Автономная некоммерческая организация
высшего образования
«Университет Иннополис»
Autonomous noncommercial organization
of higher education
“Innopolis University”
ВЫПУСКНАЯ КВАЛИФИКАЦИОННАЯ РАБОТА
ПО НАПРАВЛЕНИЮ ПОДГОТОВКИ
09.03.01 – «ИНФОРМАТИКА И ВЫЧИСЛИТЕЛЬНАЯ ТЕХНИКА»
GRADUATE THESIS FIELD OF STUDY
09.03.01 – «COMPUTER SCIENCE»
НАПРАВЛЕННОСТЬ (ПРОФИЛЬ) ОБРАЗОВАТЕЛЬНОЙ
ПРОГРАММЫ
«ИНФОРМАТИКА И ВЫЧИСЛИТЕЛЬНАЯ ТЕХНИКА»
AREA OF SPECIALIZATION / ACADEMIC PROGRAM TITLE:
«COMPUTER SCIENCE»
Тема
Исследование по измерению и контролю внимания
программистов во время работы при помощи
электроэнцефалографии
Topic
EEG monitoring and controlling programmers' attention during
work
Работу выполнил /
Thesis is executed by
Амирова Розалия
Рушановна
Amirova Rozaliya
Научный
руководитель /
Thesis supervisor
Суччи Джианкарло
Giancarlo Succi
подпись / signature
подпись / signature
Иннополис, Innopolis, 2020
I dedicate this work to my family. You always support and love me. Thank
you for encouraging my pursuit for knowledge
Contents
1 Introduction
6
2 Literature Review
8
2.1 Chapter overview . . . . . . . . . . . . . . . . . . . . . . . . .
8
2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
2.3 Research method . . . . . . . . . . . . . . . . . . . . . . . . .
10
2.3.1
Research Questions . . . . . . . . . . . . . . . . . . . .
10
2.3.2
Search Process . . . . . . . . . . . . . . . . . . . . . . .
11
2.3.3
Inclusion and Exclusion Criteria . . . . . . . . . . . . .
11
2.3.4
Data Collection . . . . . . . . . . . . . . . . . . . . . .
12
2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12
2.4.1
RQ1: How biophysical signals were used in determining
the conditions of human activity or work . . . . . . . .
12
2.4.2
RQ2: In the framework of RQ1, how EEG was used? . .
15
2.4.3
RQ3: What kind of analysis of EEG results was used? .
18
2.4.4
RQ4: How attention and stress level were studied during
programming? . . . . . . . . . . . . . . . . . . . . . . .
20
2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23
CONTENTS
4
3 Design and Methodology
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
24
3.1.1
Brain Waves . . . . . . . . . . . . . . . . . . . . . . . .
25
3.1.2
Signs of Attention and Mental Workload . . . . . . . . .
26
3.2 Experimental Design . . . . . . . . . . . . . . . . . . . . . . .
28
3.2.1
Purpose . . . . . . . . . . . . . . . . . . . . . . . . . .
28
3.2.2
Data Sources . . . . . . . . . . . . . . . . . . . . . . . .
28
3.2.3
Equipment and Tools . . . . . . . . . . . . . . . . . . .
29
3.3 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . .
30
3.4 Data collection protocol . . . . . . . . . . . . . . . . . . . . . .
32
3.4.1
Experimentation Settings . . . . . . . . . . . . . . . . .
32
3.4.2
Prepatation Procecedures . . . . . . . . . . . . . . . . .
33
3.4.3
Experimentation Steps . . . . . . . . . . . . . . . . . .
33
3.5 Data analysis protocol . . . . . . . . . . . . . . . . . . . . . . .
34
3.5.1
Channel selection . . . . . . . . . . . . . . . . . . . . .
34
3.5.2
Data preprocessing . . . . . . . . . . . . . . . . . . . .
34
3.5.3
Feature selection . . . . . . . . . . . . . . . . . . . . . .
35
4 Implementation and Results
37
4.1 Participants Statistics . . . . . . . . . . . . . . . . . . . . . . .
37
4.2 Data Capturing . . . . . . . . . . . . . . . . . . . . . . . . . .
40
4.3 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . .
40
4.3.1
Channels Selection . . . . . . . . . . . . . . . . . . . . .
40
4.3.2
Data Preprocessing . . . . . . . . . . . . . . . . . . . .
42
4.3.3
Feature Extraction . . . . . . . . . . . . . . . . . . . .
44
5 Analysis and Discussion
48
CONTENTS
5
5.1 Attention Control . . . . . . . . . . . . . . . . . . . . . . . . .
48
5.2 Mental Workload types . . . . . . . . . . . . . . . . . . . . . .
49
6 Conclusion
51
Bibliography cited
53
List of Tables
3.1 Technical characteristics of EEG Smart BCI cap . . . . . . . .
29
4.1 Included and excludes electrodes . . . . . . . . . . . . . . . . .
41
4.2 Comparison of mean band values for the beginning and ending
of the session
. . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1 Mean values of bands for different types of mental activity
. .
46
50
List of Figures
3.1 Student during experimentation session . . . . . . . . . . . . .
30
3.2 Selected channels . . . . . . . . . . . . . . . . . . . . . . . . .
35
4.1 Programming experience of the participants . . . . . . . . . . .
38
4.2 Difficulty of the task for the participants . . . . . . . . . . . . .
38
4.3 Level of the comfort of participants . . . . . . . . . . . . . . .
39
4.4 Level of the focus of participants . . . . . . . . . . . . . . . . .
39
4.5 Mitsar EEG Studio . . . . . . . . . . . . . . . . . . . . . . . .
42
4.6 Automatic artifacts detection . . . . . . . . . . . . . . . . . . .
43
4.7 Example of artifact in data . . . . . . . . . . . . . . . . . . . .
43
4.8 EEG spectra analysis . . . . . . . . . . . . . . . . . . . . . . .
45
4.9 Analysis Wizard . . . . . . . . . . . . . . . . . . . . . . . . . .
46
5.1 Comparison of Alpha and Theta band values for the halves of
the recordings . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
Abstract
The field of information technology has tightly entered into all spheres of human activity. The core of Computer Science is programming, modern science
and industry rely on the work done by them. For improving speed and quality of development of IT projects it is essential to study how to increase the
efficiency of developers. One way to improve the quality of developers’ work
is to help them concentrate better, timely detect a drop in concentration and
brain fatigue, which could be done by controlling the level of attention during
programming. More and more types of devices for detecting biophysical signals
are becoming more accessible. This study focuses on the study of the level of
attention of programmers using EEG, as the most promising and realistic for
use in this environment. We found that the level of attention can be determined
using the control of alpha and beta waves measured using EEG, as well as specific features of the functional state of the brain compared to another type of
mental load - driving.
Chapter 1
Introduction
The field of information technology (IT) has tightly entered into all
spheres of human activity. The core of Computer Science is programming,
modern science and industry rely on the work done by them. For improving
speed and quality of development of IT projects it is important to study how
to increase the efficiency of developers. One way to improve the quality of
developers’ work is to help them concentrate better, timely detect a drop in
concentration and brain fatigue, which could be done by controlling the level
of attention during programming. On the other hand, it is very important to
monitor the level of satisfaction and happiness of employees [1] [2] to prevent
possible psychological disorders [3]. In order to contribute to the development
of this field, for the current work, we formulated and answered the following
research questions:
• RQ1: What methods do exist to estimate the attention level during coding?
• RQ2: Can the change of the level of attention during programming be
measured using EEG?
7
• RQ3: Is there the possibility to identify correlations between attention
levels and types of mental activity, assuming that the attention level is
measured using EEG devices?
This work structured as follows. In the second chapter, we give a literature review about the use of biophysical signals in the studied field. The third
chapter is dedicated to describing experimentation and analysis protocols. Implementation details are given in the fourth chapter. The fifth chapter contains
the results of the experiments. Conclusion and further work directions in this
area are given in the sixth chapter.
Chapter 2
Literature Review
2.1
Chapter overview
The systematic literature review on the measuring of working conditions
for people in general and measuring an attention level for programmers is presented in this section. Special attention is paid to the EEG method is one of
the most popular and promising. The purpose of this study is to investigate
state of the art in the field, find gaps, suggest future work and find answers to
the research questions. Current literature review based on Systematic Literature Review (SLR) [4] done by me and my colleagues Repryntseva Anastasiia,
Tarasau Herman, Artem Kruglov and Sara Busechian. Original SLR was performed according to Dr Andy Siddaway [5], and Barbara Kitchenham [6] works
and consists of an introduction which describes prerequisites for writing current study and general terms, research methods with a description of research
methodology, research questions, the definition of the search process and queries
used, list of inclusion and exclusion criteria, a results section answers research
questions. Subsections 5 and 6 are dedicated to discussion and conclusion,
2.2 Introduction
9
respectively.
2.2
Introduction
The use of biophysical signals in the analysis of human physiological state
and well-being got broad popularity in different areas of science. Medical experts use those signals to study the processes in our bodies and how external
factors affect those processes. Computer science researches have acknowledged
the role of attention and other mental states in the well being of software individuals, teams, and organizations [7]–[16], and use biosignals to build systems
that will help people monitor their state and develop new analysis techniques
that will help understand the meaning of the collected signals better.
One of the methods for reading brain activity is Electroencephalography
(EEG). It studies the functional state of the brain by recording its bioelectric
activity. This method provides a wide scope for experiments because it allows
us to interpret data online, conduct experiments during various activities since
it is portable, non-invasive and does not require the help of doctors.
As was said previously, Computer Science (CS) researchers use EEG in
combination with other bio-signals to build Brain-Computer Interfaces (BCI)
– systems that can measure the activity of the brain and the central nervous
system, analyze it and convert it into artificial output. Using different CS
algorithms and techniques, BCIs can clean, enhance or improve the natural
output of the brain. Moreover, many data analysis techniques can be applied
in BCIs to predict human behaviour or state. Such systems can be used in
different fields of research such as e-learning, driving, performance at work and
analysis of programmers’ behaviour.
2.3 Research method
10
The goal of the research is to perform a preliminary review of the current
status of the use of bio-signals in the studies of the human physiological state
and how EEG was used in the field. Also, it aims to collect a list of EEG
analysis techniques that can be referenced in further studies.
2.3
Research method
This section describes the research process and steps performed during
the Systematic Literature Review (SLR). First, the formulation of the research
questions and their importance is given. Then inclusion and exclusion criteria
were given as well as the data collection process is described.
2.3.1
Research Questions
To identify the primary studies that address the topic of our SLR, we formulated three research questions (RQ). Our study aims to answer the following
ones:
• RQ1: How biophysical signals were used in determining the conditions of
human activity or work?
• RQ2: In the framework of RQ1, how EEG was used?
• RQ3: What kind of EEG analysis techniques were used?
• RQ4: How attention and stress level were studied during programming?
2.3 Research method
2.3.2
11
Search Process
Our search process was a manual search in the two largest digital libraries
available: ACM Digital Library and IEEE Xplore Digital Library.
For each RQ, the keywords were extracted, and proper search queries
were defined using those keywords.
All the results from all 3 search queries were exported to a Rayyan QCRI
[17] – a web-application for collaboration on systematic literature reviews.
2.3.3
Inclusion and Exclusion Criteria
During the review process, the studies were checked to satisfy the following inclusion criteria:
• Availability online to ensure paper accessibility
• Focus on biophysical signals and especially brain activity
• Focus on measuring the level of attention or stress to ensure its compliance
with the study
• Format of the research paper (thesis, papers, posts, books, videos, etc.)
• Methods description and approaches of brain activity and biophysical
signal analysis
• Focus on studies of work environment
• Written in English
The studies of the following topics were excluded from further processing:
• Papers which do not meet any of the inclusion criteria.
2.4 Results
12
• Similar papers, which are written by the same authors or describing the
same concepts.
2.3.4
Data Collection
The data elicited from the reviewed materials were:
• The main area of the research
• The research question/questions of the study
• The authors of the research
• The summary of the research
• The gaps in the research and the areas of further studies
2.4
Results
2.4.1
RQ1: How biophysical signals were used in determining the conditions of human activity or work
Literature analysis shows that conditions of human activity or work can
be slipped into three parts: measuring attention, stress detection, and tracking
programmers’ activity.
Measuring attention
A study by [18] proposes to use EEG signals for Attention Recognition
(AR) and extends previous research that used eye-gaze, face-detection, head
pose and distance from the screen to track user’s attention. AR is a promising
2.4 Results
13
field that can be applied in many areas such as e-learning, driving, and most
relevant - in measuring consciousness during video conferences. In [19] EEG
was used to determine the attention level, while the subject was performing a
learning task. In [20] EEG was used to estimate alertness in real-time. In [21]
presented a single channel wireless EEG device which can track driver’s fatigue
level in real-time on a mobile device such as smartphone or tablet. Measuring
attention is very important in many fields, such as detecting drivers’ drowsiness
and workers’ fatigue.
Analyzing all the previously mentioned studies we gathered the techniques
for attention measurement into a list, which states that attention could be
measured via:
• heart rate variability [22]
• galvanic skin response [22]
• pupil diameter, eye blink frequency [22]
• brain activity measurement (EEG, MEG (Magnetoencephalogram),
fNIRS (functional near-infrared spectroscopy), fMRI (functional magnetic resonance imaging), ECoG (electrocorticogram), etc.) [22], TMS
(transcranial magnetic stimulation), PET (positron emission tomography), NIRS (near-infrared spectroscopy) [23]
• Conner’s Continuous Performance Test (CPT). The method, which is
described in the studies [24] and [23] and performed as following: the
subject has to react when a rare signal appears.
• The test of variables attention (T. O. V. A.) is an objective neuropsychological examination of attention, which is a simple electronical game
2.4 Results
14
which tracks the response of the subjects to a visual or auditory stimulus.
[23] [20], [25] [20]
Stress detection
Several studies presented designs of the systems that monitor the human
physical and mental state in the working environment. Using different biophysical signals and environmental measures, they detect stress levels of employees.
A new apрaratus [26] was designed to assess the stress levels of callcentre operators. The study uses two types of sensors to monitor the working
environment: environmental and physiological. The evaluation of stress relies
more on the latter signals. 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 [27] measures biophysical signals in
real-time and notifies unwanted mental behaviour.
The notifications are
sent in case the following conditions in the worker are detected: 1) absentminded/inattentive, 2) stressed, 3) extreme fear, 4) anger, 5) stun/daze, 6)
overloaded with work, 7) drowsiness, and 8) dizziness. The author 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 physical state and
increase productivity and well being at work. However, the range of biological
signals collected was significantly broader than in [26] and brain, and muscle
activity analysis was used.
The device proposed in [28] determines the relaxation level of the user. It
consists of the Virtual reality headset and the olfactory necklace. The necklace
changes the intensity of aroma, depending on the subjects’ EEG datagrams.
2.4 Results
15
In [29], mental stress was measured while solving arithmetic tasks. The
[30] detected the difficulty of program comprehension tasks among the students.
The [31] 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 [27] [26];
• galvanic skin resistance [26] – showed that increasing skin conductance
indicates the rise of stress level;
• body temperature [27];
• blood pressure [27] - 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 [27] [28] [30] [30] [32] [33] [34] [35] [36] [37] [38] [31];
• EMG [27];
2.4.2
RQ2: In the framework of RQ1, how EEG was
used?
The study conducted by [27] relies on the concept of neuroergonomics
design, and especially aspects like stress, attention, drowsiness, and others to
design efficient systems to be used by humans. To measure these metrics,
several methods are used in neuroergonomics, but one of the most relevant is
neuroimaging. The authors of the study designed a system that keeps live feed
about human’s psychophysiological information and used EEG as their primary
method to measure brain activity.
2.4 Results
16
They designed a simple BCI’s where one dry EEG electrode sensor is
placed on the forehead. The authors use a high-pass filter and a low-pass filter
to clean the noise at low/high frequencies and a notch filter to filter specific
bands of the signal. After passing all filters and amplification, the resulting
signal is then converted to digital format. The study shows how collecting
EEG data can help in creating effective and comprehensive BCI systems to
monitor behaviour and well-being at work.
Another research from [18] investigates how EEG can extend the techniques for AR. Previously EEG was used mainly for emotion recognition. This
study mostly focuses on the methods of EEG data processing, feature extraction, and further attention classification. The EEG data were collected while
subjects were reading or watching random content. After finishing each subject
filled a self-assessment form. Later, based on the gained results, the data were
divided into five classes and preprocessed. The classification algorithms were
then applied to the acquired data. By doing so, authors propose to use EEG
data for AR and probably supplement the techniques used previously in these
kinds of studies.
In [20] study, EEG signals are used to estimate the alertness level by
recording the response time for the Test Of Variables Of Attention (TOVA)
and the EEG signals in parallel. The correlation between those two measures
was then studied. The results of the experiment show that EEG can be used
in real-time systems that estimate human alertness.
In [28] researchers conducted the experiments: 5 min control experiment
and 5 min with VR headset and olfactory necklace (with lavender aroma),
where the 360 degrees beach was shown to test subject. EEG was recorded
using commercial Muse headband. It provides four flexible electrodes located
2.4 Results
17
at 10-20 positions TP9, AF7, AF8 and TP10 with reference for Fpz. After
the experiment, a test subject filled in a questionnaire. The authors showed
that there is 25% boost of actual relaxation and 26.1% per cent boost with
questionnaire study.
In [29] the test subject filled the demographic form PSS. Then 10 seconds
of a calm picture was shown in the beginning and at the end of the experiment. After that subject was asked to solve 10 arithmetic questions. After the
experiment test subject was asked to determine highly stress stages, namely
before, during or after the mental induced task. EEG was used to record data
from the test subject. The Mindset 24 Topographic Neuro Mapping Instrument
by Nolan Computer Systems LLC was used. The 10-20 recording system was
used, namely: Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz,
P4, T6, O1 and O2. Sampling frequency: 256 Hz, impedance <5 kOm, cutoff
range: -80 to 80
In [30] a relax screen was shown to test subjects, then they solved 3
practice questions and 9 ordinary questions using TPS. Between each question,
the relaxing screen was showed to take a break. EEG was used to record the
programming activity. Emotiv Epoc device with 16 channels: 14+2 reference,
following the 10-20 international system. Sampling frequency: 128 Hz.
In [31] the subject completed the 90 minutes simulated driving. The
subjects obliged to lie on a bed if they feel sleepy. The EEG was recorded with
Neuroscan with 64 electrodes, 2 of them were EOG.
In [39] the impact of in-vehicle secondary tasks on driver mental state
while driving was measured. This was done by capturing the changes in EEG
waves. Authors used a portative data acquisition platform to collect wireless
EEG data from six subjects during a driving session and found six potentially
2.4 Results
18
distracting scenarious.
In [40] shown the growth of mental fatigue in a Stroop task with a help
of EEG using independent component analysis (ICA) approach. Particularly,
they studied mental effort and mental engagement by the continuous frequencies
changes from frontal independent component (IC) related to cognitive control
and posterior ICs related to attention.
2.4.3
RQ3: What kind of analysis of EEG results was
used?
This section describes the main techniques to analyze EEG data. It starts
with Machine Learning Techniques and proceeds with others. A lot of the
papers used Machine Learning techniques for analysis of EEG data. They can
be divided into three groups: Neural Networks, Classification Algorithms, and
Other techniques.
Machine Learning Techniques
Nowadays, Neural Networks is a widely used machine learning approach.
In [41] study, authors train an MLP NN to learn characteristics of EEG that
define attention state. The main goal of this study was to investigate the hidden
nature of attention mechanisms while recording the subject’s EEG data. The
novelty of the proposed method is in using four levels of attention instead of
2, as was used in previous studies. Also, the authors emphasize the fact that
identifying an attentive state is easier than inattentive because of more noise and
irrelevant information recorded during the inattentive state. The [42] research
focuses on the continuous detection of changes in human alertness and EEG
2.4 Results
19
power spectrum on a minute time scale. Authors emphasize the variability of
EEG dynamics and say that group statistics used in previous studies cannot be
used effectively. So information collected from each operator is then fed to a
neural network to adapt to individual differences in EEG dynamics. The results
are then compared to linear models. A novel approach was described in [35].
The authors used Convolutional Neural Network as a feature extractor. The
data was preprocessed, first, by statistical indicators, to remove points with the
subject’s score standard deviation of more than 2 times the mean. Then two
feature vectors were built: linear by Pearson’s coefficients and nonlinear by SL
matrix. Then, linear and nonlinear features are fused by the CNN framework.
The more classical approach is K-nearest neighbours. In [18] the authors
of the study introduce EEG measures to track emotions and attention. The
project applies classification algorithms to the EEG signals, and k-NN was one
of them. After extracting 13 important features, a k-NN classifier was used
to divide the data into both 3 and 5 attention classes. The [43] shows how
to detect driving fatigue based on k-NN and the correlation coefficient of the
subject’s Attention and Meditation. Naive Bayes classification is also a widely
used machine learning algorithm. In [30] authors measured task difficulty. EEG
data were first normalized by computing the mean on all channels and subtracting it from each channel for each subject. Then filtering was done on 1-second
segments by Elliptic Infinite Impulse Response filtered described by Manoilov.
Then four types of features were extracted: Energy, Event-Related Desynchronization, Frequency ratio, and Asymmetry ratio. After that, the Naive Bayes
classifier was used to classify the data from each feature vector.
2.4 Results
20
Other methods
There are several techniques worth mentioning, for example, P300. P300
(also called P3) wave [44] is an endogenous potential that surfaces itself as a
positive deflection in the voltage with an average latency of roughly 250 to 550
ms depending upon the task [45]. It is captured during the process of decision
making and appears after 300 ms of the presence of the stimulus. Authors
concluded that the P300 amplitude significantly decreases while fatigue takes
place.
Another commonly used approach is the Independent Component Analysis
(ICA) [46]. ICA is a widely used method for decomposing multichannel data
into components that are statistically independent (ICs). In the context of EEG
data analysis, some components should represent brain activity, while others
should represent noise resulting from eye and muscle movements.
2.4.4
RQ4: How attention and stress level were studied
during programming?
Several methods for measuring attention and stress level of programmers
have been presented in the literature. A study [47] presents a method for identifying programmers’ stress based on keystroke dynamics. Authors collected data
using special background program while students solved tasks. Two examples
of keystroke data were captured for each subject, the first while programming
in normal conditions without pressure, the second under time pressure and
consequent stress. After data collection, statistical data analysis was applied
to assess the significance of keystroke dynamics differences. Authors concluded
that some of the features might be indicators of stress.
2.4 Results
21
Pupillography method was used in [48] to capture meta-information of the
programmers’ emotional and mental state (whether they experience stress, cognitive workload, level of mental workload, attention, etc.). Authors performed
experiment on 30 professional developers and concluded that the developers’
mental workload and cognitive load indicated by the pupillography is consistent with developers’ own estimation of this parameters and load reported by
the programmers using NASA-TLX task load index.
The relation between pair programming and developers’ attention level
was studied in [49]. Authors collected data using PROM [50] tool, which is
installed at the developer’s computer and records information about the programs used. It does not distract the user from work and thus provides more
precise information about real working scenarios. Authors concluded that pair
programming helps people to focus better on the work. Developers spend more
time for project, less switch the windows while work in pairs.
A study [51] shows how EEG can be applied to understand the mental
activities of programmers during pair programming. Here, a portable multichannel EEG device was used to understand if there is any difference in the
mental processes of the minds of developers when they use different development approaches. The data were collected during several pair programming
sessions where two developers played the role of a "driver" and "navigator"
consecutively. The goal then was to determine whether those activities induce
a higher level of concentration.
Another study [52] in this field compares the cognitive activities of novice
and expert developers and assesses their programming language comprehension.
By conducting an EEG experiment, they showed that indeed there is a clear
difference in how these two groups understand programming languages. There
2.5 Discussion
22
was a higher brain activation in certain electrodes. Expert programmers showed
better short-term memory and comprehension abilities in general.
Analyzing the results, it can be said that the approach of using EEG to
analyze the brain activity of developers is rather effective and practical, as it
can be used in the normal programmers surrounding and show good results in
distinguishing between different brain activity patterns.
It was observed that EEG is one of the most popular and easy ways to
measure people’s attention and stress because of its ease of use and relatively
accurate results.
2.5
Discussion
This section represents different findings of this Systematic Literature re-
view. The Systematic Literature review aimed to identify current progress in
biophysical signal usage in IT and cross fields. From 317 publications, 40 publications have answered the Research questions. The result of the review showed
that in recent years, from 2015 till 2019, there is a high interest in developing
systems with the help of biophysical signals. This study mostly focused on
describing methods for attention, emotion recognition and experimental procedures.
With the recent increased interest in Machine Learning and availability
of such data sets as DEAP [53] and AMIGOS [54], a high number of studies
described Machine Learning methods for attention.
Some studies focused on the feature extraction of data for future usage in
classification algorithms. The development of such methods is a good indicator
of interest in biophysical signal-based systems.
2.6 Conclusion
23
A little number of studies used the programmers as the main experiment subjects. Thus, giving research opportunity to investigate new methods
based on biophysical signals. Such research should help to develop a system of
attention recognition for IT developers, giving industrial companies sufficient
information about the performance of their employees.
Also, the primary data collection method was EEG. Several studies used
other biophysical signals such as EMG, heart rate, blood pressure. The potential combined usage of different biophysical signals is an open question.
2.6
Conclusion
The Systematic literature review clearly shows the high interest in using
EEG based systems for attention and emotion recognition. Mostly, all studies
are developing new techniques in Machine Learning signal processing.
Analysis and processing techniques were separated into different groups
according to the ML method used. Based on the review of the techniques Section 3.3 gives sufficient information regarding data preprocessing and classification methods used. As there is a lack of study on programmers’ performance,
future research should be more focused on this topic, also focusing on metrics
and open systems [55]–[57] and understanding the dynamics of the collaboration
between people [58]. The question: "How can we help programmers perform
better using the biophysical system?" remains open. Researchers may try to
answer this question with the help of methods described in this Systematic
Literature Review.
Chapter 3
Design and Methodology
The chapter firstly presents the background of the experimentation. Secondly, it reports about the experimental methodology used in this work to
understand software developers attention level during programming, discussing
first the existing research, to substantiate the subject and methods of the study,
secondly the sequence of experiments performed, then the data collection process, and finally the data analysis protocol.
3.1
Background
In this section, we will describe the main concepts of the research, paying
attention to different types of brain waves and what is essential to know before
reading about the experimentation. However, we do not describe state of the art
and approaches could be used for the capturing and analysis of the brain since
this subject has been already covered in Chapter 2. This section describes the
different types of brain waves firstly. Then it proceeds with specific patterns,
which are in our interest in the experimentation.
3.1 Background
3.1.1
25
Brain Waves
Delta band (1 - 4 Hz)
Delta band is in the range from 1 to 4 Hz and is the highest and slowest
by its amplitude. Delta waves could be seen during deep non-REM sleep and
correlate with the deepness of sleep. Usually, delta waves could be seen more
in the right hemisphere and generated in the thalamus brain part. Delta waves
help us to consolidate gathered information, so they are essential for long-term
memory and learning new skills [59].
Theta band (4 - 8 Hz)
Theta band waves are in the range from 4 to 8 Hz. Research show [60]
[59] us that the frontal theta waves are correlated to the high level of mental
workload, attention or memorising. The higher level of theta frequencies, the
higher level of the task is [61]. Theta waves usually could be captured from
all over the cortex. Theta is typically used for studying spatial navigation and
monitoring brain activity in operational environments.
Alpha band (8 - 12 Hz)
Alpha band waves are in the range from 8 to 12 Hz were discovered in
1929 by Hans Berger [59]. Alpha frequencies are related to memory, sensor
and motor tasks. It positively correlates with physical relaxation with closed
eyes, so it is studied in research about meditation [62]. On the other hand,
alpha waves are decreased during mental or body activity, so they used as an
indicator of mental workload [63]. The alpha band could be captured from
posterior cortical lobes, such as occipital, parietal and posterior temporal.
3.1 Background
26
Beta Band (12- 25 Hz)
Beta band waves are in the range from 12 to 25 Hz. The beta band
usually correlates with mental concentration and active thinking [59]. Beta
power increases when the subject wants to execute movements and could be
seen in central cortex. Noticeable, it also increases when we observe movements
of other subjects, because of activations in "mirror neuron system" [64]. Beta
frequencies are generated in posterior and frontal regions.
Gamma Band (above 25 Hz)
Gamma band waves are in the range from 25 to 140 Hz. For now, the exact
role of Gamma waves is unclear. Some of the researches report that the gamma
waves do not reflect cognitive processes and are a by-product of processes related
to eye-movement [65] [66] and micro-saccades. On the other hand, other authors
report that gamma waves are correlated with work of memory and attention,
similar to theta [67] [68]. Future research will have to address the role of gamma
in more detail.
3.1.2
Signs of Attention and Mental Workload
Alpha and Theta Waves
[69] In [70] authors analyzed attention using N-back task and observed
that the theta power was increased during difficult task relative to a simple
task, whereas power of alpha band tended to be increased in the simple task
compared to difficult tasks. Similar results reported for also working memory
(WM) task [71], and for more complex cognitive tasks [72]. In [73] authors
found the following correlation: as task difficulty increased, frontal midline
3.1 Background
27
theta EEG activity increased while parietal midline alpha reduced. Authors
of [74] suggested that theta activity is associated with multiple processes, such
as working memory, problem-solving, self-monitoring. They found that many
parts of the brain involved in the activation of theta waves and conclude that
theta band reflects comprehensive functional brain states.
Event-Related Desyncronization
Event-Related Desynchronization (ERD) measures how much neuron
populations no longer synchronously react after being triggered to perform the
given task [75]. More difficult tasks cause bigger ERD difference between resting and working samples. The ERD is equal to the percentage of change of
power band between the resting period before a working sample and the working sample itself. Authors of [76] report the following conclusions:
• Lower alpha band desynchronization indicates an attention
• Upper alpha band desynchronization indicates reflects semantic memory
performance
• Theta band synchronization indicates episodic memory and the processing
of new information
Theta/Beta Ratio
The Theta/Beta ratio (TBR) is a power of the slow theta band divided
by the value of the fast beta frequency band. Authors of [77] and [78] report that TBR negatively correlates with attentional control among healthy
subjects. In [79] authors performed an experiment on twenty-six participants.
3.2 Experimental Design
28
Firstly, baseline EEG was recorded, then subjects had to do a 40-minute breathcounting task while EEG was recorded. Participants pressed the button when
they experienced Mind Wondering (MW) episodes during the session. Authors
concluded in [79] that the frontal TBR correlates with MW, which means a
state of reduced control over thoughts and low level of attention.
3.2
Experimental Design
3.2.1
Purpose
Based on questions stated in section 1.1, the following hypotheses were
derived for RQ2 and RQ3. RQ1 was answered in Chapter 2. The goal of the
current section is to prove or disprove those hypotheses, providing sufficient
justification.
Hypothesis for RQ2:
• Level of attention during programming EEG can be measured using EEG.
Hypothesis for RQ3:
• A relationship between types of mental activity and an attention level
could be investigated using data captured by EEG.
• There is a specific pattern of brain activation during programming which
could be seen in EEG data.
3.2.2
Data Sources
We collected programming dataset by ourselves, as described in section
3.4. To compare programming with another type of mental activity, we decided
3.2 Experimental Design
Characteristic
EEG channels
Reference
Frequency band
Sampling rate
Storage rate
Noise
Input range
29
Value
24
A1, A2, (A1+A2)/2, Cz, REF
0(DC) 70 Hz
2000 Hz
250 Hz
1.2 µV peak-to-peak
±300 µV
Table 3.1: Technical characteristics of EEG Smart BCI cap
to select driving, because both programming and driving require a high level of
attention and concentration, and both of them produce high mental workload.
As a driving dataset, we used data from [80]. This dataset includes original
EEG recordings of twelve healthy persons in two states: driving and resting.
Data collected by a 40-channel Neuroscan amplifier in .cnt format.
3.2.3
Equipment and Tools
EEG Smart BCI cap was used in the experimentation. It is a 24-channel
wireless cap by Mitsar company, which transmits the data using Bluetooth.
Data were cleaned, processed and partially analyzed in the Mitsar EEG studio.
Electrodes were placed according to standard 10-20 scheme [81]. Eighteen scalp
electrodes were used: F7, F3, Fz, F4, F8, Fp1, Fp2, T3, C3, C4, T4, T5, P3,
Pz, P4, T6, O1 and O2 with the reference electrode Cz.
For the analysis, we used MNE 0.19.2 on MacOS, Python 3.7.4 and
Jupyter notebook 6.0.1.
3.3 Participants
30
Figure 3.1: Student during experimentation session
3.3
Participants
To make the data more homogeneous and closer to real life, the following
restrictions on experimental subjects were chosen:
• We invited to take part not novice programmers but software developers
with at least two years experience in different types of projects. This
would help them feel more confidently and not worry during the experiment to avoid noises and unwanted signals.
• Programmers with different levels of experience may develop software
differently, so we needed to have software developers with almost the
same level of expertise.
We decided to invite to participate 10 bachelor students of Innopolis
University because they were the best suited to the object of the study and had
3.3 Participants
31
enough time to take part in the experimentation. Each participant filled the
questionnaire with the following questions:
• Gender
• Age
• Working experience to verify that all participants have almost the same
experience. Three options were given:
– Beginner (less than 1 year of experience)
– Intermediate (1-3 years of experience)
– Advanced (more than 3 years of experience)
After the experiment, we sent to the participants posterior questionnaire
to evaluate their feelings and perceptions about the experiment with the following questions.
• How comfortable it was to wear an EEG device during programming
(from 1 to 10). Question intended to assess the applicability of the EEG
device in real life during programming.
• The most uncomfortable thing in the experiment. Question intended to
improve future experiments.
• How difficult the task was for you: easy, moderate or hard. For most of
the participants, the task was average; for the others, it was easy.
• The feeling about concentration level during solving the task (from 1 to
10).
3.4 Data collection protocol
3.4
Data collection protocol
3.4.1
Experimentation Settings
32
The experiment was conducted at the Innopolis University Library. Before the experiment, each subject answered questions about their music preferences and the programming experience. Programmers were divided into three
groups of experience: Elementary, Intermediate and Advanced that defined the
difficulty of the task given. Subjects solved a given task using their preferred
programming language. The environment was the same for all experiments:
• Time of the day: 12.00 - 18.00
• Time of experiment: 20-30 minutes
• Environment: open space hall in Innopolis University
As this was the first observational study, it was necessary for us to follow
the experiment with strict procedure and rules, so that it would be beneficiary
for our future large scale experiments. EEG was recorded according to the
following procedure:
• Eyes closed: 2 minutes
• Eyes opened: 2 minutes
• Main experiment: test subject solving given task
The time for solving task was different for each person, but they did not
exceed 20 minutes. If the test subject did not solve the task in the given time,
the researcher stopped the experiment.
3.4 Data collection protocol
3.4.2
33
Prepatation Procecedures
Before the experimentation sessions, EEG device was charged, and the
following procedures were carried out:
• Cleaning the device and electrodes with alcohol wipes and cleaning the
electrodes with a special brush
• Putting the cap on the test subject and making it comfortable for the
subject: place ears in special positions, fasten chin closure.
• Filling the electrodes with conductive gel.
• Launching the EEG studio and connect the device via Bluetooth channel
to the computer, synchronize it with the EEG studio
• Choosing the reference electrode (Cz) and montage in EEG studio
• Verification electrodes one by one: checking that the signal is in the
acceptable range and there are no sharp drops or rises of the signal. If
the electrode does not work properly, checking its position and putting
additional gel for a snug fit to the skull if needed.
3.4.3
Experimentation Steps
The steps for the experiment are listed below.
• EEG machine calibration. The calibration is made up of two parts. The
first is when subjects sit in a relaxed state with their eyes closed in front
of the screen and the second is the same but with their eyes opened.
• Subject reads the task (1-3 mins).
3.5 Data analysis protocol
34
• Subjects solved the task (6-15 mins).
• Subject takes off the cap
• Disconnecting and switching off the cap
• Cleaning the device and electrodes with alcohol wipes and cleaning the
electrodes with a special brush to remove gel
3.5
Data analysis protocol
3.5.1
Channel selection
Right channel selection is a trade-off between various parameters, such as
quantity and redundancy of data, cleanness, information from different parts of
scalp etc. For example, frontal electrodes are affected by eye and face muscle
movements. During the processing, we found out that frontal electrodes can not
be cleaned using filters or Individual Component Analysis (ICA) and manual
filtering. To capture valid and clean data, we decided to analyze only central
electrodes (F3, Fz, F4, C3, C4, P3, Pz, P4 with Cz as a reference) since they
provide data which can be used in further analysis.
3.5.2
Data preprocessing
The process of data preprocessing included the following steps:
• Notch filter is used to remove noise from AC lines, which has a frequency
of 50 Hz in Russia.
3.5 Data analysis protocol
35
Figure 3.2: Selected channels
• Calculating the mean signal from the channels showed in Figure 3.2 Applying filters for particular bands (L1A, L2A, UA, Th): high and low pass
filters for frequencies for intervals listed in section 3.5.3.
• manual and automatic artifacts detection
3.5.3
Feature selection
The set of the possibles features was:
• Theta/Beta Ratio
• Changes in Alpha and Theta frequencies
• Comparison of mean values of lower-1 alpha (L1A), lower-2 alpha (L2A),
upper alpha (UA) and Theta (Th) frequencies of samples
TBR is mostly related to the control of visual attention and, mostly studied in
regard to concentration disease, so it is not well-suited goals of current research.
Second feature well suits our needs, because it considers functional state of the
3.5 Data analysis protocol
36
brain during long continuous process [70] [71] [72] [73]. Theta power increasing
during difficult task relative to a simple task, whereas alpha power increase in
the simple task compared to difficult tasks. Theta activity is associated with
multiple processes, such as working memory, problem-solving, self-monitoring
[74], which also is characteristics of the development process.
The third feature also considers the age of the participants. To compute
Alpha band sub-bands, we need to know Peak Alpha Frequency, which depends
on the participant’s age and computes as the following [82]:
IAF = 11.95 − 0.053 ∗ .Age
The sub-bands computed as the following:
• L1A: from IAF - 4Hz to IAF - 2Hz
• L2A: from IAF - 2Hz to IAF
• UA: from IAF to IAF + 2Hz
• Th: from IAF - 6Hz to IAF - 4Hz
This division to sub-bands improves the precision of the results and reflects
different processes in the brain in the following way. The L1A and L2A bands
show an increase in attention, whereas the UA band reflects semantic memory
processes [82]. Increase in power of theta band and decrease in alpha indicates
cognitive and memory performance, according to [83].
Chapter 4
Implementation and Results
4.1
Participants Statistics
During the experimentation, we asked some questions from participants
about their age, education level, programming experience, the results of solving
the task (solved or not), was the task difficult or not. The results are the
following:
• Age: all subjects were 21 years old, except for one who is 22
• Education level: all subject were Bachelor level students of 4th form
• Programming level: distribution shown in figure 4.1.
• Result: 62,5% of participants solved the task
• Assessment of the difficulty of the task: distribution shown in figure 4.2
After the experimentation, we conducted one more survey about the personal feelings of the participants about the EEG, which will help to improve
4.1 Participants Statistics
Figure 4.1: Programming experience of the participants
Figure 4.2: Difficulty of the task for the participants
38
4.1 Participants Statistics
39
Figure 4.3: Level of the comfort of participants
Figure 4.4: Level of the focus of participants
experimentation process in future and to investigate the applicability of the
EEG in industrial programming. The questions and were the following:
• Level of the comfort of wearing EEG device The answer is presented in
figure 4.3
• The most uncomfortable thing while wearing the EEG device. Participants noted factors such as liquid gel on the head and the need to wash
hair after the experiment, restricted movements (they introduce noise),
the difficulty of putting a cap on the head, the difficulty of putting on
glasses while capturing EEG.
• Level of focus while solving a problem. Results are presented in figure 4.4
4.2 Data Capturing
4.2
40
Data Capturing
For the data collection, we used EEG studio by Mitsar. This is a collection
of tools, which includes database handler, Acquisition and Analysis modules.
The user interface of EEG Studio is presented in Figure 4.5. Main advantages
of EEG Studio are:
• It developed by the same designed by the same manufacturer as the EEG
cap, so it perfectly interoperates with Mitsar EEG cap from the box
• It allows selecting reference electrode
• It supports export to many formats, which was very handy because some
part of processing was done in MNE tool
• It has a user-friendly interface and allows to apply filters, visualise data
and automatically detect artifacts
4.3
Data Processing
4.3.1
Channels Selection
EEG device is very sensitive to movements; that is why frontal electrodes
are subject to distortion due to eye and face movements. People have different
structures of scalps, so EEG cap does not perfectly suit everyone, and occipital
electrodes in some subjects it does not fit snugly, especially in those with thick
hair. These factors became reasons to exclude some electrodes from consideration. Selected channels were listed in section 3.5.1. Table 4.1 shows the list of
electrodes with information about inclusion or the reason for exclusion.
4.3 Data Processing
Channel
Fp1
Fp2
F7
F3
Fz
F4
F8
T3
C3
C4
T4
T5
P3
Pz
P4
T6
O1
O2
Cz
Included / The reason of exclusion
Excluded due to eye and muscle movement
Excluded due to eye and muscle movement
Excluded due to eye and muscle movement
Included
Included
Included
Excluded due to eye and muscle movement
Poor contact between occipital electrodes and
skin for some participants due to skull structure
Included
Included
Poor contact between occipital electrodes and
skin for some participants due to skull structure
Poor contact between occipital electrodes and
skin for some participants due to skull structure
Included
Included
Included
Poor contact between occipital electrodes and
skin for some participants due to skull structure
Poor contact between occipital electrodes and
skin for some participants due to skull structure
Poor contact between occipital electrodes and
skin for some participants due to skull structure
Included as a reference
Table 4.1: Included and excludes electrodes
41
4.3 Data Processing
42
Figure 4.5: Mitsar EEG Studio
4.3.2
Data Preprocessing
Artifact detection
For artifacts detection, we used a manual method and automatic one,
provided by MNE studio. Example of the manual method was shown in Figure
4.7. Automatic artefact detection in MNE was configured as follows: all data
which amplitude changed for more than 200µV in 200ms were considered as
a doubtful region and excluded it with a ±200ms around it. The example of
noisy data cleaned by MNE is shown in Figure 4.6.
Filtering the data
EEG device is intended to capture tiny electrical activity oscillations of
the brain. That is why it is susceptible to noises from the environment, such
4.3 Data Processing
Figure 4.6: Automatic artifacts detection
Figure 4.7: Example of artifact in data
43
4.3 Data Processing
44
as AC lines, muscle movements, Wi-Fi and Bluetooth waves, electronic devices.
In current work, we want to study the functional state of the brain in the real
environment in the process of software development, that is why our data is
subject to interference from the outside. To get consistent results, we need to
clear data properly. First of all, we needed to remove noises from AC lines,
which frequency in Russia is 50Hz, which intersects with EEG bands of our
interest. To perform this kind of filtering, we used Notch filter of 50Hz.
raw = mne.io.read_raw_cnt(filename, preload=True)
data = mne.filter.notch_filter(x=raw.get_data(),
Fs=sfreq,
freqs=[50])
To gather the data from bands listed in Section 3.5.3 we used low-pass and
high-pass filters to keep only the range of waves that we are interested in (L1A,
L2A, UA, Th).
experiment_sub_bands[’L1A’] =
mne.filter.filter_data(data=np.mean(data.get_data(), axis=0),
l_freq=IAF_p - 4,
h_freq=IAF_p - 2,
sfreq=sfreq)
4.3.3
Feature Extraction
Levels of Attention
To study levels of Attention, we selected the cleanest samples and then divided samples to halves and compared mean values of Alpha and Theta bands.
4.3 Data Processing
45
Figure 4.8: EEG spectra analysis
For that, first of all, we opened samples in EEG studio, then divided the experimentation part by marking it as two epochs using "Markers" pannel and
"Manual marking" tab. After that, we used the Analysis Wizard to perform
spectral analysis to quantify the amount of oscillatory activity of different frequency bands in the recording. After that, we obtained the table with spectral
values of the corresponding part of the recording for further analysis. We performed these steps for both halves of experimentation for all of the participants.
Then we compared halves, the example of the results is shown in Table 4.2
Analysis of Types of Brain Activity
To compare programming with another type of activity, we decided to
pick mean values of Alpha and Theta bands, dividing theta to three sub-bands:
lower 1 alpha, lower 2 alpha and upper alpha. First of all, we picked channels
4.3 Data Processing
46
Figure 4.9: Analysis Wizard
Participant 1
Theta difference Alpha Difference
F3-Cz
0,27
1,83
Fz-Cz
-1,32
1,02
F4-Cz
1,44
2,86
C3-Cz
-2,01
-0,27
C4-Cz
-0,69
0,94
P3-Cz
-2,22
7,01
Pz-Cz
-2,13
6,13
P4-Cz
-2,69
6,07
Band
Table 4.2: Comparison of mean band values for the beginning and ending of
the session
4.3 Data Processing
47
of interest listed in Section 3.5.3 using the following method from the MNE
library:
data = raw.pick_channels(ch_names=electrodes_list)
Then we computed the average value for each sub-band as it was described
in Section 4.3.2 and averaged it between selected channels. Averaging, in that
case, is acceptable because selected electrodes were from one zone. After that,
we calculated the average value for each sub-band between the subjects from
all of the two mental states: Programming and Driving.
Chapter 5
Analysis and Discussion
This chapter presents the analysis of the obtained results with the discussion of its validity and applications. It starts with a review of the possibility
of attention tracking of programmers, then proceeds with a comparison of the
mental workload between programming and driving in order to discuss the possibility to compare the mental workload between programming and other types
of mental activities.
5.1
Attention Control
During studying the EEG dataset collected by ourselves with samples
recorded during programming, we found an interesting pattern. In the majority of the samples, we found that mean values of Theta and Alpha varied
between the first and second half of the recordings. In particular, Alpha decreased, and Theta increased. The example of data is presented in Figure 5.1
Positive Alpha difference means that in the first half of the recording, the mean
value of the Alpha band was higher than in the second, which means that the
Fatigue was lower and then increased. Negative Theta difference means that in
5.2 Mental Workload types
49
Figure 5.1: Comparison of Alpha and Theta band values for the halves of the
recordings
the first half of the recording mean Theta value was lower than in the second,
which means that the Attention was higher and then decreased. This pattern
could be explained in the following way: during the first half of the experimentation session, the participants felt slightly detached, but by the end of
the experiment they gathered their attention to solving the problem as soon
as possible. This is only a primary assumption, which cannot be considered as
a conclusion. However, with full confidence, we can argue that changes in the
level of attention can be analyzed using EEG. This information can be useful
for improving the quality and productivity of work, as well as building online
systems for controlling the level of attention.
5.2
Mental Workload types
We can see that results for driving lower because data collected for driv-
ing dataset was recorded by another type of device as presented in Table 5.1.
However, we can see that Theta band for both programming and driving is
higher than alpha, which is a signal of a high level of mental concentration.
For programming, the difference between theta and alpha is much higher than
for driving, which means a higher level of mental workload, attention and task
5.2 Mental Workload types
Band
L1A
L2A
UA
Th
Programming
38046083.85489248
30690901.415404692
25460400.87148648
53956243.903502904
50
Driving
1971244.8703139534
1911859.340372675
1866851.002737085
2123524.258705829
Table 5.1: Mean values of bands for different types of mental activity
difficulty, according to [61]. The relative value of UA for driving is higher than
for programming, which is a signal of higher semantic memory process [82].
Chapter 6
Conclusion
Current work aimed to report about methods of detection attention for
programmers. First of all, it reviews current methodologies of studying human
brain biophysical signals. To provide an extensive review of state of the art, a
Systematic Literature Review performed. Then research focuses on investigating the possibility to detect the level of attention while coding using EEG and
identification of correlations between levels and types of mental activity, assuming that the attention level is measured using EEG devices. An experiment was
conducted to investigate these aspects. It was crucial to identify and adhere
to a precise protocol. To compare programming with other mental activities,
we used driving open-source dataset. Then the data was analyzed, and the
following conclusions are drowned. First of all, the level of attention could be
measured using EEG based on the data collected from central electrodes (F3,
Fz, F4, C3, C4, P3, Pz, P4 with Cz as a reference) by tracking in changes of
Alpha and Theta bands. Regarding the comparison types of mental activities,
for programming, the difference between theta and alpha is much higher than
for driving, which means a higher level of mental workload, attention and task
52
difficulty, according to [61]. The relative value of UA for driving is higher than
for programming, which is a signal of higher semantic memory process [82]. As
a result, we can conclude that with the help of EEG, it is possible to study
changes in the level of attention using alpha and theta waves. This fact in the
future allows us to build a system for monitoring the level of attention, which
can be used to improve the quality of work and detect fatigue. It could help to
prevent diseases associated with overwork. A deeper study of specific neuron
activation patterns can be used to build human-machine interfaces.
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