Abstract: with a hand-operated joystick. However, some people

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




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.



Related Theory



Arduino simulator



Arduino Mega Board

EEG module

Assembled wheel chair


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




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 (