Improving the quality of life for the elderly and disabled people and
giving them the proper care at the right time is one the most important roles
that are to be performed by us being a responsible member of the society.

It’s not
easy for the disabled and elderly people to mobile a mechanical wheelchair,
which many of them normally use for locomotion or movements. Hence there is a
need for designing a wheelchair that provides easy mobility. In this thesis, an
attempt has been made to propose a brain controlled wheelchair, which uses the
captured signals from the brain and processes it to control the wheelchair.

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(EEG) technique deploys an electrode cap that is placed on the user’s scalp for
the acquisition of the EEG signals which are captured and translated into
movement commands by the arduino microcontroller which in turn move the

measuring brain waves it delivers to brain to computer interface unit which
analyzed and amplified and classify waves into alpha, beta, gamma, waves then
arduino microcontroller controls the speed of the wheelchair and the
accelerometer provides direction to the wheelchair.


Keywords—Microcontroller, Electroencephalogram, Brain
computer interface, Brain signals




electric-powered wheelchair is a wheelchair acting by an electric motor
controlled with a hand-operated joystick. However, some people suffering from
severe motor disabilities cannot use the joystick, such as paralysis and
physically disable people and locked-in syndrome. So they have other special
devices available (touchpad, head /speech control, eye, EEG, etc). With the
objective of responding to numerous mobility problems, various intelligent
wheelchair related research have been created in the last years. In this
research try not only to give mobility to handicapped people but, more
importantly, independently of third party help. Despite these new types of
control methods, can acquire users intention to control the wheelchair.
However, each type of alternative control has its limitations. Wheelchair users are
among the most visible members of the disability community; they experience a
very high level of activity and functional limitation and also have less of
employment opportunities. Elderly people are the group with the highest rates
of both manual and electric wheelchair use.

Wheelchair users report difficulty in basic life
activities, and perceived disability. It’s not easy for the physically
challenged and elderly people to move a mechanical or electric wheelchair. In
recent times there have been a wide range of technologies that help aid the
disabled physically challenged. These control systems are designed to help the
physically challenged specifically. These competitive systems are replacing the
conventional manual assistance systems. The wheelchair too has developed
significantly with a variety of guidance systems alongside like using the
joystick and a touch screen, and systems based on voice recognition. These
systems however are of use to those with a certain amount of upper body
mobility. Those suffering from a greater degree of paralysis may not be able to
use these systems since they require accurate control. To help improve the
lifestyle of the physically challenged further, this research work aims at
developing a wheelchair system that moves in accordance with the signals
obtained from the neurons in the brain through the electroencephalograph(EEG)
electrode.EEG stands for electroencephalogram, a electrode commonly used to
detect electrical activity in the brain. Detecting, recording, and interpreting
“brain waves” began in the late 1800s with the discovery and exploration of
electrical patterns in the brains and the technology has evolved to enable
applications ranging from
the medical detection of neurological disorders to playing games controlled
entirely by the mind.



                                II. RELATED


[1] Creusere et al (2012), “Assessment of
subjective brain wave form quality from EEG brain replies via time space
frequency analysis”, page 2704-2708. Theories give details herein and research
work is the problem of quantifying
changes in the perceived quality of signals by directly measuring the brain
wave responses of human subjects using EEG technique. Ideas taken on from this
research work are that has preferred an approach constructed on time space frequency
analysis of EEG wave form set for detecting different brain disorders.


[2] Jutgla et al (2012)” Diagnosis of Alzheimer’s
disease from EEG by means of synchrony measures in optimized frequency bands”,
page 4266-4267. Theories give 39 details herein research work is the EEG is
considered as a promising diagnostic tool for analysing brain disorders
symptoms because of its non-invasive safe and easy to use properties. EEG has
the potential to complement or replace some of the current tradition diagnostic
techniques. Ideas taken from this research work are EEG datasets of the
patients with different brain disorders symptoms have been collected to
diagnosis the seizures symptoms related to the patients.



 [3] Michalopolous
et al (2011) reported that the Characterization of evoked and induced activity
in EEG and assessment of intertrail variability”, page 978-988. Theories give
details herein research work is the brain reply to an internal or external
experience is poised through the superposition of suggested and persuaded brain
activity which reproduces dissimilar brain mechanisms involved. Caminiti (2010)
reported that the identification of different brain activities through EEG
assessment procedure. Ideas taken from this research work are identifying brain
activities for diagnostic purposes and provide useful tools for brain computer
interfaces through insight on the activation of different brain channels



[4] Duque Grajales J.E., Múnera Perafán A., Trujillo
Cano D., Urrego Higuita D.A., Hernández Valdivieso A.M.(2009),” System for
Processing and Simulation of Brain Signals”, Page 340-345. Theories give
details herein research work has presented the methodology used to develop a
system useful in the simulation of brain signals. It has been described in
detail the procedure in the modelling of EEG signals and insight brain signals
recorded during surgical procedures. Ideas taken from this research work are
processing and simulation of brain signals from different signal processing
models which allows going deep into the study of brain function during sleeping
and pathological situations and facilitated the assessment of the effect of
different drugs in different brain disorders


[5] Sosa et al (2011) reported in theories give
details herein research work is the operational procedures of EEGLAB and
efficiency of EEG signal processing for students and professionals to perform
and analysis of the EEG signals. Its use as a starting point for the comparison
of different brain signal processing algorithms. Ideas taken from this research
work are Capabilities of EEGLAB for diagnosis purpose and basic explanation of
the working procedure of that tool for signal processing such as – loading the
dataset, plotting techniques to get the proper result, etc.


[6] Bhattacharya et al (2011) theories give details
herein research work Presented the information about EEGLAB software for
Brain-computer interface (BCI) is an emerging technology which aims to convey
people’s intentions to the outside world directly from their thoughts. Ideas
taken from this research work are the Feature learning of EEG to the
classification among frequencies in tribunals and within recording locations.
Methods to allow users to remove data channels, artefacts by accepting or
rejecting visually.


[7] Ye Yuan (2010) theories give details herein
research work; EEG dataset is collected after analysing the entire length of
the EEG recording the patient frequently 40 for long time to detect traces of
different human brain activities. Ideas taken from this research work are
change of the structure of different brain activities during seizures is
observed by the change of embedding dimension of EEG signals if the human brain
is considered as a nonlinear dynamic system.




As a communication and control
pathway to directly translate brain activities into computer control signals,
brain-computer interface (BCI) has attracted increasing attention in recent
years from multiple scientific and engineering disciplines as well as from the
public. Offering augmented or repaired sensory-motor functions, it appeals
primarily to people with severe motor disabilities. Furthermore, it provides a
useful test-bed for the development of mathematical methods in brain signal



Figure no.2.1A
conceptual block diagram of overview of BCI System.





An important issue in BCI research is
cursor control, where the objective is to map brain signals to movements of a
cursor on a computer screen. Its potential applications are well beyond “cursor
control”, e.g. it can also be used in BCI-based neuro-prostheses.

Therefore, based on the first report of an EEG-based
system, the authors showed that through guided user training of regulating two
particular EEG rhythms (mu and beta), two independent control signals could be
derived from combinations of the rhythmic powers. The downside of the approach
is with the required intensive user training

A Brain Computer Interface device requires deliberate
conscious thoughts; some thought alone BCI applications includes prosthetic
control, collecting information from never, etc.






Figure no.3.1 Block


Brainwaves are produced by
synchronized electrical pulses from masses of neurons communicating with each
other. Brainwaves are detected using sensors (EEG electrode) placed on the
scalp. They are divided into bandwidths to describe their functions, but are
best thought of as a continuous spectrum of consciousness; from slow, loud and
functional – to fast, subtle, and complex. Our brainwaves change according to
what we are doing and feeling. When slower brainwaves are dominant we can feel
tired, slow, or dreamy. The higher frequencies are dominant when we feel active
or hyper-alert. Brainwaves are complex reflect different aspects when they
occur in different locations in the brain. Brainwave speed is measured in Hertz
(cycles per second) and they are divided into bands of slow, moderate, and fast


Infra low (<0.5HZ) Infra-Low brainwaves (also known as Slow Cortical Potentials), are thought to be the basic cortical rythms that underlie our higher brain functions. Very little is known about infra-low brainwaves. Their slow nature make them difficult to detect and accurately measure, so few studies have been done. They appear to take a major role in brain timing and network function.    Delta Waves (0.5 to 3HZ). Delta brainwaves are slow, loud brainwaves (low frequency and deeply penetrating, like a drum beat). They are generated in deepest meditation and dreamless sleep. Delta waves suspend external awareness and are the source of empathy. Healing and regeneration are stimulated in this state, and that is why deep restorative sleep is so essential to the healing process. Theta Waves (3 to 8HZ). Theta brainwaves occur most often in sleep but are also dominant in deep meditation. Theta is our gateway to learning, memory, and intuition. In theta, our senses are withdrawn from the external world and focused on signals originating from within. It is that twilight state which we normally only experience fleetingly as we wake or drift off to sleep. In theta we are in a dream; vivid imagery, intuition and information beyond our normal conscious awareness. It’s where we hold our ‘stuff’, our fears, troubled history, and nightmares.   Alpha Waves (8 to 12HZ). Alpha brainwaves are dominant during quietly flowing thoughts, and in some meditative states. Alpha is ‘the power of now’, being here, in the present. Alpha is the resting state for the brain. Alpha waves aid overall mental coordination, calmness, alertness, mind/body integration and learning.   Beta Waves (12 to 38 HZ). Beta brainwaves dominate our normal waking state of consciousness when attention is directed towards cognitive tasks and the outside world. Beta is a ‘fast’ activity, present when we are alert, attentive, engaged in problem solving, judgment, decision making, or focused mental activity. Beta brainwaves are further divided into three bands; Lo-Beta (Beta1, 12-15Hz) can be thought of as a 'fast idle', or musing. Beta (Beta2, 15-22Hz) is high engagement or actively figuring something out. Hi-Beta (Beta3, 22-38Hz) is highly complex thought, integrating new experiences, high anxiety, or excitement. Continual high frequency processing is not a very efficient way to run the brain, as it takes a tremendous amount of energy.     Gamma Waves (38 to 42HZ). Gamma brainwaves are the fastest of brain waves (high frequency, like a flute), and relate to simultaneous processing of information from different brain areas. Gamma brainwaves pass information rapidly and quietly. The most subtle of the brainwave frequencies, the mind has to be quiet to access gamma.  Gamma was dismissed as 'spare brain noise' until researchers discovered it was highly active when in states of universal love, altruism, and the ‘higher virtues’. Gamma is also above the frequency of neuronal firing, so how it is generated remains a mystery. It is speculated that gamma rhythms modulate perception and consciousness, and that a greater presence of gamma relates to expanded consciousness and spiritual emergence.   IV.METHODOLOGY Figure no. 4.1Block Diagram A.    Electric Wheelchairs Stop Accidental Rolling Once a manual wheelchair gets rolling, it can be hard to stop. Whether it’s on a ramp or in San Francisco, a manual wheelchair can be prevented from rolling if you put the brakes on but are much harder to stop once they get rolling. Electric wheelchairs, on the other hand, can be slowed down and stopped with just the movement of the accelerometer. The power that makes it go is also an excellent way of making it stop. They’re sturdier Weight can be one of the most daunting aspects of power wheelchairs, especially if you plan to take it anywhere. But that weight can also be a positive. Because the centre of gravity is lower with electric wheelchairs, they’re much more difficult to tip over. This means that they’re more solid when it comes to front-to-back tipping and side-to-side tipping. Weight has its advantages. B.    They Offer Constant Power If you’re at a park and in a manual wheelchair, you or the person pushing you has to take into account the trip back to the starting point. A person who has lot energy at the start can get very tired moving a wheelchair around before too long. That means an exhausting trip back. C.    Electric Wheelchairs Do the Work The most obvious reason that electric wheelchairs beat manual ones is that they do all the work it takes to get someone from place to place. While many people could get themselves around with a manual wheelchair, there are some hills and inclines that are hard for just about everyone. Of course, there are quite a few people who simply don’t have the arm strength or ability to use their hands that it takes to work a manual wheelchair. Without a motorized wheelchair, they would always require someone to move them around, while an electric wheelchair gives them freedom that wheelchair-bound people from 30 years ago couldn’t experience. The paper is an essential need for disabled person to remove assistance. The paper implements a robot whose speed is controlled by the concentration level and the directions are given by a accelerometer basically it is a need of a disabled person to move around different places.  This robot also avoid accidents in panic situations due to the use of concentration level as in panic situation the concentration level drops down to zero so is the speed of the motor drops down zero.  This will help the person to independently move around without having a help or assistance of any different individual. V. RESULTS AND DISCUSSION   We performed a survey to obtain the concentration levels of handicap people and normal people, in order to determine the   threshold value of the concentration that will in turn help to provide the variation in the speed of the motors As we move further with the survey it has being determined that the concentration level of a handicapped person is 60-75% whereas for a normal person the concentration level varies between 70-85%.     Figure no. 5.1 Results of the survey carried out for the concentration levels   Table no. 5.2 Results of the survey carried out for the concentration levels   Number of test Normal Handicap Test 1 92 65 Test  2 68 78 Test 3 82 69 Test 4 78 95         Figure No. 5.3 Concentration Level is 57     Figure No. 5.4 Concentration Level is 57     Figure No. 5.4 Concentration Level is 48       VI. CONCLUSION    In this paper we have described our application and designed a wheelchair which is fully automated and controlled using Beta wave (human brain attention) of Mind wave sensor which is detected from brain signal. It uses Arduino to control wheelchair. The mind-wave mobile provides the data depending on the concentration level which in turn determines the speed of the wheelchair and accelerometer is used to provide direction to the wheelchair. The experimental results were very encouraging, which also demonstrated different concentration level of individuals which is used is controlling speed.


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