ability to concentrate on

one task and ignore distractions

A: Graph showing results of

all metrics

B: detecting “engagement”

with the definition

Figure 1: Examples of the results in MyEmotive.

excitement, relaxation, engagements, stress and

focus. The questionnaire consists four sections: (a) task

one: listening to the audio description; (b) task two:

seeing the attraction how looked in the past; (c)

demographic information; (d) Authorization. in

sections a & b participants are required to rate their

experience from 1 to 10, where 1 is the least and 10 is

maximum, when used the SmartC app based on the

six cognitive metrics, so each metric is rated from 1 to

10. Participants were also, given an opportunity to add

any comment regarding their experience. The results

obtained from the questionnaire was transferred MS

Excel preparing for the analysis phase.

4 STUDY SETTING

This study used a combination of three research

techniques as mentioned earlier to obtain rich data. It

used the EMOTIV device that monitors brain activity

and then translate it into meaningful data - impaired

data, which measures cognitive aspects of focus,

engagement, relaxation, stress, interest and

e x c i t e m e n t . E M O T I V i s a w e a r a b l e d e v i c e t o b e w o r n

on the head (a brain-worm device), which has five

channel EEG that help capture the brain’s signals

(EMOTIV INSIGHT, 2022) (see Figs 2 & 3). T h e

MyEmotiv a p p was used to obtain the data from

EMOTIV by simply pairing it to the device via

Bluetooth.

Participants were given a leaflet talking about the

purpose of the study and explaining how the device

works. Participants were required to wear the device

while at the same time perform two tasks using the

SmartC app, which is a mobile app for cultural

heritage sites that has features enable users to explore

heritage places in the context (Alkhafaji et al., 2019).

The features include: listening to audio explanations

a n d s e e i n g h o w a t t r a c t i o n s l o o k e d i n t h e p a s t , s e e - i t -

in-the-past, using augmented reality. This feature

simply works when visitors place a mobile device in

front of the related attraction, then an old image of the

attraction attached to a live camera view appears to

show how the attraction appeared in the past

(Alkhafaji et al., 2020).

The tasks that participants were requested to do

include: (a) listening to an audio explanation

regarding the related attraction; (b) seeing the

attraction how looked in the past. The researchers

helped participants to wear the device and set it up

to start the monitoring process. Participants filled out

a questionnaire directly after each task to rate their

experience. The questionnaire contained three

sections: (a) the demographic section; (b) task one,

which was about listening to the audio explanation;

and (c) task two, which was seeing how an attraction

looked in the past.

The questions were designed based on the six

metrics in MyEmotiv and the reason is to compare the

results of the MyEmotiv app that the device captured

automatically with the results that the participants

reported themselves. Participants were asked to rate

their experience when using the SmartC app in the

outdoor setting of the Royal pavilion from 1 to 10,

where 1 is the minimum and 10 is the maximum; these

were converted to percentages to be easily compared.

Biometric Evaluation to Measure Brain Activity and Users Experience Using Electroencephalogram (EEG) Device

103

Figure 2: A participant trying EMOTIV while doing the

first task.

Figure 3: A participant trying EMOTIV while doing the

second task.

The observational study was carried out during the

experience sessions; researchers took notes while

participants were performing the tasks (i.e. listen to

an audio and look at a picture of the attraction

displaying how it looked in the past). EMOTIV

worked properly with almost all participants, but

failed to connect properly with two of them, which

led the data were not recorded. One participant

performed the first task only as she had something to

do and the session took quite some time to perform.

More details regarding challenging and implication

are given in Section 7.

5 RESULTS

The results of the study are presented in this section.

For task 1, as shown in the Fig. 4, the results

obtained from the MyEmotiv app show participants

were interested and engaged in listening to the audio

explanation as the average respectively were 64% and

57%. These results were the highest amongst the

metrics of the app, where focus was the lowest as

it was 36%, which indicates participants were not

completely focused. The possible explanation of this

results could be the nature of the outdoor settings as it

was busy and noisy, which could affect participants’

focus. On the other hand, the stress was 42% and

relaxation was 49%, which indicate they had a slight

stress. That could be explained as they were not quite

relaxed when using a new device.

Participants also performed the second task, which

was seeing an old image of the attraction that showed

how the pavilion looked in the past. The results of

this task were less positive than the previous task

as it was slightly challenging to see the old image (see

Fig. 5). Participants needed to spend extra time trying

to get the image to appear, and more challenging to

keep it for enough time to be seen properly. That was

due the nature of this feature as it was based on

location, which sometimes caused the image to

disappear when making a slight move on location. In

addition to the EMOTIV device experience,

participants were asked to fill out a questionnaire to

report their experience themselves in terms of the

same aspects as the MyEmotiv app, on scale from 1

to 10. The data were converted to percentages to be

easily compared to the brain activity results.

The results of the questionnaire suggest that

participants were interested (81%) and engaged (79%)

with the experience; they were focused (77%), but

less excited (50%). Additionally, whilst the results

indicate participants were not highly relaxed (59%),

they were not stressed (27%) (see Fig. 4).

The figure of the results of the second task looks

different from the first one as shown in Fig. 5 and that

could be because the old image was not easy to obtain,

which caused a slight frustration amongst participants.

The results were: focus 70%, interest 63%,

engagement 43%, excitement 38%, stress 43% and

relaxation 42%; a further discussion is presented later

in this section. As shown, the average of the

“engagement” and “excitement” metrics are not

encouraging, which could give impression that

participants were not engaged. The possible

explanation is the nature of the feature as explained

earlier, it required the device to be against the

attraction and in a specific position for the old image

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to appear, which was slightly hard to find, “really

struggled to find position of phone where past photo

showed” . S a m e i s t r u e r e g a r d i n g t h e a v e r a g e o f t h e

“stress” category as one participant added against to

her rate, “was holding breadth to try to find the

correct position”.

Participants also were given a choice to add

comments if they would like to. Four of them chose

to add comments as given below:

“I like the feature of how looked in the past but

with more features such as video and 3D images”

“excellent ideas. Great to see technology

supporting heritage”

“I’ve lived in Brighton for 17 years and only visit

the Pavilion once, because it's quite expensive, so,

this could be a great alternative for a lower cost

experience, but still […] for the pavilion.”

“I would like to see the image for longer.”

Based on these comments, it is clear that participants

enjoyed the experience, but wished the image to stay

for longer to be better seen. The results are

compared in the next sub-section.

From looking to the above figures, it is clear that

the results of the questionnaire are more positive

than the results of the app. However, the trend is

similar in most metrics, with the exception of

“focus”, where the data shows big difference. This

could mean participants thought they were focused,

while they were trying to focus but the noise around

them prevented them from being focused without

realising. Our justification for this explanation is

that this device is supposed to work in any

circumstances (e.g. noisy or quite) based on the

official website (Šumak et al., 2017), so we assume

that its results are more accurate in this context.

Additionally, this would also explain the “stress”

results as the app reported “stress” is 42%, which

could mean they were get slightly stressed as they

were trying to focus in busy and noise circumstances

Figure 4: The results of task 1.

Figure 5: The results of task 2.

Biometric Evaluation to Measure Brain Activity and Users Experience Using Electroencephalogram (EEG) Device

105

that is not very easy. Comments by a participant

supports this explanation as she added against to her

rate of the metrics for the first task, “focus” and

“stress” to explain them, “noisy garden”, “trying to

hear”.

Figure 5 shows that the results of the second task

suggest both tools are relatively consistent and there is

no big difference between them. This could indicate

that the EMOTIV device could be a reliable tool to

measure people’s experience.

As the results of both studies suggest that the

participants were not highly relaxed but slightly

stressed, this could be due to two possible reasons:

first: using a new device, which they are not very

familiar with; second: the noisy environment that

required participants to put more efforts to focus.

An observational technique was also used in this

study. Participants were verbalising their thoughts as

they performed the tasks, which made it easier for

researchers to capture them.

The results of this technique show that participants

liked the idea of using a mobile app for acquiring

information regarding cultural heritage sites. They

showed a great interest in using EMOTIV to measure

their experience as they looked excited, especially

they were able to see a 3D image that shows brain

activity pattern while they were performing the tasks.

It was noticed that most participants made comments

r e g a r d i n g t h e f i r s t t a s k a f t e r t h e y w e r e d o n e w i t h i t ,

while they were making comments regarding the

second task while they were doing it. This could be

explained in two ways: first, it could give an

impression they were more relaxed and enjoying the

first task, while they got slightly frustrated during the

second task. Second: the first task needed participants

to focus due to the noisy environment. Whilst both

explanations could be valid; we tend to go with the first

one due to the fact that participants looked interested

and enjoying the audio as explained below.

Regarding the first task and as mentioned earlier,

participants seemed the were enjoying the audio

explanation and mentioned the word “very

interesting” a lot during and after the task when they

talked about it after the completion of it. Regarding

the second task, participants very much wished to see

the old image of the attraction, but because it was

slightly challenging to obtain, they looked slightly

frustrated. One participant said, “I am holding breath

to get the old image”, as she mentioned that she really

wanted to see it clearly. Another Participant

mentioned that the time spent on getting the image to

show up had a negative effect on engagement. In brief,

the results of the observations were consistent with the

results of the questionnaire and the MyEmotiv app.

6 DISCUSSION

The user experience study presented in this paper

helped have preliminary insights regarding the use of

EEG devices such as EMOTIV Insight to measure the

experience of people automatically when performing

a task or simply when doing any activity. This would

support researchers to measure participants’

experience during field studies to get a better insight

on the investigated issues. The results of all methods

were relatively consistent, which give validity to the

findings.

The results indicate that the biometric data

obtained from of EMOTIV are reliable to some

extent, which means the device have a potential to be

used by researchers in field studies to measure the

experience of users alongside other self-reported

techniques. This could be a good tool in evaluation

studies, as often measuring the experience of users is

challenging due to the fact that the experience,

including aspects such as the ones measured in this

study (e.g. engagement, interest, etc.), is in users’

mind (Attfield et al., 2011; Henrie et al., 2015)] and

participants sometimes are not able to accurately

report the experience (Poole & Ball, 2006). An

example of this, the results of the questionnaire

show the average of the “focus” was 77% during the

first task, while the results of the app show it was

38%, which is a big difference. The justification for

this could be that participants sometimes cannot

measure their experience properly, or simply not

very accurate as human often forget things after a

short period. Someone could ask here, why not the

other way around and not the results of the app were

not accurate? Our justification for this, that although

there was a gap between the results of both tools of

the first task, the trend was the same, the only

exception was the “focus”. Additionally, the results

of the second task were relatively similar of both

tools. The other reason, the study took place at the

outdoor setting of the site on a beautiful summary

day, which was very busy; consequently, it was

noisy, which made sometimes slightly hard to focus.

Thus, we believe that the results of the app were

more accurate in this context.

Although the trend of the results was similar,

there was a gap between the results from both

resources (questionnaire and EMOTIV) as the results

of the questionnaire were slightly higher in most

metrics. That suggests that participants were more

generous in reporting their experience than the app.

Alongside the positive results regarding the

validity of capturing biometric data, this study

captured participants’ experience regarding the use of

HUCAPP 2023 - 7th International Conference on Human Computer Interaction Theory and Applications

106

a mobile app to acquire information at heritage

places. The results suggest participants enjoyed the

experience and liked using a mobile app for acquiring

historical information about cultural heritage places.

They showed a great interest in using such apps in the

context while at the same time enjoying being at the

place, i.e. not having to choose between the place and

the technology. This would give a sense of the place,

while at the same time receiving information about

the story of the place with less-cost. In short, this

study suggests two important points:

• Biometric data obtained from a device such as

EMOTIV Insight have a good potential to be

considered in user experience studies.

• Visitors of cultural heritage sites like and some

of them prefer using mobile apps that provide

stories of cultural heritage sites to have a low-

cost experience.

7 LIMITATIONS AND

IMPLICATIONS

A few implications and limitations were raised during

this study, which include:

• Time constraint of participants: although

recruiting participants in the context and

without previous planning has its own benefits,

such as capturing the experience of real users,

which is good for the validity of the research,

However, it has disadvantages in terms of time

constraints for participants as they were not

prepared to spend a considerable portion of

their leisure time participating in a study.

• Participants were not easy to recruit for the

same previous reason, as they were not willing

to waste their time of leisure participating in the

study.

• A technical issue had arisen during the study,

which was in setting up the EMOTIV device.

It seemed the sensors of the device needed to

be directly on the skin of the skull to work

properly, which was slightly challenging for

participants of thick hair.

• In addition, some hygiene issues were raised

as the device needed to be worn on heads; due

to this, some participants were slightly

cautious about wearing it.

• Noise was an issue as the site was busy on the

day of the study, which made it slightly

challenging sometimes to focus.

• Finally, the study is limited to a small-scale

sample; consequently, studies with a larger

number of participants are needed to confirm

validity of the accuracy of the EMOTIV

device.

8 CONCLUSIONS

A user experience study has been presented in this

paper. The EMOTIV insight device was utilised in this

study to measure the users’ experience during the use

of a mobile app for cultural heritage sites at outdoor

settings. A combination of three methods were used

in this study: capturing automated biometric data

using EMOTIV, questionnaire and observations. The

results of all methods were mostly consistent. The

results suggest that the data obtained from EMOTIV

are relatively reliable; thus, such devices could be a

good support for researchers to measure users’

experience in field studies.

The results show that participants were interested,

engaged and focused to some extent with the

experience when using a mobile app to acquire

information in an outdoor setting of a heritage place.

They mentioned it would provide a low-cost

experience. Also, the results suggest participants

were slightly stressed, possibly because of the noisy

environment combined with using a new device.

Additionally, the results show that participants were

m o r e g e n e r o u s w h e n r a t i n g t h e i r e x p e r i e n c e t h a n t h e

app. This also needs further research to be confirmed.

Further research is needed to with more

participants to provide a better insight regarding the

use of the EEG devices such as the EMOTIV Insight

device.

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