Brain Computer Interface & It's Applications | NeuroSky Minwave | Raspberry Pi

A
PROJECT REPORT
ON
“Brain Computer Interface & its Applications”
Guide- Submitted by-
Mrs. Preeti Gupta Vishal Aditya
Assistant Professor (ASET) B.Tech (CSE, VIII Semester)
AMITY SCHOOL OF ENGINEERING & TECHNOLOGY
AMITY UNIVERSITY RAJASTHAN

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ABSTRACT
Brain Computer Interface is a leading technology in today’s world. In simple words, it
creates an interface between the brain and the machine, allowing the machine to get input from
human brain. This technology has been up in the airs since decades and a lot of work has already
been done, but a lot is also remaining. The main use of this technology is in the field of medical
science. Works are being done in the field of military and skill improvement domains as well.
This technology is growing day by day and is proving to be one of the most important
technologies in the current scenario.
This project shows how Brain Computer Interfaces (BCI) works and how it can be used
in certain applications. EEG (Electroencephalography) was used to read brain waves and then
those waves were analyzed to fetch fruitful results which are used in certain applications. The
applications which have been demonstrated in this project are Brain Controlled Game Simulator,
Brain Controlled Keyboard (for handicapped).

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D E C L A R A T I O N
I hereby declare that the project entitled “Brain Computer Interface & its
Applications” submitted for the partial fulfillment of B. Tech Degree is my
original work and the project has not formed the basis for the award of any degree,
associate ship, fellowship or any other similar titles.
Signature of the Student:
Vishal Aditya
Programme: B. Tech (CSE, VIII Semester)
Place: Jaipur
Date:

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ACKNOWLEDGEMENT
It is indeed a great pleasure and matter of immense satisfaction for us to express our deep
sense profound gratitude towards all the people who have helped, inspired us in our project
work.
First I would like to give my gratitude to Mrs. Preeti Gupta (Assistant Professor, ASET)
for the effort taken by her right from the selection of the project to its completion. She spent her
precious time whenever we were in need of guidance.
Moreover I would like to thank Prof. (Dr.) D.D.Shukla (Director, ASET) and Prof. (Dr.)
Tarun Kumar Sharma (Head Of Department, CSE/IT), who were always there whenever I
needed any support and were a constant source of inspiration for accomplishment of this project.
Vishal Aditya

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Table Of Contents
Title page i
Abstract ii
Declaration iii
Acknowledgment iv
Table of Contents v
List of Figures vii
1 Introduction 1
1.1 Introduction 2
1.2 Aims & Objective 2
1.3 Brain Computer Interface 3
1.3.1 Invasive BCI 3
1.3.2 Non Invasive BCI 4
1.4 Electroencephalography (EEG) 5
2 Reading Brain Waves 7
2.1 Types Of Waves 8
2.1.1 Gamma Waves (31-50 Hz) 8
2.1.2 Beta Waves (13-30 Hz) 8
2.1.3 Alpha Waves (8-12 Hz) 8
2.1.4 Theta Waves (4-7 Hz) 8
2.1.5 Delta Waves (1-3 Hz) 9
2.2 Comparison Between EEG Bands 9
2.3 BCI System 10
2.3.1 Signal Acquisition 11
2.3.2 Feature Extraction 11
2.3.3. Feature Translation 11

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2.3.4 Output Device 11
3 Requirement & Feasibility Analysis 12
3.1 Requirement Analysis 13
3.1.1 Hardware Requirement 13
3.1.2 Software Requirement 13
3.2 Feasibility Analysis 13
3.2.1 Technical Feasibility 13
3.2.2 Economic Feasibility 14
4 Research Methodology 15
4.1 Literature Review 16
4.2 Objective 18
4.3 Algorithm 18
4.3.1 Attention 18
4.3.2 Meditation 18
4.3.3 Blink Detection 19
4.3.4 Mental Effort 19
4.3.5 Familiarity 19
4.3.6 Appreciation 19
4.3.7 Emotional Spectrum 20
4.3.8 Cognitive Preparedness 20
4.3.9 Creativity 20
4.3.10 Alertness 20
5 Working & Applications 21
5.1 EEG & Mindwave 22
5.1.1 The Origin 22

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5.1.2 EEG Signals 24
5.1.3 Clinical EEG 25
5.1.4 Emotiv Epoch EEG 26
5.1.5 NeuroSky Mindwave 27
5.2 How Does The Technology Work ? 30
5.3 Applications Of Our Project 31
5.4 How to Setup/Configure 31
5.4.1 Mind Controlled Robot 32
5.4.1.1 How to Do ? 33
5.4.1.2 What is RaspberryPi ? 33
5.4.1.3 Circuit Connection 34
5.4.1.4 Motor Direction Logic 35
5.4.1.5 Software Setup 35
5.4.2 Brain Keyboard 37
5.4.2.1 How to Setup/Configure 38
5.4.3 Data Visualization 38
5.4.3.1 Python 38
5.4.3.2 Open Vibe 39
5.4.3.3 MATLAB 41
5.4.3.4 Visual Studio 43
6 Conclusion 45
6.1 Conclusion 46
6.1.1 Medical Uses of EEG 46
6.1.2 EEG Technology & Research 47
6.1.3 EEG-Enabled Prosthetics 47
6.1.4 EEG-Enabled Communication Devices 48

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6.1.5 EEG & Education 49
6.1.6 EEG Entertainment 49
7 Future Scope 50
7.1 BCI – Advantages & Disadvantages 51
7.2 EEG – Future Scope 51
7.3 BCI – Future Scope 51
References 53

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List Of Figures
Sr. no. Figure No. Figure Title Page No
1 Figure 1.2.1 Invasive BCI 4
2 Figure 1.2.2 Non Invasive BCI 5
3 Figure 2.2 EEG Bands of Frequencies 9
4 Figure 2.3 BCI System 10
6 Figure 5.1 Brain Game Simulator Setup 22
6 Figure 5.1.1.1 Human Brain Lobes 23
7 Figure 5.1.1.2 Different layers of the brain cortex 23
8 Figure 5.1.2 Example of EEG Bands 24
9 Figure 5.1.3 Common Areas of Bipolar EEG Sensory 26
10 Figure 5.1.4 Emotiv Epoch EEG NeuroHeadset 26
11 Figure 5.1.5 Mindwave EEG NeuroHeadset 27
12 Figure 5.4 Brain Controlled Car Racing Game 32
13 Figure 5.4.1 Mind Controlled Robot 32
14 Figure 5.4.1.1 Mind Controlled Robot – Hardware Setup 33
15 Figure 5.4.1.2 RaspberryPi 33
16 Figure 5.4.1.3 Circuit Connection 34
17 Figure 5.4.1.4 Booting Raspbian/Noobs 35
18 Figure 5.4.1.5 Installing XRDP 36
19 Figure 5.4.2 Brain Keyboard 37
20 Figure 5.4.3.1 Screenshot of Plot in Python 39
21 Figure 5.4.3.2 Acquisition Server & Clients in Open Vibe 40
22 Figure 5.4.3.2.1 Running & Setting-up Acquisition Server 40
23 Figure 5.4.3.2.2 Acquisition Client & Signal Display Connection 41
24 Figure 5.4.3.3 NeuroSky Mindwave and MATLAB 41
24 Figure 5.4.3.4 Raw Value using Visual Studio 44
25 Figure 6.1.1 EEG Testing 46
25 Figure 6.1.4 Stephen Hawking 48
26 Figure 7.1 Future Scope (Robotic Assistance) 52

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27 Figure 7.2 Future Scope (Google Glass & BCI) 53

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Brain Computer Interface & its Applications 1
Chapter 1
Introduction

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1.1 INTRODUCTION
The project titled ‘Brain Computer Interface & its Applications’ is processed and designed
under multiple softwares, MATLAB and Visual Studio. Core programming languages used are
Python and C#. The project aims to deal with creating an interface between human brain and a
computer using Electroencephalography (EEG) technique, a technique to read brain waves using
sensors that read delta, alpha, beta, gamma & theta waves from the brain, amplifies it and gives
the output in form of numerical values to the machine. The users just need to think of movement
in order to drive the system. Several brain scanning techniques like fMRI, PET, or EEG are
capable to analyze what you are thinking, dreaming, or seeing.
EEG device that is used to read the brain waves is NeuroSky Mindwave Headset (MW001).
It comprises of a Wi-Fi dongle and two lobes, frontal lobe for forehead and another lobe that
connects to left ear. In all, the device can detect and give three values which can be used to
develop mind-controlled applications:
 Raw Values
 Attention Values
 Meditation Values
 Eye Blink Strength
1.2 AIMS AND OBJECTIVE
The core objectives which have been designated as fundamental to the project are:
• To acquire EEG signal from BCI technique using NeuroSky Mindwave Headset.
Read the brain waves using the EEG device.
• Filter those Raw Values and understand the basic functionalities of how those brain waves are
giving the particular output as per the reaction of the brain.
Using the raw values obtained gathered as a base, we then used it to fetch Attention, Meditation
and Eye Blink Strengths values.
. • Analyze the values and use them with certain applications to control them by mind, such as
Game Control, Keyboard Control for the disabled and show the use of BCI in Military.
After the Raw Values are fetched and analyzed, we now need to use them in our applications
enabling mind controlled applications.

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1.3 BRAIN COMPUTER INTERFACE
A Brain Computer Interface (BCI) often called a Mind-Machine Interface (MMI) or
sometimes called a Direct Neural Interface (DNI), Synthetic Telepathy Interface (STI) or a Brain
Machine Interface (BMI) is a direct communication pathway between the brain and an external
device. BCIs are often directed at assisting, augmenting or repairing human cognitive or sensory-
motor functions.
Humans’ brain is filled with neurons, individual nerve cells connected to one another by
dendrites and axons. Every action like think, move, feel or remember something make neurons
are at work. That work is carried out by small electric signals that zip from neuron to neuron as
fast as 250 mph. The signals are generated based on the differences in electric potential carried
by ions on the membrane of each neuron. The signals then can be detected, interpreted to what
they mean and use them to direct a device of some purpose. Therefore, BCI is a system that
provides direct interface between the human brain and the computer. In order to develop the BCI
system, the feasible technique should be studied. BCI systems are broadly classified into invasive
and non-invasive techniques.
1.3.1 INVASIVE BCI:
Invasive BCI are Neuroprosthetics where electrode arrays heads are buried within the
brain during neurosurgery and left there on a permanent basis. Invasive devices produce the
highest quality signals of BCI device because they lie in the grey matter of brain. They have by
far the best signal to noise ratio and accuracy of any BCI method. Unfortunately invasive BCI is
costly and require complex surgery to implant. They are require a permanent hole in the skull,
build-up prone to scar-tissue, causing the signal to become weaker or even non-existent, as the
body reacts to a foreign object in the brain. Electrocorticography (ECoG) is one of the invasive
BCI. It also known as partially invasive as the device is implanted inside the skull but rest
outside the brain rather than within the grey matter. ECoG is a very promising intermediate BCI
modality because it has higher spatial resolution, better signal-to-noise ratio, wider frequency
range, and less training requirements than scalp-recorded EEG, and at the same time has lower
technical difficulty, lower clinical risk, and probably superior long-term stability than
intracortical single-neuron recording. This feature profile shows potential for real world
application for people with motor disabilities. Unfortunately ECoG is also costly and required

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dangerous nature of surgeries for such system.
Figure 1.3.1 Invasive BCI
1.3.2 NON INVASIVE BCI:
Non-invasive BCI is the most popular technique where the electrodes need to be placed
outside of the skull or on the scalp. Non-invasive methods are limited in that they are often
susceptible to noise, have worse signal resolution due to distance from the brain, and have
difficulty recording the inner workings of the brain. However they have the advantages that can
combat these difficulties by lower cost, greater portability and the fact that they do not require
any special surgery. Most non-invasive BCI systems use electroencephalogram (EEG) signals.
EEG is the first non-invasive neuron imaging technique discovered which is used for measuring
the electrical activity of the brain. Besides electrical activity, neural activity also produces other
types of signals such as magnetic and metabolic that could be used in a BCI. Magnetic fields can
be recorded by using magnetoencephalography (MEG), while brain metabolic activity which is
reflected in changes in blood flow can be observed by using positron emission tomography

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(PET), functional magnetic resonance imaging (fMRI), and optical imaging [1].
Unfortunately, such alternative techniques require sophisticated devices that can be operated only
in special facilities. Moreover, techniques for measuring blood flow have long latencies and thus
are less appropriate for interaction.
Figure 1.3.2 Non-Invasive BCI
1.4 ELECTROENCEPHALOGRAPHY (EEG):
EEG is the first non-invasive neuron imaging technique discovered which is used for
measuring the electrical activity of the brain. EEG signals are detected from the scalp and contain
noise as a result of electrical interference and movement of electrodes. Applying a large number of
EEG channels may include noisy and redundant signals that degrade the BCI performance and also
involve a prolonged preparation time that directly impacts the convenience in the use of the BCI.
Therefore, selecting the least number of channels that yield the best or required accuracy can
balance both needs for performance and convenience. Due to its ease of use, cost and high
temporal resolution this method is the most widely used one in BCIs nowadays. The advantages of
using EEG technique are:
a) Hardware costs are significantly lower than those of most other techniques.

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b) EEG sensors can be used in more places than fMRI, SPECT, PET, MRS, or MEG, as these
techniques require bulky and immobile equipment. For example, MEG requires equipment
consisting of liquid helium-cooled detectors that can be used only in magnetically shielded rooms,
altogether costing upwards of several million dollars and fMRI requires the use of a 1-ton magnet
in, again, a shielded room.
c) EEG has very high temporal resolution, on the order of milliseconds rather than seconds. EEG is
commonly recorded at sampling rates between 250 and 2000 Hz in clinical and research settings,
but modern EEG data collection systems are capable of recording at sampling rates above 20,000
Hz if desired. MEG and EROS are the only other non-invasive cognitive neuroscience techniques
that acquire data at this level of temporal resolution.
d) EEG is silent, which allows for better study of the responses to auditory stimuli.
e) EEG does not aggravate claustrophobia, unlike fMRI, PET, MRS, SPECT, and sometimes MEG.
f) EEG does not involve exposure to high-intensity (>1 Tesla) magnetic fields, as in some of the
other techniques, especially MRI and MRS. These can cause a variety of undesirable issues with
the data, and also prohibit use of these techniques with participants that have metal implants in
their body, such as metal-containing pacemakers.
g) Extremely non-invasive, unlike ECoG which actually requires electrodes to be placed on the
surface of the brain.
The characteristics of EEG that compare favourably with behavioural testing:
a) EEG can detect covert processing (i.e., processing that does not require a response)
b) EEG can be used in subjects who are incapable of making a motor response.
c) EEG is a powerful tool for tracking brain changes during different phases of life. EEG sleep
analysis can indicate significant aspects of the timing of brain development, including evaluating
adolescent brain maturation.
d) In EEG there is a better understanding of what signal is measured as compared to other research
techniques, i.e. the BOLD response in MRI.

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Chapter 2
Reading Brain Waves

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2.1 TYPES OF WAVES:
You have around 86 billion neurons in your brain which control your whole body and generate
thoughts in your head. These neurons fire with certain frequencies. The EEG headset reads these
frequencies by reading the electrical activity that the neurons produce. Scientists have agreed to
split these frequencies in different frequency bands. All the frequency bands have been studied
intensively and are associated with several states of mind [2]:
 Delta waves (1-3 Hz): Deepest meditation and deep sleep
 Theta waves (4-7 Hz): Normal sleeping and normal meditation
 Alpha waves (8-12 Hz): Relaxation/reflection
 Beta waves (13-30 Hz): Active thinking, focus, hi alert, anxiety
 Gamma waves (31-50 Hz): Conscious perception
2.1.1 GAMMA WAVES (31-50 Hz):
 Too much: Anxiety, high arousal, stress
 Too little: ADHD, depression, learning disabilities
 Optimal: Binding senses, cognition, information processing, learning, perception, REM
sleep
2.1.2 BETA WAVES (13-30 Hz):
 Too much: Adrenaline, anxiety, high arousal, inability to relax, stress
 Too little: ADHD, daydreaming, depression, poor cognition
 Optimal: Conscious focus, memory, problem solving
2.1.3 ALPHA WAVES (8-12 Hz):
 Too much: Daydreaming, inability to focus, too relaxed
 Too little: Anxiety, high stress, insomnia, OCD
 Optimal: Relaxation
2.1.4 THETA WAVES (4-7 Hz):
 Too much: ADHD, depression, hyperactivity, impulsivity, inattentiveness
 Too little: Anxiety, poor emotional awareness, stress

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 Optimal: Creativity, emotional connection, intuition, relaxation
2.1.5 DELTA WAVES (1-3 Hz):
 Too much: Brain injuries, learning problems, inability to think, severe ADHD
 Too little: Inability to rejuvenate body, inability to revitalize the brain, poor sleep
 Optimal: Immune system, natural healing, restorative / deep sleep
2.2 COMPARISON BETWEEN EEG BANDS:
The following is a comparison between the different bands of frequencies of Alpha, Beta
Gamma, Theta and Delta waves:
Figure 2.2 EEG Bands of Frequencies

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All the frequencies are further divided as follows:
 Delta: 1-3Hz
 Theta: 4-7Hz
 Alpha1: 8-9Hz
 Alpha2: 10-12Hz
 Beta1: 13-17Hz
 Beta2: 18-30Hz
 Gamma1: 31-40Hz
 Gamma2: 41-50Hz
2.3 BCI SYSTEM:
A BCI system consists of three components: Signal or Data Acquisition, Signal Processing
(Feature Extraction, Feature Translation), and Output Device. These components are controlled by
a protocol which defines the timing for operation, signal processing details, nature of device
commands and the performance.
Figure 2.3 BCI System

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2.3.1 SIGNAL ACQUISITION:
Signal acquisition in a BCI helps in the measurement of brain signals using a sensor
modality. The sensor is basically a device implanted in the brain usually multi-electrode arrays that
records the signals directly related to the movement. The signals can be amplified to levels suitable
for electronic processing. Also, they can be subjected to filtering to remove electrical noise or other
undesirable signals. After amplification and filtering process, the signals can be digitized and
transmitted to a computer.
2.3.3 FEATURE EXTRACTION:
Feature extraction in Brain Computer Interface (BCI) is the process of analyzing the digital
signals to distinguish signal characteristics and represent them in a compact form suitable for
translation into output commands. These features been extracted should have good correlations
with the users intent.
2.3.3 FEATURE TRANSLATION:
Resulting signal features are passed to the feature translation algorithm, which converts the
features into the commands for the output device (i.e., commands that accomplish the users need).
2.3.4 OUTPUT DEVICE:
The commands from the feature translation algorithm operate the external device of the
Brain Computer Interface (BCI), providing functions such as cursor control, letter selection, robotic
arm operation etc. The device operation then provides feedback to the user finally, thus completing
the closed loop of Brain Computer Interface (BCI).

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Chapter 3
Requirement & Feasibility
Analysis

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3.1 REQUIREMENT ANALYSIS
The requirement gathering process is intensified and focused specifically on input. To
understand the nature of the project to be built, the researcher (“analyst”) must understand the
information domain for the research, as well as required function, behaviour, performance and
interface, Requirement for both the system and the research project are documented and reviewed
with the expertise to bias and due to variance [3].
3.1.1 Hardware Requirement
 Processor: Pentium 4 1.6GHz
 Memory: 1GB
 Hard disk: 200MB
 Bluetooth: Bundled USB dongle
 Keyboard
 Mouse
3.1.2 Software Requirement
 Operating System: Windows XP, Vista, or later.
 Documentation: Any Office Products
 Softwares: MATLAB, Visual Studio
 Languages: Python, C#.
3.2 FEASIBILITY ANALYSIS
All projects are feasible given unlimited resources and infinite time. Unfortunately the
development of a computer-based system is more likely to be plagued by scarcity of resources and
difficult delivery dates. It is both necessary and prudent to evaluate the feasibility of a project at
the earliest possible time. We concentrate our attention on two primary areas of interest.
3.2.1 Technical Feasibility
A study of function, performance and constraints may affect the ability to achieve an
acceptable system. This is the most difficult area to access at this stage of the system development
process. The consideration that are normally attached with this are:
1. Development risk
2. Resource availability
3. Technology
During technical analysis the analyst evaluates technical merits of the system concept, while

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at the same time collecting information about performance, reliability, maintainability and
productibility.
3.2.2 Economic Feasibility
An evaluation of development cost weight against the ultimate income or benefit derived
from the development system. Development of this Research project is economically feasible and
is achievable within the cost limit.

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Chapter 4
Research Methodology

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4.1 LITERATURE REVIEW
Research work on Brain Computer Interface (BCI) began early in the year 1970 itself. This
research work was started at the University of California Los Angeles (UCLA) under the
permission from the National Science Foundation after a contract from DARPA. Thanks to the
development of brain imaging technologies like EEG, ECOG fMRI, MEG etc. This is because
they were one of the major milestones which helped the research workers to continue their work
on BCI. At first, BCI work was carried on many animals and today it is tested on human beings
too. Today, one of the major goal of BCI research is to develop applications that helps disabled
people (suffering from brainstem stroke, spinal cord injury, blindness etc.) to communicate and
interact with people and external environments.
Recently, brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs) have
been shown to achieve remarkable communication speeds. As they use electroencephalography
(EEG) as non-invasive method for recording neural signals, the application of gel-based EEG is
time-consuming and cumbersome. In order to achieve a more user-friendly system, this work
explores the usability of dry EEG electrodes with a VEP-based BCI. While the results show a high
variability between subjects, they also show that communication speeds of more than 100 bit/min
are possible using dry EEG electrodes. To reduce performance variability and deal with the lower
signal-to-noise ratio of the dry EEG electrodes, an averaging method and a dynamic stopping
method were introduced to the BCI system. Those changes were shown to improve performance
significantly, leading to an average classification accuracy of 76% with an average
communication speed of 46 bit/min, which is equivalent to a writing speed of 8.8 error-free letters
per minute. Although the BCI system works substantially better with gel-based EEG, dry EEG
electrodes are more user-friendly and still allow high-speed BCI communication.
A Brain-Computer Interface (BCI) is a device that allows to control a computer by brain
activity only, without the need for muscle control. Its main purpose is to restore or improve
communication for paralysed patients. While there are different kinds of BCI that either rely on
motor imagery or the detection of event-related potentials like the P300, BCI systems based on
visual evoked potentials (VEPs) are currently the fastest method to establish non-invasive BCI
control. Using code-modulated visual evoked potentials (c-VEPs), it was shown that participants

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could spell more than 20 error-free characters per minute (> 140 bit/min). When using steady state
visual evoked potentials (SSVEPs), the communication speed can further be improved by using
frequency and phase information of the SSVEP to reach communication speeds up to 60
characters per minute (> 300 bit/min).
While these results show that VEP-based BCIs allow for high-speed communication in a
lab environment, there are only few efforts to transfer those systems out of the lab and make them
useable for a broader audience. One of those few approaches is the Tübingen c-VEP BCI, which
was previously shown to reach spelling speeds of more than 20 error-free letters per minute and in
another work was improved to control mouse and keyboard of the Windows®operating system,
thereby enabling a user to completely control a computer and use arbitrary applications by means
of his brain activity only. While this is an important step towards improved useability of a BCI
system, the use of gel-based EEG devices still limits the applicability, as gel-based EEG is time-
consuming to apply, cannot be applied by the user itself, and “few people are ready to wash their
hair each time they wish to use Word or a LaTeX editor”.
To solve this problem, dry EEG electrodes have been developed that are quick to setup and
allow to measure EEG without the need for conductive gel. While there are a variety of different
dry electrodes being developed, there are also commercially available dry EEG electrodes that
deliver good results and have been shown to work for BCI applications using P300, SSVEP and
motor imagery. Further, there are consumer-grade EEG headsets with dry electrodes or easy to
apply wet electrodes that don’t need the application of conductive gel like the Emotiv EPOC.
While such systems have also been used for BCI applications, the signal quality of such EEG
hardware is not comparable to professional devices and their use is not recommended for serious
applications.
A good comparison of different dry EEG electrodes developed and tested can be found in
Baek et al. who review and discuss several publications. They conclude that dry electrodes allow
the acquisition of spontaneous EEG signals with lower signal quality than gel-based EEG
electrodes and that movement artefacts are a larger problem in dry electrodes. Nevertheless, signal
quality is good enough to record P300 and SSVEP response to use them for BCI applications.
While dry electrodes haven’t been tested with a c-VEP BCI system so far, the aim of this study
was to test if commercially available medical-grade dry EEG electrodes can be used with a high-
speed c-VEP BCI and what algorithmic improvements of the system are necessary to deal with the
increased signal-to-noise ratio of dry EEG electrodes [4.1].

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4.2 OBJECTIVE
The objective of this assignment is to extract and then analyze the Raw Values obtained by
EEG.
4.3 ALGORITHM
NeuroSky algorithms provide the foundation of a universe of applications that can be built to
optimize brain health, education, alertness and overall function. General algorithm is as follows:
1. First, for the raw EEG signals, first you need to apply pre-processing tasks like feature
extraction. Various methods are available for the same like VLC methods, PCA or wavelets
etc
2. Then apply classification methods over the transformed data.
3. Generally if you apply classification algorithms directly on the raw data, then it does not
yield good accuracy and good models.
NeuroSky provides inbuilt algorithms that are used to record certain types of activities [4.2].
4.3.1 Attention:
The Attention Meter algorithm indicates the intensity of mental “focus” or “attention.” The
value ranges from 0 to 100. The attention level increases when a user focuses on a single thought or
an external object, and decreases when distracted. Users can observe their ability to concentrate
using the algorithm. In educational settings, attention to lesson plans can be tracked to measure
their effectiveness in engaging students. In gaming, attention has been used to create “push”
control over virtual objects.
4.3.2 Meditation:
The Meditation Meter algorithm indicates the level of mental “calmness” or “relaxation.”
The value ranges from 0 to 100, and increases when users relax the mind and decreases when they
are uneasy or stressed. The Meditation Meter quantifies the ability to find an inner state of
mindfulness, and can thus help users learn how to self correct and find inner balance to overcome
the stresses of everyday life. The algorithm is also used in a variety of game-design controls.

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4.3.3 Blink Detection:
The Blink Detection algorithm signals a user’s blinks. A higher number indicates a
“stronger” blink, while a smaller number indicates a “lighter” or “weaker” blink. The frequency of
blinking is often correlated with nervousness or fatigue. Eye blinks are akin to a standard on/off
binary system and therefore are valuable for controls that require definitive responses. For instance,
in communication applications, one blink means no, two mean yes — giving individuals with a
special needs a simple way to communicate [5].
4.3.4 Mental Effort:
The Mental Effort algorithm measures the mental workload while performing a task. The
harder a user’s brain works on a task, the higher the value. The algorithm works well with both
physical (e.g., drawing) and mental (e.g., reciting) tasks, and can be used for continuous real-time
tracking and between-trial comparisons to measure the effects of multi-tasking, workload
variability, and more. The algorithm can be used to track the effects of diverse cognitive loads on
the ability to learn and provide feedback for user self-improvement.
4.3.5 Familiarity:
The Familiarity algorithm tracks learning processes to measure the relative level of
understanding, learning, or comfort with a task. It is a measure of the subconscious learning of
procedural (motor) and mental tasks. In some cases, it reflects how well a user is doing with the
task. By observing trends, users can better understand and assess their learning process. It can be
applied to tasks that are physical in nature (e.g., drawing) or mental (e.g., recitation), and enable
data-tracking assessments to gauge learning status.
4.3.6 Appreciation:
The Appreciation algorithm provides real-time measurement of the level of enjoyment or
appreciation a subject feels towards an external audiovisual stimulus. The algorithm allows
moment-by-moment detection of emotions. In marketing applications, it can be used to track and
understand a users level of appreciation, providing insights and strategic direction for marketing
efforts.

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4.3.7 Emotional Spectrum:
Emotional spectrum measures the intensity (low-high) and pleasantness (pleasant/unpleasant) of
emotional activity.
Pleasant/Unpleasant identifies whether a user is having a pleasant or unpleasant emotion. Pleasant
emotions could be happy, serene, relaxed, etc. Unpleasant emotions could be anger, disgust,
depressed, etc.
Intensity measures how strong a user’s emotions are in real time. The emotion could be happy,
angry, calm, or any other emotion. 4 eTensity levels Very Weak – Weak – Strong – Very Strong.
4.3.8 Cognitive Preparedness:
Cognitive Preparedness measures the brain’s capacity for optimal cognitive performance on
a relatively complex task or in other words, the brain’s capacity for higher level cognitive
functions.
4.3.9 Creativity:
Creativity measures the brainwave activities underlying creative cognition. The higher the
Creativity value, the stronger the brainwave activities promoting innovative and creative thinking.
4.3.10 Alertness:
Alertness measures user’s alertness or vigilance level at the moment. High alertness value
indicates you are at a state of focus while low value represents a relaxing state of mind.

Chapter 5
Working & Applications

5.1 EEG – ELECTROENCEPHALOGRAPHY AND MINDWAVE
Figure 5.1: Brain Game Simulator Setup
Our brain has always been fascinating and a lot of researchers are still trying to explore what we
can do if we can capture some meaningful data from brain. It's a simple approach used by
NeuroSky to capture raw data (frequency) that can further be analyzed for various applications.
The working principle of our project is based on earlier discussed eSense Algorithms for
capturing Attention, Meditation & Eye Blink Strengths.
5.1.1 The Origins
EEG or electroencephalography are since Hans Berger in 1929 exposed that the activity
of the brain could be measured from electrodes situated in the human skull. With EEG we could
measure in fact, the functional state of the brain and diagnose some future or actual problems.
This is the most common way to measure injuries in the brain and functional brain disturbances,
but the creation of the signal is not well understood. Different regions of the cortex have
different cytoarchitectures and each region has its own morphological patterns, aspects of

intrinsic organization of the cortex are general. Most of the cortical cells are arranged in the
form of columns, in which the neurons are distributed with the main axes of the dendritic trees
parallel to each other and perpendicular to the cortical surface. This radial orientation is an
important condition for the appearance of powerful dipoles. Figures below lists the parts of
human brain cortex and zones of interest.. These layers are places of specialized cell structures
and within places of different functions and different behaviors in electrical response. An area of
very high activity is, for example, layer IV, which neurons function to distribute information
locally to neurons located in the more superﬁcial (or deeper) layers. Neurons in the superﬁcial
layers receive information from other regions of the cortex. Neurons in layers II, III, V, and VI
serve to output the information from the cortex to deeper structures of the brain [6].
Figure 5.1.1.1: Lobes of the human brain, external cerebrum, midbrain areas such as the dience-
phalon and the hindbrain areas such as the cerebellum, medulla, etc.
Figure 5.1.1.2: Different layers (columns) of the brain cortex.

Pyramidal cells in layers III and V are mainly responsible for the generation of the EEG.
5.1.2 EEG Signals
The EEG signal consists of spontaneous potential ﬂuctuations that also appear without a
sensory input. It seems to be a stochastic signal, but it is also composed of quasi- sinusoidal
rhythms. The synchrony of cerebral rhythms may occur from pacemaker centers in deeper
cortical layers like the thalamus or in sub cortical regions, acting through diffuse synaptic
linkages, reverberatory circuits incorporating axonal pathways with extensive ramiﬁcations, or
electrical coupling of neuronal elements. The range of amplitudes is normally from 10mV to
150mV, when recorded from electrodes attached to the scalp. The EEG signal consists of a
clinical relevant frequency range of 0.5–50 Hz (10).
The most common frequency bands of EEG are the most common way of analysis. This
information can reveal physiological and statistical evidence but each band could vary on people
and animals with is behaviors and metal sanity, age, etc. The most important patterns of human
EEG are described below [7].
Figure 5.1.2: Example of EEG Bands

The phenomena of alpha de-synchronization channel could be used to get the eyes closed/open
detection.
Delta Waves
The appearance of delta waves are common in neonatal and infant EEGs and during in sleep
stages in adult EEGs. If delta EEGs appears by itself in a adult it means cerebral injury
Theta Waves
In the beginning where part of the delta waves, but scientists discovered the importance activity
of these waves. Its region of interaction is between thalamic region and play dominant part in
childhood and infancy. The normal adult waking of theta waves are a few or small amount of
these frequencies observed in drowsiness and sleep. Large amount of theta waves are associated
between different amount of pathologies.
Alpha Waves
These are originated on the posterior half back of the head and are from occipital an parietal
regions. These waves are observed during conditions of awakeness, physical relaxation and
mental inactivity can be blocked by mental activity or an influx of light when eyes are opened.
Beta Waves
Are presented in a healthy adult and the area of formation are in the frontal and central region of
the cortex. Typical voltage of beta waves are less than 30uV. Beta activity increase when the
organism is added with barbiturates, some non barbiturates sedatives and minor tranquilizers. It
also appears during mental activity and tension.
5.1.3 Clinical EEG
The most common EEG uses up to 30 landmarks on the skull using bipolar derivation (two
electrodes on the skull and the difference is the gradient of potential). Unipolar derivation is
done with an electrode or group of electrodes with the active part (activity) and the inactive part
(usually nose or ear). The advantage of unipolar derivation are that the amplitude of each
deﬂection is proportional to the magnitude of the potential change that causes it and the

demonstration of small time differences between the occurrence of a widespread discharge at
several electrode.
Figure 5.1.3: Common areas of bipolar EEG sensory.
5.1.4 Emotiv Epoch EEG
Based on the latest developments in neuro-technology, Emotiv presents a revolutionary
personal interface for human computer interaction. Emotiv EPOC is a high resolution, multi-
channel, wireless NeuroHeadset. The EPOC uses a set of 14 sensors plus 2 references to tune
into electric signals produced by the brain to detect the user’s thoughts, feelings and expressions
in real time. The EPOC connects wirelessly to PCs running Windows or MAC OS X.
Experience the fantasy of controlling and influencing the virtual environment with your mind.
Access applications and play games developed specifically for the EPOC, or use the EmoKey to
connect to current PC games and experience them in a completely new way [8].
If you or any of your 3rd party applications require access to raw EEG data, you will need to
purchase the Emotiv EEG NeuroHeadset.
Figure 5.1.4: Emotiv Epoch EEG NeuroHeadset

5.1.5 NeuroSky Mindwave
Measuring Electroencephalogram (EEG) activity has historically required complex, intimidating
and immovable equipment costing thousands of dollars. NeuroSky is unlocking a new world of
solutions for education and entertainment with our research-grade, mobile, embeddable EEG
biosensor solutions. Precisely accurate, portable and noise-filtering, our EEG biosensors translate
brain activity into action.
Our EEG solution digitizes analog electrical brainwaves to power the user-interface of games,
education and research applications. We amplify and process raw brain signals to deliver concise
input to the device. Our brainwave algorithms, developed by NeuroSky researcher and our
partner universities and research institutions are uncovering new ways to interact with our world.
Figure 5.1.5: Mindwave EEG NeuroHeadset
Both EEGs are good. NeuroSky Mindwave is cheap, easy to hack and useful for develop simple
applications and filters for study brainwave signals. One thing to note is that NeuroSky EEG is
only for develop games and must not be used to study the signals as a medical device, this is not
the goal of this company. The cost of the NeuroSky Mindwave is about 100 USD.
Emotiv EEG is good at a clinical level because gives you a lot of information of regions from F1
to F15, has an gyroscope to sense orientation of the head and a SDK for developer or researcher
in Linux and Windows. The cost of the EEG rounds about 300 USD. But if you need to get raw

data for better analysis you need to buy the complete package of EEG NeuroHeadset, and
software, it’s around 750 USD.
There are two frames that are outputted at variable rates by the device:
AA 04 80 02 00 56 27 AA AA 04 80 02 00 53 2A AA AA 20 02 38 83 18 02 43 EA 00 03 90 00
00 89 00 00 47 00 00 1E 00 00 28 00 00 3B 00 00 27 04 00 05 00 E7 AA AA 04 80 02 00 53 2A
AA AA 04 80 02 00 55 28 AA AA 04 80 02 00 54 29 AA AA 04 80 02 00 54 29 AA
Purple frame is outputted every 512 Hz and is not exchangeable the frequency time of output.
Green frame is outputted every 1 Hz and is not exchangeable the frequency time of output.
The frames contain useful information about raw values and calculated values. The below table
lists the different frames that are output of every frame.
For the 512 Hz frame the information output is:
byte: value // Explanation
[ 0]: 0xAA // [SYNC]
[ 1]: 0xAA // [SYNC]
[ 2]: 0x04 // [PLENGTH] (payload length) of 8 bytes
[ 3]: 0x80 // [RAW_WAVE_VALUE] 16-bit two's-compliment signed value (high-order byte
followed by low-order byte) (-32768 to 32767)
[ 4]: 0x02 // [VLENGHT] (payload variable length) of 'n' bytes
[ 5]: 0x00 // [RAW_HIGH] high order byte of raw data two's compliment signed value
[ 6]: 0x53 // [RAW_LOW] low order byte of raw data two's compliment signed value
[ 7]: 0x2A // [CHKSUM] (1's comp inverse of 8-bit Payload sum)

For the 1 Hz frame the information output is:
byte: value // Explanation
[ 0]: 0xAA // [SYNC]
[ 1]: 0xAA // [SYNC]
[ 2]: 0x20 // [PLENGTH] (payload length) of 32 bytes
[ 3]: 0x02 // [POOR_SIGNAL_QUALITY] (0 to 255)
[ 4]: 0x38 // 56 of 255
[ 5]: 0x83 // [ASIC_EEG_POWER] eight big-endian 3-byte unsigned integer values
representing delta, theta, low-alpha, high-alpha, low-beta, high-beta, low-gamma, and mid-
gamma EEG band power values
[ 6]: 0x18 // upper byte of EEG_POWER_DELTA
[ 7]: 0x02 // middle byte of EEG_POWER_DELTA
[ 8]: 0x43 // lower byte of EEG_POWER_DELTA
[ 9]: 0xEA // upper byte of EEG_POWER_THETA
[10]: 0x00 // middle byte of EEG_POWER_THETA
[11]: 0x03 // lower byte of EEG_POWER_THETA
[12]: 0x90 // upper byte of EEG_POWER_LOW_ALPHA
[13]: 0x00 // middle byte of EEG_POWER_LOW_ALPHA
[14]: 0x00 // lower byte of EEG_POWER_LOW_ALPHA
[15]: 0x89 // upper byte of EEG_POWER_HIGH_ALPHA
[16]: 0x00 // middle byte of EEG_POWER_HIGH_ALPHA
[17]: 0x00 // lower byte of EEG_POWER_HIGH_ALPHA

[18]: 0x47 // upper byte of EEG_POWER_LOW_BETA
[19]: 0x00 // middle byte of EEG_POWER_LOW_BETA
[20]: 0x00 // lower byte of EEG_POWER_LOW_BETA
[21]: 0x1E // upper byte of EEG_POWER_HIGH_BETA
[22]: 0x00 // middle byte of EEG_POWER_HIGH_BETA
[23]: 0x00 // lower byte of EEG_POWER_HIGH_BETA
[24]: 0x28 // upper byte of EEG_POWER_LOW_GAMMA
[25]: 0x00 // middle byte of EEG_POWER_LOW_GAMMA
[26]: 0x3B // lower byte of EEG_POWER_LOW_GAMMA
[27]: 0x00 // upper byte of EEG_POWER_MID_GAMMA
[28]: 0x00 // middle byte of EEG_POWER_MID_GAMMA
[29]: 0x27 // lower byte of EEG_POWER_MID_GAMMA
[31]: 0x04 // [ATTENTION] eSense (0 to 100)
[32]: 0x00 // Attention level
[33]: 0x05 // [MEDITATION] eSense (0 to 100)
[34]: 0x00 // Meditation level
[35]: 0xE7 // [CHKSUM] (1's comp inverse of 8-bit Payload sum)
5.2 HOW DOES THE TECHNOLOGY WORK ?
Brainwaves are tiny electrical impulses released when a neuron fires in the brain.
NeuroSky’s brain-computer interface (BCI) technology works by monitoring these electrical
impulses with a forehead sensor. The neural signals are input into our ThinkGear chip, and
interpreted with our patented Attention and Meditation algorithms. The measured electrical
signals and calculated interpretations are then output as digital messages to the computer, toy, or

mobile device, allowing you to see your brainwaves on the screen, or use your brainwaves to
affect the device’s behavior.
5.3 APPLICATIONS OF OUR PROJECT:
Brain Game Simulator: We are trying to automate the windows games first racing
games controls using attention & meditation level values that we have captured from the frontal
lobe of brain using NeuroSky Mindwave Headset.
# Python Direct X game controller server
# DirectX works with Scancodes for Direct Input
5.4 HOW TO SETUP/CONFIGURE?
 Install python2.7 64-bit/32-bit (recommended) or later.
 Install dependency packages for python
o PyWin32: To automate windows keys using sendkeys & directX programming
o Mindwave: To interface with ThinkGear socket connection & capture data from
NeuroSky Mindwave Headset.
 Check your port number from Device Manager & Edit the line from
“braingamesimulator.py” file:
headset = mindwave.Headset('COM13', 'CC0E')
 Run file “braingamesimulator.py”
 Calibration for individual person can be done by changing threshold values of if-elif
conditions.
 Start any racing game preferred Asphalt8.

Figure 5.4: A brain controlled car racing game
5.4.1 Mind Controlled Robot:
We are trying to control a robot using BCI through the threshold values of attention &
meditation. Further, the data is sent to Raspberry Pi micro-controller through socket connection
over Wireless network & GPIO triggers are sent to drive motors of Robot.
Figure 5.4.1: Mind Controlled Robot

5.4.1.1 How to do?
Hardware Setup:
Figure 5.4.1.1: Mind Controlled Robot – Hardware Setup
5.4.1.2 What is RaspberryPi?
Figure 5.4.1.2: Raspberry Pi

The Raspberry Pi is a low cost, credit-card sized computer that plugs into a computer monitor
or TV, and uses a standard keyboard and mouse. It is a capable little device that enables people
of all ages to explore computing, and to learn how to program in languages like Scratch and
Python. It’s capable of doing everything you’d expect a desktop computer to do, from browsing
the internet and playing high-definition video, to making spreadsheets, word-processing, and
playing games.
Using Python for RaspberryPi.
Python is a wonderful and powerful programming language that's
easy to use (easy to read and write) and with Raspberry Pi lets to
connect projects to the real world. Python syntax is very clean,
with an emphasis on readability and uses standard English
keywords. Start by opening IDLE from the desktop. The easiest
introduction to Python is through IDLE, a Python development environment.
5.4.1.3 Circuit Connection with MOTOR Driver L298n Dual H-Bridge
Figure 5.4.1.3: Circuit Connection

5.4.1.4 Motor Direction Logic
5.4.1.5 Software Setup:
 Boot Raspbian/Noobs in Raspberry Pi.
Figure 5.4.1.4: Booting Raspbian/Noobs

 Install XRDP to enable remote desktop connection.
Figure 5.4.1.5: Installing XRDP
 Install Dependencies: Python2.7, Mindwave, GPIO, PyQt4.
 Start Hotspot from laptop using Connectify or CMD.(for wireless communication)
 Connect to Remote Desktop Connection with the IP scanned for wlan0 of RPi.
 Run the program “robotcontrol.py” to start connecting to NeuroSky Headset & send
signals to drive motors of Robot.
 Calibration: “Try to focus on Robot & increase your attention levels for FORWARD
control & distract yourself to increase your meditation level for REVERSE control.

5.4.2 Brain Keyboard:
This application is an attempt to develop a keyboard interface for physically
handicapped: Lost his/her hand & they can type using virtual keyboard through eye blinks.
Figure 5.4.2: Brain Keyboard
People who have severe movement disorders like Amyotrophic Lateral
Sclerosis (ALS) or Locked in Syndrome, is a condition where a patient is awake and
aware of its surrounding but unable to communicate or perform any action due to
paralysis of almost every voluntary muscles in the body (with the exception of eye
movements and blinking). The people who are suffering from severe Cerebral Palsy
disorder are not able to speak or not understandable enough so they could not
communicate and interact with others.
These people also have severe movement disorder.Imagine these patients having a
fully functional brain trapped within a non-functioning body. The brain of the
patient would be fully conscious and aware of its surroundings, it could think and
process stimuli, but unable to translate thought into action. This is caused by a
gradual degeneration of the nerve cells in the central nervous system that controls
voluntary muscle movement. The application has 26 alphabetical keys with a space
and a delete key. Here, a square selection box scans all the keys one by one in a
sequence. There is a time delay of about 100ms to move the square selection box
from one key to another key. The application takes the input when user blinks his

eye. The eye blinks triggers the current selected key (as shown in the picture) and
the letter gets print on the text box
5.4.2.1 How to setup/configure?
 Install python2.7 64-bit/32-bit (recommended) or later.
 Install dependency packages for python
o PyWin32: To automate windows keys using sendkeys & directX programming
o Mindwave: To interface with ThinkGear socket connection & capture data from
NeuroSky Mindwave Headset.
o Check your port number from Device Manager & Edit the line from
brainkeyboard.py:
headset = mindwave.Headset('COM13', 'CC0E')
 Run & start training your mind & eye blink strength for better controls
5.4.3. Data Visualization:
Capture & Plot RAW Data from NeuroSky Mindwave Headset
Our brain has always been fascinating & lot of researchers are still trying to explore what we can
do if we can capture some meaningful data from brain. It's a simple approach used by NeuroSky
to capture raw data (frequency) that can further be analyzed for various applications.
5.4.3.1 Python:
Dependencies
 Python2.7 or later version
 Packages: PyQtGraph, Matplotlib, PyQt4
 Drivers: ThinkGear Connector

GitHub: https://github.com/vsltech/neuroskymindwavecapture/
Figure 5.4.3.1: Screenshot of Plot in Python
5.4.3.2 OpenVibe:
The purpose of OpenViBE is to get data from the acquisition device through the Acquisition
Server and then send it to one or more clients. This client is usually, for now, the OpenViBE
designer. The Acquisition Server and the clients (Designers) can be either on the same machine
or different machines on the same network, or any combination of these.
The following diagram explains the possibilities:

Figure 5.4.3.2: Acquisition Server and Clients in OpenVibe
Procedure
Run and setup the Acquisition Server
Figure 5.4.3.2.1: Running and setting up the acquisition server in OpenVibe
Run the designer and create your first scenario
Now search for the Signal Display box and drag it into the scenario. This box displays the signal
it gets on the input. Now connect the two boxes. Click on the signal output (pink arrow) on the
Acquisition Client box and drag a connector to the matrix input (green arrow) of the Signal
Display box.

Figure 5.4.3.2.2: Connecting acquisition client with Signal Display
5.4.3.3 MATLAB
Figure 5.4.3.3: NeuroSky Mindwave and MATLAB
MATLAB is a part of the code decode Attention value calculated by e-sense algorithm from
ThinkGear packets
 Matlab Code to Read Eyeblink using Mindwave: This part of the code decode eye blink
value calculated by e-sense algorithm from ThinkGear packets.

 Matlab code to read Meditation using Mindwave: This part of the code decode
meditation value calculated by e-sense algorithm from ThinkGear packets.
 Mind-Attention Control LED using Arduino.
 Blinking led using EEG.
 Interfacing Mindwave with Arduino.
MATLAB Code to read attention values [9]:
Matlab code to read attention using Mindwave:
TG_DATA_ATTENTION = 2;
%Clear Screen
clc;
%Clear Variables
clear all;
%Close figures
close all;
%Preallocate buffer
data_att = zeros(1,256);
%Comport Selection
portnum1 = 7;
%COM Port #
comPortName1 = sprintf('.COM%d', portnum1);
% Baud rate for use with TG_Connect() and TG_SetBaudrate().
TG_BAUD_115200 = 115200;
% Data format for use with TG_Connect() and TG_SetDataFormat().
TG_STREAM_PACKETS = 0;
% Data type that can be requested from TG_GetValue().
TG_DATA_ATTENTION = 2;
%load thinkgear dll
loadlibrary('Thinkgear.dll');
%To display in Command Window
fprintf('Thinkgear.dll loadedn');
%get dll version
dllVersion = calllib('Thinkgear', 'TG_GetDriverVersion');
%To display in command window
fprintf('ThinkGear DLL version: %dn', dllVersion );
% Get a connection ID handle to ThinkGear
connectionId1 = calllib('Thinkgear', 'TG_GetNewConnectionId');

if ( connectionId1 < 0 )
error( sprintf( 'ERROR: TG_GetNewConnectionId() returned %d.n', connectionId1 ) );
end;
% Attempt to connect the connection ID handle to serial port "COM3"
errCode = calllib('Thinkgear', 'TG_Connect', connectionId1,comPortName1,TG_BAUD_115200,
TG_STREAM_PACKETS );
if ( errCode < 0 )
error( sprintf( 'ERROR: TG_Connect() returned %d.n', errCode ) );
end
fprintf( 'Connected. Reading Packets...n' );
i=0;
j=0;
disp('Reading Brainwaves');
figure;
while i < 20
if (calllib('Thinkgear','TG_ReadPackets',connectionId1,1) == 1) %if a packet was read...
if (calllib('Thinkgear','TG_GetValueStatus',connectionId1,TG_DATA_ATTENTION ) ~= 0)
j = j + 1;
i = i + 1;
%Read attention Valus from thinkgear packets
data_att(j) = calllib('Thinkgear','TG_GetValue',connectionId1,TG_DATA_ATTENTION );
disp(data_att(j));
%Plot Graph
plot(data_att);
title('Attention');
%Delay to display graph
pause(1);
end
end
end
disp('Loop Completed')
%Release the comm port
calllib('Thinkgear', 'TG_FreeConnection', connectionId1 );
5.4.3.4 Visual Studio
Similarly, there is the visual studio code as well, which is coded in C#. The same steps
are followed as above but in C#:

Figure 5.4.3.4: Getting Raw Values using C# in Visual Studio

ASET
Chapter 6
Conclusion

ASET
6.1 CONCLUSION:
As we have seen the working of BCI using EEG, it can be used in several modern
applications as follows:
6.1.1 Medical Uses of EEG:
EEG testing is used to diagnose and evaluate various neurological conditions, such as
epilepsy. The monitoring of a patient’s brain waves can give their doctor insight into any
abnormalities in brain function. Other conditions which may be tested for using EEG include
Alzheimer’s disease and narcolepsy.
Figure 6.1.1 EEG Testing
EEG is also used to monitor brain activity when a patient is in a coma. If the coma is natural and
the patient is on complete life support, EEG can detect brain death. If a patient is in a medically
induced coma, EEG is used to determine the appropriate amount of sedation needed to keep the
patient in the coma.
If extensive brain damage is suspected due to physical trauma, drug abuse, or any other factor,
EEG testing can help determine the actual extent of the damage.
EEG cannot measure the performance of the brain beyond its electrical activity, and cannot be used
to determine intelligence or to diagnose most mental health conditions (with the exception of
certain kinds of psychoses which affect electrical activity in the brain) [10].

ASET
Modern EEG tests, unlike the invasive procedures of the 18th and 19th centuries, are completely
painless and safe. No electricity is actually administered to the patient—it is simply measured.
6.1.2 EEG Technology and Research:
EEG technology enables research in a variety of fields, from medicine to marketing. Recent
research into sports-related concussions has used EEG testing to determine the short- and long-term
results of sustaining multiple concussions or other head traumas. EEG has also played an important
role in recent sleep studies, helping researchers learn the true effects of various lengths and
qualities of sleep on the human brain.
EEG testing is also being used in advertising. Researchers who observe eye movement, facial
expressions, and brain waves to determine the effectiveness of various pieces of media are
pioneering a field called neuromarketing, which seeks to use biometrics to better the odds of
advertising success in a world already oversaturated by media.
These are just a few examples of new and growing fields of research enabled by EEG
technology—the measuring of human engagement and brain activity has nearly universal
applications.
6.1.3 EEG-Enabled Prosthetics:
Controlling a prosthetic limb with your mind was the stuff of science fiction until 1999,
when a collaboration between researchers at the MCP Hahnemann School of Medicine in
Philadelphia and Duke University in Durham (North Carolina) reported that they were successful
in getting rats to control a simple robot using the rats’ brain activity alone. The field of
neuroprosthetics is young, but promising.
15 years later, a paraplegic wearing a robotic exoskeleton controlled by his own brain signals
kicked off the 2014 World Cup in Brazil. In 2015, researchers from the University of Houston
developed an algorithm which allowed an amputee to grasp objects using a robotic hand which he
controlled with his own mind.
Early research in the field of neuroprosthetics required more invasive methods of recording brain
signals (much like the early work of Richard Caton and Hans Berger). However, the robotic hand

ASET
at the University of Houston was enabled by EEG alone, signalling a drastic advancement in
neuroprosthetic technology and a new hope for people living with loss of limbs or mobility.
6.1.4 EEG-Enabled Communication Devices:
Most people are familiar with the story of theoretical physicist Stephen Hawking, who lost
the ability to speak after gradually becoming paralyzed due to ALS. His speech deteriorated rapidly
in the late 70s, at which point he required a translator of sorts to communicate clearly with others.
In the 80s, a serious bout of pneumonia necessitated a tracheotomy, which rendered him
completely unable to speak.
Figure 6.1.4 Stephen Hawking
Hawking used a computer program to help him speak for several years, which was fairly high-tech
for the late 1980s and early 90s. In 2005 he began using a program which allowed him to
communicate via computer using only the movement of his cheek muscles, and in 2012 he began
experimenting with brainwave-powered communication devices.
A communication device powered by brain activity alone would restore speech to patients even
with complete paralysis. The technology has yet to be perfected, but new research is promising,
and may one day lead to perfect technology-enabled communication for patients with ALS and
other degenerative illnesses [11].

ASET
6.1.5 EEG and Education:
Learning about how someone’s brain works is an incredible educational tool. The education
system suffers from a lack of resources and understanding, which hurts teachers’ ability to
effectively instruct students who have learning disabilities or simply don’t learn well from
conventional instruction methods.
Some connections have been made between ADHD diagnoses and EEG readings showing
dysregulated cortical electrical activity. While diagnostics are not the responsibility of a teacher,
applications of EEG technology which monitor focus and concentration could lead to a greater
understanding of which educational techniques work best for individual students. Additionally,
educational EEG-enabled games requiring intense focus could help unfocused students train their
brains to concentrate better [12].
6.1.6 EEG Entertainment:
What if you could play video games with your mind? It may not carry the scientific gravity
of neuroprosthetics, but it sure is fun. Neurogaming (are you starting to get the hang of this naming
convention?) is another new tech field—and definitely the most exciting growth area for the
gaming industry—enabled by EEG.
Game development company Crooked Tree Studios used NeuroSky’s EEG technology to
build “Throw Trucks With Your Mind”, the objective of which is probably self-explanatory.
Players focus their thoughts on a single object, and the NeuroSky MindWave Mobile
Headset interprets the electrical activity of their brains to pick up and hurl objects at opponents.
Lat Ware, the creator of “Throw Trucks With Your Mind”, explains that the inspiration for the
game came from his own experience with experimental neurofeedback therapy to treat his
Attention Deficit Disorder. Learning that his brain activity could be fed into and interpreted by a
computer fascinated Ware, eventually leading him to pitch his idea to Johnny Liu, the Director of
NeuroSky’s Developer Program. Ware then raised nearly $50,000 via Kickstarter to get his project
off the ground. The technology to support more applications of neurogaming exists but, until
recently, it was too expensive to appeal to the average consumer of video games. Now that EEG
headsets can be purchased for less than the cost of the newest traditional gaming console,
neurogaming is poised to be the next big thing in video game entertainment [13].

ASET
Chapter 7
Future Scope

ASET
7.1 BCI ADVANTAGES & DISADVANTAGES
Some of the good advantages of BCI are direct communication between brain and devices,
better living for paralyzed people etc. While the disadvantages of BCI are; ill effects in the brain
due to viral attacks, requiring excessive training for proper usage, high cost, slow speed, lack of
better sensor modality, invasive BCIs are risky since it requires neurosurgery etc.
7.2 EEG FUTURESCOPE:
As more exciting developments are made with EEG applications and the cost of hardware
decreases, the future of EEG for device manufacturers seems full of promise. Consumers
increasingly turn to mobile apps and wearable devices (such as MyFitnessPal, Clue, MySugr, orthe
LifeBeat™ fitness tracking wristband) to gain insight into their health and wellness metrics, and
help them meet their personal health goals. As EEG data can be interpreted to measure biometrics
such as focus, attention, mental fatigue, stress, and more, it presents a new opportunity to give
consumers insight into their personal wellness metrics.
As mentioned above, with personal EEG devices becoming affordable for the average consumer,
the neurogaming applications are virtually endless. Augmented and virtual reality games which can
be controlled with the mind may seem like science fiction, but are solidly within the reach of
modern game developers. Combined with other biometrics such as heart rate, pupil dilation, and
facial expression analysis, it seems anything is possible–if you can observe, measure, and interpret
it, you can make a game of it.
7.3 BCI FUTURESCOPE:
As the coming step of technological advancement, many research workers and scientists are
trying to bring out wide variety of BCI applications useful for the society. Coming years, we can
make BCI restore and augment human functions thereby improving quality of living. Some
applications like flying an aeroplane just by thinking, a blind driving a vehicle etc. will be coming
to a reality. In the medical sector, research workers are trying to bring out miniaturized equipments
and introduction of wireless BCI. It’s said that in the coming future, we can replace the robotic

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devices and directly bypass the signals to the nerves in the damaged part of the brain, thereby
allowing the paralyzed patient to move their body. Last but not the least, development in BCI can
bring out drastic and attractive changes to the society [14].
Future applications of BCI:
• Bionic eye
• Transfer the hearing impulse to brain.
• Robotic assistance to old and disabled.
• Real time gaming.
• Use a device similar to Google Glass. Include a BCI device within it.
• Use a BCI bar code reader in a product.
• When BCI bar code reader is read by a Google glass device activate the BCI interface in the
device and record and process the signal on a server level.
Figure 7.1: BCI - Future Scope (Robotic assistance to old and disabled)

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Figure 7.2: BCI - Future Scope (Google Glass and BCI)

REFERENCES
[1] A Brain-Computer Interface for Shared Vehicle Control on TORCS Car Racing Game,
2014 10th International Conference on Natural Computation
[2] NeuroSky SDK for .NET: Development Guide and API Reference.
[3] Flappy Brain - Cambridge Jam Raspberry Pi project.
WEB REFERENCES
[4.1] http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0172400
[4.2] http://neurosky.com/biosensors/eeg-sensor/algorithms/
[5] http://blog.makezine.com/archive/2010/04/tan_le_demos_mind-controller_on_mar.html
[6] https://www.cnet.com/uk/news/moving-objects-with-mattels-brainwave-reading-
mindflex/
[7] http://www.knight-of-pi.org/raspberry-pi-mindcontrol-neurosky-mindwave-as-simple-
eeg-interface/
[8] http://neurosky.com/biosensors/eeg-sensor/algorithms/
[9] https://github.com/BarkleyUS/mindwave-python
[10] http://developer.neurosky.com/docs/doku.php?id=app_notes_and_tutorials
[11] https://www.pantechsolutions.net/blog/list-of-source-codes-available-for-neurosky-
mindwave-mobile/
[12] https://store.neurosky.com/products/pc-developer-tools
[13] http://openvibe.inria.fr/
[14] https://cofindtira.jimdo.com/2017/05/03/drivers-in-labview/

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