Abstract: to brain to computer interface unit which analyzed

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 (