Review: Use of EEG on Measuring Stress Levels When Painting and

Programming

Nataliya Tupikina, Gcinizwe Dlamini

and Giancarlo Succi

Innopolis University, Russian Federation

Keywords:

Electroencephalography, Software Development, Stress, Painting.

Abstract:

For years, brain activity, stress level during programming and painting have been analyzed separately. As the

world gets more digital and human life gets more dependent on technology, it has become more important

to analyse the relationship between programming, software developers’ brain activity, creative practices (i.e

painting) and stress level. In this paper, we present the results of a systematic literature review whereby the

research questions are centred around analysing the relationship between stress levels and brain activity when

a person is painting or writing a piece of software. The search for relevant studies was done on google scholar

and IEEE Xplore. The results of our review show that: (1) EEG can be used to accurately measure stress

levels, (2) there is limited research in the analysis of stress level pattern of the stress level when people paint

depending on different situations and styles of painting. In light of the systematic literature review result,

using EEG we plan to conduct experiments to measure the stress level when a person is painting a picture or

programming.

1 INTRODUCTION

In the era where technology has become the main in-

strument for a human being to accomplish daily tasks,

the demand for high-quality software is increasing

from day to day. The increase in high-quality soft-

ware has induced demand for software developers to

produce creative software in a limited time. Conse-

quently, this results in increased stress levels for pro-

grammers. The impact of certain stress levels can lead

to increased performance and productivity. However,

excessive stress levels can result in deterioration of

abilities (Hassan et al., 2006). Certain levels of stress

can impair technical skills, creativity skills, as well as

memory, concentration and other cognitive processes

(Hassan et al., 2006; Klein, 1996).

Stress is usually described as the condition when

threatened homeostasis happens in the body, followed

by the responses of two types: physiological as well

as behavioural (Chrousos, 1998). After the brain no-

tices the ’threat’, the hypothalamus, along with the pi-

tuitary gland, and also adrenal glands make the whole

body react by producing hormones and other things.

Centuries ago, by ’threat’ people usually meant

https://orcid.org/0000-0002-4578-5011

https://orcid.org/0000-0001-8847-0186

wild animals, feuding tribes. But today, when most

people work in safe conditions and can get food with-

out hunting by visiting the local shop, stress did not

disappear. Scientists analyse stress levels and emo-

tional well-being across many ﬁelds, involving the

Information Technology ﬁeld, as many programmers

face burnout (Hetland et al., 2007). In our research,

we are curious to ﬁnd out the impact of certain stress

levels on software quality and how it relates to creativ-

ity. In addition, we are aimed at analysing the brain

waves patterns when developing creative software.

Painting is one of many forms of art which evokes

creativity. Some do it for living, some do it for fun,

some do not do it at all. While painting can be taught

and the skill can be improved, in theory, almost any-

one can draw at least something. In another form of

art, such as playing music, it is hard to repeat a fa-

miliar melody without knowing the notes and how to

play correctly. However, with painting nearly anyone

can try to reproduce a picture that they see or some-

thing that they imagine. On the other hand, a person

can imagine and draw something that never existed

instead of reproducing a thing they have already seen.

This kind of creativity is to be adopted in software

development in order to produce useful technology.

Compared to painting, programming requires

some skill level to start writing. At least the knowl-

374

Tupikina, N., Dlamini, G. and Succi, G.

Review: Use of EEG on Measuring Stress Levels When Painting and Programming.

DOI: 10.5220/0011101200003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 374-380

ISBN: 978-989-758-569-2; ISSN: 2184-4992

edge of the structure of some programming language

is required, and even the clear code may work incor-

rectly, possibly attributing to the stress level. Pro-

gramming still involves creativity as the program-

mer needs to plan everything ahead, creating a pro-

gram that was never done before. Painting would be

a suitable activity to be compared to programming,

since it also introduces some level of creativity, in-

stead of some monotonous routine. The questions be-

hind this research are : what exactly affects program-

mers’ stress levels? Could the programming itself

reduce or increase a person’s stress level? How can

we adopt some practices from painting and mimic the

state of mind observed during painting to produce cre-

ative software? To answer the aforementioned ques-

tions we decided to conduct a systematic literature re-

view whereby we deﬁned our research questions as

follows:

RQ1. Can stress level be accurately measured using

an EEG device?

RQ2. What is the pattern of the stress level when peo-

ple paint depending on different situations and

styles of painting?

RQ3. Does the stress level of programmers somehow

follow some speciﬁc pattern during code writ-

ing?

RQ4. Can we draw a comparison between speciﬁc

situations or activities that are done while pro-

gramming and done while painting according

to the pattern of the level of stress?

The rest of this paper is organized as follows: Sec-

tion 2 presents background information on EEG to-

gether with related works. Section 3 outlines our

approach in answering the aforementioned research

questions (RQ1., RQ2., RQ3. and RQ4.). Section 4

and 5 presents the results together with the results dis-

cussions. Threats to validity are presented in section

6. Section 7 draws the conclusions and introduces fu-

ture work.

2 BACKGROUND

This section presents background on brain waves,

electroencephalography (EEG) analysis techniques

and studies related to stress measurement using EEG.

It can be skipped by readers already familiar with the

matter.

2.1 Brain Waves

Electroencephalography is a an approach for measur-

ing brain activity using electrodes attached to a scalp.

The brain activity is measured from from the elec-

tric signals produced by the ﬁring of neurons in the

brain. The electrodes attached to the scalp capture os-

cillations at a range of frequencies. The oscillation

frequencies are classiﬁed into frequency ranges (i.e

delta, theta, alpha, beta, and gamma). Researchers

have suggested that the different frequencies captured

with neuroimaging device are correlated to different

mental states and has a biological signiﬁcance (Im-

peratori et al., 2014; Imperatori et al., 2019).

2.2 Measuring Stress

Hans Seyle (Selye, 1985) initially deﬁned stress as

the nonspeciﬁc result of any demand upon the body,

be the effect mental or somatic. Detecting stress and

identifying the effects of stress on daily life has been a

objective for researchers in the social science domain.

However recently software engineers have started in-

vestigating the impact of different mental states on

software quality and development process.

Some cross-sectional studies suggest an associa-

tion between brain activity, creativity and stress (An-

dersson et al., 2001). Over the years researchers have

proposed systems and approaches the human physical

and mental states such as stress in the working envi-

ronment using different biophysical signals. A new

apparatus (Andersson et al., 2001) was designed to

assess the stress levels of call-centre operators. The

study uses two types of sensors to monitor the work-

ing environment: environmental and physiological.

The evaluation of stress relies more on the latter sig-

nals. The goal of the authors was to design the system

to improve the well-being of the employees with the

application of multi-sensor analysis.

The portable system described in (Aljuaid, 2019)

measures biophysical signals in real-time and noti-

ﬁes unwanted mental behaviour. The notiﬁcations are

sent in case the following conditions in the worker are

detected: 1) absent-minded/inattentive, 2) stressed, 3)

extreme fear, 4) anger, 5) stun/daze, 6) overloaded

with work, 7) drowsiness, and 8) dizziness. The au-

thor focuses on neuroergonomics as a primary ﬁeld of

study. As well as the previous study, this one aimed

to design a system to predict human mental and phys-

ical state and increase productivity and well being at

work. However, the range of biological signals col-

lected was signiﬁcantly broader than in (Andersson

et al., 2001) and brain, muscle activity analysis was

used.

The device proposed in (Amores et al., 2018) de-

termines the relaxation level of the user. It consists

of the Virtual reality headset and the olfactory neck-

lace. The necklace changes the intensity of aroma,

Review: Use of EEG on Measuring Stress Levels When Painting and Programming

375

depending on the subjects’ EEG datagrams. In (Sai-

datul et al., 2011), mental stress was measured while

solving arithmetic tasks. The (Duraisingam et al.,

2017) detected the difﬁculty of program comprehen-

sion tasks among the students. The (Sun et al., 2015)

describes a method to determine the drivers’ vigilance

level. In the context of the studies mentioned above

the following biophysical signals were used:

• heart rate (Aljuaid, 2019) (Andersson et al.,

2001);

• galvanic skin resistance (Andersson et al., 2001)

• body temperature (Aljuaid, 2019);

• blood pressure (Aljuaid, 2019) - a sensor is placed

in the temple part of the head or in the upper part

of the shoulder depending on the type of device;

• EEG (Aljuaid, 2019) (Amores et al., 2018) (Du-

raisingam et al., 2017) (Duraisingam et al., 2017)

• EMG (Aljuaid, 2019);

3 METHODOLOGY

Our proposed approach is a literature review to an-

swer the posed research questions. The following sec-

tions outline the details about each stage. Our litera-

ture review follows the approach deﬁned in (Kitchen-

ham and Charters, 2007).

3.1 Search Strategy

To retrieve the literature to answer our research

questions, we used digital libraries namely: Google

Scholar and IEEE Xplore. Based on the research

questions, we compiled a set of keywords correspond-

ing to each RQ. Search strings were developed con-

sidering the keywords which were joined with the op-

erator OR and AND. Table 1 shows the overall key-

words, search queries and the corresponding research

questions.

RQ4. answer will be derived from RQ1., RQ2.

and RQ3., therefore we did not make keywords and

search query corresponding to the research question

(RQ4.). The search for publications was manual.

3.2 Data Extraction

The data retrieved from the selected digital libraries

were manually assessed and relevant information was

extracted and stored for further analysis. For each and

every retrieved study, the full names of all authors,

full titles, years of publication, publication journals

or conferences, abstracts information was stored in a

spreadsheet. As part of preprocessing the retrieved

data, we ﬁltered out all duplicates to create a list of

studies for further analysis (i.e if the same article ap-

peared multiple times in different digital libraries en-

gines for one RQ or in the same search engine for

multiple RQs, it was only recorded once).

3.3 Inclusion and Exclusion Criteria

After compiling the data extracted using the speciﬁc

search queries, it is important to determine which

publications are relevant to the topic, since some ar-

ticles may include given keywords despite covering

a non-related topic (i.e the development of software

that analyses EEG results or covers art not related to

painting). For a study to be considered in our litera-

ture review, it should meet the following criterion:

1. The study should be available in the English lan-

guage

2. The study should include experiments

3. These experiments should involve painting or pro-

gramming.

4. The article must be published not more than 30

years ago

Moreover, some other studies were excluded

based on the following criterion:

1. The studies involving verbal creativity experi-

ments, imagination experiments must be omitted

2. The studies where no code is written during exper-

iments despite involving programmers or cover-

ing a topic related to software development (UML

creation, code analysis).

3.4 Quality Assessment

“Overview Quality Assessment Questionnaire

(OQAQ)” has been proposed by A.D.Oxman and

G.H.Guyatt to assess certain aspects of the scientiﬁc

quality of research overviews (Oxman, 1991). For

further and ﬁner ﬁltering of the retrieved publications

we applied a quality assessment using the following

questions:

QA1. Does the paper contain data resulting from the

experiments on stress authors had using EEG?

QA2. Do experiments involve more than 3 people?

QA3. Do the volunteers from the experiments belong

to the same age group?

Publications that are more suitable for further re-

view, should have more than 1.5 points after assigning

scores according to the criteria outlined below:

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

376

Table 1: Keywords and Search Queries.

Research Questions Keywords Search Query

RQ1. Measuring, Monitoring, Stress, Devices,

EEG

“EEG” OR “devices” AND “stress”

AND “measuring” OR “monitoring”

RQ2. EEG, Painting, Pattern, Stress “EEG” OR “electroencephalogram”

AND “painting” AND “pattern” AND

“stress”

RQ3. EEG, Programmers, Programming, De-

velopers, Stress, Pattern

“EEG” AND (“programming” OR “de-

velopers” OR “programmers”) AND “ex-

periment” AND “stress”

QA1. 1, does have experiment held by authors, 0

- authors analyse available data from experi-

ments related to other studies

QA2. 1, experiments involve more than 3 people, 0.5

- 3 people, 0 - less than 3 people

QA3. 1, all people involved in the experiments be-

long to the same age group, 0 - people in

experiments do not belong to the same age

group.

4 RESULTS

This section presents the results achieved after follow-

ing the steps outlined in section 3. The studies that

were left after applying inclusion and exclusion crite-

ria along with quality assessment were considered to

be relevant to the discussed ﬁelds.

By using the keywords in Table 1 above, we found

74,900 articles on Google Scholar, applied inclusion

and exclusion criteria to 50 of them, performed qual-

ity assessment on 20 of them, and only 10 were as-

sumed to be relevant. The studies distributions ac-

cording to the year of publication is presented in Fig.

1. Tables 2, 3 and 4 present all the included studies

in correspondence to research questions together with

the quality assessment scores.

Table 2: Papers Related to RQ1 Which Passed the Quality

Assessment.

Names of authors Year QA1 QA2 QA3 Score

Hou et al. 2016 + + + 3

Hosseini and Khalilzadeh 2010 + + + 3

Jun and Smitha 2016 + + + 3

Saeed et al. 2015 + + + 3

Table 3: Papers Related to RQ2 Which Passed the Quality

Assessment.

Name of the paper Year QA1 QA2 QA3 Score

Zhang et al. 2021 + + - 2

Wang and Huang 2016 + + + 3

Wang and Shih 2016 + + + 3

2021

2020

2019

2018

2017

2016

2015

2013

2012

2010

2009

1998

Years

Number of Papers

Before I/E and QA

After I/E and QA

Figure 1: Distribution Of The Reviewed Literature Ror SQ1

Over The Years.

Table 4: Papers Related to RQ3 Which Passed the Quality

Assessment.

Name of the paper Year QA1 QA2 QA3 Score

Salma et al. 2017 + + - 2

Yamamoto et al. 2016 + - + 2

Desai 2017 + + + 3

5 DISCUSSION

In this section, we discuss the answers to our research

questions.

RQ1. Can We Accurately Measure the Level of

Stress using EEG Devices?

Based on the data and experimental results

retrieved from selected studies, we conclude

that an EEG device is a reliable tool for mea-

suring stress levels with decent accuracy. Hou

et al. (Hou et al., 2015) and Jun (Jun and

Smitha, 2016) separately conducted experi-

ments based on the same test and classiﬁed

the results using the same classiﬁer for dif-

ferentiating stress levels, each achieving ac-

Review: Use of EEG on Measuring Stress Levels When Painting and Programming

377

curacy higher than 75%. Hosseini (Hosseini

and Khalilzadeh, 2010) used the Elman clas-

siﬁer and the accuracy was 82.7%. Saeed et

al. (Saeed et al., 2015) used three different

classiﬁers, and every single one of them had

at least 64% accuracy. The high accuracy with

different types of classiﬁers shows that EEG is

an effective way to precisely record the brain

signals to measure the stress level.

RQ2. What is the Pattern of the Stress Level

When People Paint Depending on Different

Situations and Styles of Painting?

As noted by Wang and Huang (Wang, 2016b)

and also Wang and Shih (Wang, 2016a) dur-

ing one-time experiments, concrete ﬁgure

painting reduces stress despite using different

styles of art. Zhang et al. (Zhang et al., 2021)

proved via a long experiment with Chinese

bird painting that stress relief can help to cure

depression. The common outcome seems to

be that in all painting experiments, the draw-

ing which is not abstract and has many details

helps to decrease the stress level. But since

not many studies were found relevant and did

contain information on EEG experiments, we

can determine that the research gap is present.

RQ3. Does the Stress Level of Programmers

Somehow Follow Some Speciﬁc Pattern

during Code Writing?

Salma et al. (Salma et al., 2018) noted that

the stress level for the task with a time limit is

higher than for the task without a time limit.

Yamamoto et al. (Yamamoto et al., 2016)

proved that stress level is the same for the au-

thors of successful and unsuccessful solutions

if both have a time limit. Desai (Desai, 2017)

showed that extending the time limit reduces

stress and that certain programming languages

cause more stress with the same tasks. All

the found studies do not directly address the

question about the stress level pattern while

writing code. Therefore, we can conclude that

there is a need for additional research into the

stress patterns that occur while programming.

RQ4. Can We Draw a Comparison between Spe-

ciﬁc Situations or Activities That Are

Done While Programming and Done While

Painting According to the Pattern of the

Level of Stress?

To this end, we cannot make a concrete con-

clusion because of the lack of research that is

directed in the analysis of stress levels com-

parison for painting and painting. However,

from the retrieved studies in the study, we

noticed that painting reduces stress (Zhang

et al., 2021; Wang, 2016b; Wang, 2016a)

and programming may increase stress levels

(Salma et al., 2018),(Yamamoto et al., 2016),

the change during coding could be explained

by the presence of a time limit, which was

not present during art experiments. We re-

frain from making a solid conclusion since the

situations and types of tasks from the stud-

ies differ so much. There is a need for fur-

ther research in the covered ﬁelds using EEG

with similar setups and similar kinds of activ-

ities to analyse the stress levels change during

programming and painting properly and to be

able to compare them.

6 THREATS TO VALIDITY

Here, we discuss the threats to the internal and ex-

ternal validity of our results. Based on Wohlin et

al. (Wohlin et al., 2012) the following subsections

present the different threats to validity.

6.1 Construct Validity

(Wohlin et al., 2012) deﬁnes construct validity threats

as threats linked to the generalization of the results to

the concepts behind the study. During the analysis of

selected studies, we noticed that some studies did not

give full details on the protocol and theoretical bases

for selecting speciﬁc techniques. To minimize the im-

pact of such issues we formulated a much more strict

quality assessment (i.e presented in section 3.4). In

addition, we carefully analysed the experiment proto-

col and research objective for each and every primary

study.

6.2 Internal Validity

Internal validity threats may affect the results due

to incorrect conclusions. Guidelines proposed by

Kitchenham (Kitchenham and Charters, 2007) were

used to avoid this kind of risk. We deﬁned a review

protocol to include all relevant EEG, stress and paint-

ing studies in the search. All the relevant and major

digital libraries related to brain waves analysis in soft-

ware engineering were selected as sources for studies

to minimize internal validity.

6.3 External Validity

These threats arise from the generalization of the re-

sults of the review to the real-world scenarios (Wohlin

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378

et al., 2012). In the review presented, we excluded

studies not written in English and this poses a lim-

itation on generalizing our results since in our data

retrieval and data analysis we found studies written in

other languages (i.e Russian). However, it is worth

noting that these studies written in Russian did not

meet the other inclusion/exclusion criterion as they

were analysed by the ﬁrst author who is native a Rus-

sian language speaker.

6.4 Conclusion Validity

Similar to what is known as interpretive validity, con-

clusion validity is concerned with the formulation

of inaccurate conclusions from the given data. This

could be the small set of primary studies we have

collected. To mitigate the risk of excluding primary

studies we did the following: (i) the search strings

are deﬁned by considering the concepts and their

acronyms, (ii) the related works of all the primary

studies are carefully analyzed, (iii) Duplicates were

removed based on the title, author, publisher, and year

of publication. The full text of selected studies was

carefully read through to mitigate misinterpretations

and extract accurate data.

7 CONCLUSIONS AND FURTHER

WORK

The goal of this study was to ﬁnd the relationship be-

tween observed stress levels while programming and

while painting a picture. The relationship was aimed

to be recognised at the mental state level with the

help of EEG. To achieve the aforementioned goals,

we conducted a systematic literature review using

guidelines proposed by Kitchenham (Kitchenham and

Charters, 2007). We formulated four research ques-

tions and based on inclusion/exclusion criteria and

quality assessment we found 14 studies that were used

to answer the research questions. The review shows

that EEG devices can be used to accurately measure

stress. Secondly, the review revealed that there exists

a research gap and there is a need to conduct more ex-

periments for analysis of the stress level pattern when

a programmer is writing the code.

For future works, we plan on conducting experi-

ments whereby we will focus on the comparison be-

tween speciﬁc situations or activities that are done

while programming and done while painting accord-

ing to the pattern of the level of stress. In addition, we

aim at analysing a speciﬁc pattern of stress levels of

programmers during code writing in the experiments.

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