Mind-Bot: An EEG Based Mind Controlled Mobile Robot

DOI 10.4010/2015.324
ISSN-2321 -3361 © 2015 IJESC
Research Article
April 2015 Issue
Mind-Bot: An EEG Based Mind Controlled Mobile Robot
Muhammed Hafs P1, Salas K Jose2
Vedavyasa Institute of Technology, Malappuram, Kerala, India
Abstract:
Mind-controlled mobile robot is a robot that uses an EEG-based Brain-Robot Interface (BRI) system that serves as a powerful
aid for severely disabled people in their daily and professional life which does not uses a computer in between user and robot as
in Brain-Computer interface (BCI), particularly to help them move deliberately by translating the brain signals into useful
commands. Robot intelligence includes heart rate measurement, fire detection, collision avoidance and life detection systems for
improving the safety of individuals. Two modes of robot controlling are used; BRI controlling system and wireless Remote
controlling system. The switching of one control method to another or vice versa is possible with a mode selector.
Index Terms: Brain-robot interface (BRI), Brain-Computer interface (BCI), Mind/Brain controlled robot, EEG, Robot
intelligence, Control methods, Doppler Effect.
I. INTRODUCTION
The modern life technologies have contributed a lot of
things to people‟s lives in many ways. Especially the robotic
systems can perform many industrial duties and they are
getting to be increasingly important for some people. Growing
demand of Assistive robots can lead to the idea of robots for
disabled people both in their daily and professional life. In
general, the healthy users can operate the robots with a
traditional input device such as a keyboard, a joystick, or a
mouse. But these devices are difficult to use for elderly or
disabled individuals. In this juncture some special interfaces
like external single switch, tongue drive and eye-tracking
systems have been proposed [1],[2]. Yet, these special
interfaces also do not work for severely disabled people with
illnesses such as multiple sclerosis, strokes or oral cancer.
Brain-robot interfaces (BRIs) have been developed to
address this challenge. People who are partially or fully
paralyzed (e.g., by brainstem stroke or amyotrophic lateral
sclerosis (ALS)) or have other severe motor disabilities can
find BRI as an alternative communication channel. BRIs are
Systems that can bypass customary channels of
communication (i.e., speech and muscles) to provide direct
communication and control between the human brain and
physical devices by converting diverse patterns of brain
activity into commands in real time. Invasive or Noninvasive
mechanism is an option to collect the brain signal for BRIs.
Electrodes may embed on or inside the cortex by surgery is
invasive and without the surgery is Noninvasive. Noninvasive
BRIs can use various brain signals as inputs, such as
electroencephalograms (EEG), blood oxygen level dependent
(BOLD) signals , magnetoencephalogras (MEG), and
deoxyhemoglobin concentrations [3],[4]. Due to the low cost
and convenient use in practice, EEG is the most popular signal
that has been use in BRIs.
The notion of controlling mobile robot or prosthetic
devices just by mere “thinking” (i.e., brain activity) which is
different from manual control has mesmerized researchers
over the last couple of years. A mind-controlled robot is a
robot that uses brain signal for its control via BRIs (hereafter,
brain-controlled robots refer to Mind-Bot: An EEG-based
mind-controlled mobile robots only). A special attention over
mind-controlled mobile robotic systems is necessary than
other mind-controlled devices because mobile robots require
higher safety since they are used to transport very disabled
people. The systems used to develop these kinds of robots
need better performance parallel pronouncing its higher
accuracy.
Robot intelligence includes heart rate measurement,
obstacle/collision avoidance, and fire detection and
pronounces presence of life. All of these are considered as an
additional safety techniques of Mind-Bot.
II.
SYSTEMIC ANALYSIS AND KEY
TECHNIQUES OF MIND-BOT
The significant role of a mind-controlled mobile robot is to
empower the user to access and control the robot to reach the
intented destinations safely and efficiently with his brain
signals. The core technique that is applied to implement mind
controlled robot is the BRI, which translates EEG signal into
user intention and is imperative for any mind-controlled
mobile robot. In addition to BRI, other techniques include 1)
Robot intelligence technique in sensing surrounding
situations. 2) Controlling of robot via remote can also
performed by a mode selection switch [5] which allows the
robot controlled by a person to handle the dangerous situation.
Using this remote control technique Mind-Bot can also
perform as a rescue robot in natural calamities. There are
thousands of peoples who become victims during a natural
disaster. The Mind-Bot used in rescue purpose via remote
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control is an effective way to find out the trapped lives in
disaster area such as area under earth quake and fire.
The Signal acquisition, preprocessing and feature
extractions are the imperative steps involved in the brain
signal analysis. Brain signal is in range of µV (micro volts).
Therefore BRIs require microscopic analysis on signal
processing for its better performance and accuracy. For
achieving this BRIs use an artifact filtering process along with
sophisticated EEG signal amplifier.
A. Mind-Controlled Mobile Robot
The Mind-Bot has four controlled movements that include
forward, backward, left and right side movements. These are
accessed by translating the thinking of user either for these
respective movements or to stop. The block diagram of MindBot includes LPC 2148, ZigBee, EEG amplification, fire
detector, heart rate sensor, survivor detector and object
detector as shown in Fig.1and the Fig.2 depict the working
model of Mind-Bot.
Due to their reduced size and low power expenditure,
LPC2148 are excellent for applications where miniaturization
is a crucial requirement, such as access control and point-ofsale.
The remote control of Mind-Bot and the data transfer such
as heart rate, presence of life, etc are simply possible through
wireless personal area networks (WPANs). ZigBee is a
protocol for high-level communication used to create WPANs
developed from small, low-power digital radios and it is an
IEEE 802.15.4 standard. The technology of ZigBee is
specifically designed to be simpler and cost effective than
other wireless personal area network such as Wi-Fi or
Bluetooth. All these specialties make ZigBee as more suitable
for our application.
Fig. 1. Block diagram of Mind-Bot.
In this era of Internet and technology, microcontrollers are
used to accelerate machine-to-machine (M2M) and brain-tomachine (B2M) communications. The central processing unit
of the robot can be ARM (Advanced RISC Machine)
processor.
The LPC2148 microcontroller is a 32bit
ARM7TDMI-S CPU with embedded trace supports and realtime emulation that incorporates the microcontroller with
64kB and 512kB of high speed embedded flash memory. A
128-bit wide memory interface and rare accelerator
architecture permits 32 bit code execution at the ultimate
clock rate. An alternative 16-bit Thumb mode reduces critical
codes by more than 30 % with minimal achievement penalty.
Fig. 2. Working model of Mind-Bot.
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i) Electroencephalogram (EEG) and EEG Amplifier
The signal measured and monitored from a biological
being is referred as „bio signal‟. The electrical potential
difference across a resting cell membrane of varied types of
cells in different systems of the body is reffered as Resting
Membrane Potential (RMP). RMP of Nerve fiber is –90mV
and that of muscle fiber is –70mV. The transmission of signals
is the conduction of Action Potential after the membrane
depolarization. Neuro-signal (nerve impulse) transmission
takes place in the nervous system through axons and dendrites
of nerve fiber. The impulses can travel from Central Nervous
System (Brain and Spinal Cord) to peripheral effector organs
or vice-versa. The technique of Electroencephalograph is a
common approach to obtain neuro-signal information of brain.
It is an instrument used to record electrical activity and it
includes electrodes, amplifier and recording device. Very
small silver chloride electrodes are applied on the scalp with
the help of a jelly to acquire the brain signal as shown in
Fig.3.
Fig. 3. International10-20 system seen from left
above the head.
state of these EEG waves are shown in Table 1 and its
graphical forms are shown in Fig.4.
The process of Amplification of an EEG signal carried out
by differential amplifier. EEG signal are very weak and they
can easily be contaminated by other sources. An EEG signal
that does not emanate from brain is called an artifact. Artifacts
can be physiologic or Non-physiologic. The artifact generated
from the human body part (include the heart, eyes, muscle and
tongue) is called physiologic artifacts and those what are
generated from outside of human body are called Nonphysiologic artifacts. The progress of steps involved in the
EEG amplification procedure includes artifact filtering as
mentioned in fig.5.
and
A = ear lobe, C = central, Pg = nasopharyngel, P =
parietal, F = frontal,
Table. 1. Different EEG waves and its mental state.
Fp = frontal polar, O = ocipatal.
The recordings of the electrical activity of the brain from
the scalp is called electroencephalogram (EEG). The recorded
waveforms reflect the electrical activity regarding the cerebral
cortex. There are millions of neurons situated in the brain,
each of which generates tiny electric voltage fields.
Accordingly, EEG is the superposition of many simpler
signals. The intensities of an EEG signal typically ranges from
about 0µV to 200µV in a normal adult, and its frequencies
ranges from once every few seconds to fifty or more per
second.
The important frequencies of the human EEG waves are
Alpha Beta, Theta, and Delta. The frequency range and mental
Fig. 4. Different types of brain waves in the normal
EEG.
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ERD/ERS: Event-related synchronization and eventrelated de-synchronization is based on the energy changes of
EEG signal due to mental task [11].
Fig. 5. Steps of EEG amplification.
ii) Remote control
For ensuring the safety of the user this system provides an
additional type of controlling other than BRI and it is Remote
control, which means robot is controlled by an external
person. The switching of the control system is optional which
is done by a mode selector. Simplified block diagram of
remote control is shown in Fig.6.
Atmel microcontroller is used in the design process of
remote control. These types of microcontroller provide
justified technology, an affluent efficiency in integrated
product designs, and breaking innovation. The technology of
capacitive touch in microcontroller supports to develop
navigators like buttons. Furthermore, Atmel microcontrollers
provide wireless and security support.
The liquid crystal display (LCD) attached to the Atmel
microcontroller is a 16x2 dot matrix display, which deliver us
the instantaneous data and status regarding the individual uses
the Mind-Bot.
Fig. 6.
Remote control of Mind-Bot.
ii) Signal Acquisition and Processing
B. Brain Robot Interface (BRI)
The idea behind the BRI is to translate the electrical signal
of brain into useful commands. The major steps in the BRI
involve signal acquisition and signal processing, where Signal
processing consist of preprocessing, feature extraction and
translation. The schematic of the BRI components are as
shown in Fig.7.
i) Suitable Brain Signals
The P300 (P3), SSVEP and ERS/ERD waves are the most
suitable brain signals that are used to design an EEG based
BRI.
P300: The decision making of a person triggers to generate
a waveform is called P3 wave, therefore it is considered as an
event related potential (ERP) [6]-[8].
SSVEP: The corresponding signals of visual stimulus at
particular frequency (ranging from 3.5 Hz to 75 Hz) is used to
generate electrical activity of brain is treated as Steady state
visual evoked potentials [9], [10].
Fig. 7. Schematic of the main BRI components.
The EEG signals from the scalp are collected using
silver/silver chloride (Ag/AgCl) electrodes are the very cost
effective method. If gel is required between the electrode and
scalp then this kind of electrode is called “wet” electrode. Use
of this electrode needs very long time and it is uncomfortable
to user.
The “dry” electrode address all the problems regarding the
usage of “wet” electrode, which does not need any type of gel
and skin clearing.
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The signal processing includes preprocessing, feature
extraction and translation. Preprocessing helps to avoid the
artifact from the EEG signal. Preprocessing can be possible
with a filter (such as low-pass, high-pass, band-pass and notch
filter) that eliminates the Non-physiological (such as line
noise) and physiological artifacts. On the basis of thinking, the
feature extraction classifies the frequency of preprocessed
brain signal into a particular class of activity, whether it
belongs to forward, backward, right, left, and or to stop.
Finally these frequencies undergo a translation process that
translates them into useful commands or code for accessing
the physical device like Mind-Bot.
C. Mind-Bot Intelligence
Mind-Bot has some additional features like measuring
heart rate, collision/obstacle avoidance, fire detection and the
detecting the presence of living things. These entire features
together called Intelligence of Mind-Bot.
Number of Cardiac cycle (cardiac events that occur from
beginning of one heart beat to the other) or Heart beat per
minute is defined as Heart Rate or Heart Pulse. Physical needs
of body, including the need to absorb oxygen and excrete
carbon dioxide and activities like physical exercise, illness,
and drugs cause abnormalities in heart rate of individuals.
A “Ring sensor” is used to detect the heart rate of the
person who uses the Mind-Bot. The simple arrangement of
infrared (IR) sensor on a jewel which is in the shape of a ring
hence it is called as a ring sensor as shown in Fig.8. The ring
sensor contains both IR receiver and transmitter for counting
the heart rate of the individual and sends the data to the
Remote. Ring sensor counts the heart rate by counting the
blood flow concentration, when heart contracts the blood
concentration at ring is high and it is low for each relaxation
of heart. When blood concentration is high then the
transmitted IR ray doesn‟t receive at receiver therefore it is
treated as a count, such a way the beat per minute (bpm) gives
the heart rate of individual using the Mind-Bot. The normal
resting adult human heart rate ranges from 60-80bpm (i.e.,
average 72-75bpm).
The collision avoidance system protects the Mind-Bot
form hitting the obstacle, which has a definite mass and
density. It works on the principle of Doppler Effect in
electromagnetic radiation. The collision avoiding is possible
by using another IR sensor network. These IR sensors are
arranged well on the wheel of the Mind-Bot, therefore they
detect the obstacle and stop the robot or change the direction
of the robot. Thereby collisions are reduced and provide
proper and safety journey of the individuals.
The Mind-Bot is also fitted with a Fire/Flame sensor. It is
important in the robot because, the flame in front of it may not
be considered as an obstacle at times since flame is a form of
energy physically having no mass or density. Hence it
provides the detection of flame near to the Mind-Bot and
reduces the accidents due to fire taking in account that heat
and light produced by the flame.
Fig. 8. Ring sensor.
So many people are killed by natural calamities. It is said
that if survivors were detected and rescued very fast, then the
number of victims might have reduced. There is no end to the
lives lost as the consequence of such disasters as disintegrated
tunnels, landslides and avalanches.
The development of the survivor/life detector was
prompted by an aspiration to help quicken the discovery of
survivors trapped inside collapsed buildings or partially buried
under mud. The life detector utilizes IR waves (i.e., Infrared
waves) of Passive Infrared sensor to detect the survivor.
Only living animals have body temperature. A respective
Passive Infrared (PIR) sensor identifies variations in the
quantity of incident infrared radiation on its surface, which
depending on the body characteristics and temperature of the
gadget. When a gadget, such as a human, passing through the
region, such as a façade. If the temperature at that place
increases from room temperature to the body temperature, and
back again in the view of sensor. The sensor translates the
resulting variations in the infrared radiation and produces
corresponding variations in the voltage at output terminal and
it triggers the detection of survivor. Passive Infrared sensor
module and its working model are shown in Fig.9.
III.
DISCUSSION AND CONCLUSION
Brain-robot interface is a new technology of
communication between human and robot based on the neural
activity generated by brain and independent from the
conventional channels for communication. The BRI is a great
hope of paralyzed people because it supports them to move for
their basic needs without a middle computer as in BCI or an
assistive person.
This technology obviously holds its
simplicity in action from BCI for sure
Here Mind-Bot is used for much application such as
assistive device for disabled individuals and a rescue robot in
disaster area thereby reducing the cost for designing multiple
robots. It is an optimized and customized solution of robots in
human life.
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[7] L. A. Farwell and E. Donchin, “Talking off the top of
your head: To- ward a mental prosthesis utilizing event
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Fig. 9. (a) PIR module and (b) Working model of PIR.
IV.
FUTURE SCOPE
Administration of Global System of Mobile (GSM) instead
of ZigBee can make the controlling range of Mind-Bot
increased from 100 meters to a more accessible distance.
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AUTHOR DETAILS
Muhammed Hafs P B.Tech graduate in Electronics and
Communication Engineering from University of Calicut,
Kerala, India, in 2011, where he is
currently working toward the M.Tech
degree in Embedded System from
University of Calicut, Kerala, India. Area
of interest is in Robotics, Embedded
System, and VLSI.
Salas K Jose currently working as a Assistant professor in
Embedded System, in Vedavyasa Institute of Technology. He
has completed
graduate in ECE from
University of Calicut, Kerala, India and
M.Tech in Electronics and Instrumentation
Engineering From Karunya University,
Coimbatore, Tamil Nadu, India. He has
published 3 international journals.
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mental prosthe- sis: assessing the speed of a P300based brain–computer interface,” IEEE Trans. Neural
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Jun2000.
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