Abstract:Improving the quality of life for the elderly and disabled people andgiving them the proper care at the right time is one the most important rolesthat are to be performed by us being a responsible member of the society. It’s noteasy for the disabled and elderly people to mobile a mechanical wheelchair,which many of them normally use for locomotion or movements. Hence there is aneed for designing a wheelchair that provides easy mobility. In this thesis, anattempt has been made to propose a brain controlled wheelchair, which uses thecaptured signals from the brain and processes it to control the wheelchair. Electroencephalography(EEG) technique deploys an electrode cap that is placed on the user’s scalp forthe acquisition of the EEG signals which are captured and translated intomovement commands by the arduino microcontroller which in turn move thewheelchair. Aftermeasuring brain waves it delivers to brain to computer interface unit whichanalyzed and amplified and classify waves into alpha, beta, gamma, waves thenarduino microcontroller controls the speed of the wheelchair and theaccelerometer provides direction to the wheelchair.

 Keywords—Microcontroller, Electroencephalogram, Braincomputer interface, Brain signals I.  INTRODUCTION   Theelectric-powered wheelchair is a wheelchair acting by an electric motorcontrolled with a hand-operated joystick. However, some people suffering fromsevere motor disabilities cannot use the joystick, such as paralysis andphysically disable people and locked-in syndrome. So they have other specialdevices available (touchpad, head /speech control, eye, EEG, etc). With theobjective of responding to numerous mobility problems, various intelligentwheelchair related research have been created in the last years. In thisresearch try not only to give mobility to handicapped people but, moreimportantly, independently of third party help.

Despite these new types ofcontrol methods, can acquire users intention to control the wheelchair.However, each type of alternative control has its limitations. Wheelchair users areamong the most visible members of the disability community; they experience avery high level of activity and functional limitation and also have less ofemployment opportunities. Elderly people are the group with the highest ratesof both manual and electric wheelchair use. Wheelchair users report difficulty in basic lifeactivities, and perceived disability. It’s not easy for the physicallychallenged and elderly people to move a mechanical or electric wheelchair. Inrecent times there have been a wide range of technologies that help aid thedisabled physically challenged. These control systems are designed to help thephysically challenged specifically.

These competitive systems are replacing theconventional manual assistance systems. The wheelchair too has developedsignificantly with a variety of guidance systems alongside like using thejoystick and a touch screen, and systems based on voice recognition. Thesesystems however are of use to those with a certain amount of upper bodymobility. Those suffering from a greater degree of paralysis may not be able touse these systems since they require accurate control. To help improve thelifestyle of the physically challenged further, this research work aims atdeveloping a wheelchair system that moves in accordance with the signalsobtained from the neurons in the brain through the electroencephalograph(EEG)electrode.EEG stands for electroencephalogram, a electrode commonly used todetect electrical activity in the brain.

Detecting, recording, and interpreting“brain waves” began in the late 1800s with the discovery and exploration ofelectrical patterns in the brains and the technology has evolved to enableapplications ranging fromthe medical detection of neurological disorders to playing games controlledentirely by the mind..                                 II. RELATEDTHEORY [1] Creusere et al (2012), “Assessment ofsubjective brain wave form quality from EEG brain replies via time spacefrequency analysis”, page 2704-2708. Theories give details herein and researchwork is the problem of quantifyingchanges in the perceived quality of signals by directly measuring the brainwave responses of human subjects using EEG technique. Ideas taken on from thisresearch work are that has preferred an approach constructed on time space frequencyanalysis of EEG wave form set for detecting different brain disorders.  [2] Jutgla et al (2012)” Diagnosis of Alzheimer’sdisease from EEG by means of synchrony measures in optimized frequency bands”,page 4266-4267.

Theories give 39 details herein research work is the EEG isconsidered as a promising diagnostic tool for analysing brain disorderssymptoms because of its non-invasive safe and easy to use properties. EEG hasthe potential to complement or replace some of the current tradition diagnostictechniques. Ideas taken from this research work are EEG datasets of thepatients with different brain disorders symptoms have been collected todiagnosis the seizures symptoms related to the patients.   [3] Michalopolouset al (2011) reported that the Characterization of evoked and induced activityin EEG and assessment of intertrail variability”, page 978-988.

Theories givedetails herein research work is the brain reply to an internal or externalexperience is poised through the superposition of suggested and persuaded brainactivity which reproduces dissimilar brain mechanisms involved. Caminiti (2010)reported that the identification of different brain activities through EEGassessment procedure. Ideas taken from this research work are identifying brainactivities for diagnostic purposes and provide useful tools for brain computerinterfaces through insight on the activation of different brain channels   [4] Duque Grajales J.E.

, Múnera Perafán A., TrujilloCano D., Urrego Higuita D.

A., Hernández Valdivieso A.M.(2009),” System forProcessing and Simulation of Brain Signals”, Page 340-345. Theories givedetails herein research work has presented the methodology used to develop asystem useful in the simulation of brain signals. It has been described indetail the procedure in the modelling of EEG signals and insight brain signalsrecorded during surgical procedures. Ideas taken from this research work areprocessing and simulation of brain signals from different signal processingmodels which allows going deep into the study of brain function during sleepingand pathological situations and facilitated the assessment of the effect ofdifferent drugs in different brain disorders [5] Sosa et al (2011) reported in theories givedetails herein research work is the operational procedures of EEGLAB andefficiency of EEG signal processing for students and professionals to performand analysis of the EEG signals. Its use as a starting point for the comparisonof different brain signal processing algorithms.

Ideas taken from this researchwork are Capabilities of EEGLAB for diagnosis purpose and basic explanation ofthe working procedure of that tool for signal processing such as – loading thedataset, plotting techniques to get the proper result, etc.  [6] Bhattacharya et al (2011) theories give detailsherein research work Presented the information about EEGLAB software forBrain-computer interface (BCI) is an emerging technology which aims to conveypeople’s intentions to the outside world directly from their thoughts. Ideastaken from this research work are the Feature learning of EEG to theclassification among frequencies in tribunals and within recording locations.

Methods to allow users to remove data channels, artefacts by accepting orrejecting visually. [7] Ye Yuan (2010) theories give details hereinresearch work; EEG dataset is collected after analysing the entire length ofthe EEG recording the patient frequently 40 for long time to detect traces ofdifferent human brain activities. Ideas taken from this research work arechange of the structure of different brain activities during seizures isobserved by the change of embedding dimension of EEG signals if the human brainis considered as a nonlinear dynamic system.   Implementation:As a communication and controlpathway to directly translate brain activities into computer control signals,brain-computer interface (BCI) has attracted increasing attention in recentyears from multiple scientific and engineering disciplines as well as from thepublic. Offering augmented or repaired sensory-motor functions, it appealsprimarily to people with severe motor disabilities. Furthermore, it provides auseful test-bed for the development of mathematical methods in brain signalanalysis.   Figure no.

2.1Aconceptual block diagram of overview of BCI System.    An important issue in BCI research iscursor control, where the objective is to map brain signals to movements of acursor on a computer screen.

Its potential applications are well beyond “cursorcontrol”, e.g. it can also be used in BCI-based neuro-prostheses.

Therefore, based on the first report of an EEG-basedsystem, the authors showed that through guided user training of regulating twoparticular EEG rhythms (mu and beta), two independent control signals could bederived from combinations of the rhythmic powers. The downside of the approachis with the required intensive user trainingA Brain Computer Interface device requires deliberateconscious thoughts; some thought alone BCI applications includes prostheticcontrol, collecting information from never, etc.  III.WORKING                                         Figure no.3.1 BlockDiagram Brainwaves are produced bysynchronized electrical pulses from masses of neurons communicating with eachother. Brainwaves are detected using sensors (EEG electrode) placed on thescalp.

They are divided into bandwidths to describe their functions, but arebest thought of as a continuous spectrum of consciousness; from slow, loud andfunctional – to fast, subtle, and complex. Our brainwaves change according towhat we are doing and feeling. When slower brainwaves are dominant we can feeltired, slow, or dreamy. The higher frequencies are dominant when we feel activeor hyper-alert. Brainwaves are complex reflect different aspects when theyoccur 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 fastwaves.

 Infra low (<0.5HZ)Infra-Lowbrainwaves (also known as Slow Cortical Potentials), are thought to be thebasic cortical rythms that underlie our higher brain functions. Verylittle is known about infra-low brainwaves. Their slow nature make themdifficult to detect and accurately measure, so few studies have beendone. They appear to take a major role in brain timing and networkfunction.  Delta Waves(0.5 to 3HZ).

Deltabrainwaves are slow, loud brainwaves (low frequency and deeply penetrating,like a drum beat). They are generated in deepest meditation and dreamlesssleep. Delta waves suspend external awareness and are the source of empathy.Healing and regeneration are stimulated in this state, and that is why deeprestorative sleep is so essential to the healing process.

Theta Waves(3 to 8HZ). Thetabrainwaves occur most often in sleep but are also dominant in deep meditation.Theta is our gateway to learning, memory, and intuition. In theta, our sensesare withdrawn from the external world and focused on signals originating fromwithin. It is that twilight state which we normally only experience fleetinglyas 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).

Alphabrainwaves are dominant during quietly flowing thoughts, and in some meditativestates. Alpha is ‘the power of now’, being here, in the present. Alpha is theresting state for the brain. Alpha waves aid overall mental coordination,calmness, alertness, mind/body integration and learning. Beta Waves (12to 38 HZ). Betabrainwaves dominate our normal waking state of consciousness when attention isdirected 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.Betabrainwaves are further divided into three bands; Lo-Beta (Beta1, 12-15Hz) canbe thought of as a ‘fast idle’, or musing.

Beta (Beta2, 15-22Hz) is highengagement or actively figuring something out. Hi-Beta (Beta3, 22-38Hz) ishighly complex thought, integrating new experiences, high anxiety, orexcitement. Continual high frequency processing is not a very efficient way torun the brain, as it takes a tremendous amount of energy.  Gamma Waves(38 to 42HZ).

Gammabrainwaves are the fastest of brain waves (high frequency, like a flute), andrelate to simultaneous processing of information from different brain areas.Gamma brainwaves pass information rapidly and quietly. The most subtle ofthe brainwave frequencies, the mind has to be quiet to access gamma. Gamma wasdismissed as ‘spare brain noise’ until researchers discovered itwas 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 thatgamma rhythms modulate perception and consciousness, and that agreater presence of gamma relates to expanded consciousness and spiritual emergence. IV.METHODOLOGYFigure no. 4.1Block DiagramA.    Electric Wheelchairs Stop AccidentalRollingOnce a manual wheelchair gets rolling,it can be hard to stop.

Whether it’s on a ramp or in San Francisco, a manualwheelchair can be prevented from rolling if you put the brakes on but are muchharder 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. Thepower that makes it go is also an excellent way of making it stop. They’re sturdierWeight can be one of the most daunting aspects of power wheelchairs, especiallyif 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 muchmore difficult to tip over. This means that they’re more solid when it comes tofront-to-back tipping and side-to-side tipping.

Weight has its advantages.B.    They Offer Constant PowerIf you’re at a park and in a manualwheelchair, you or the person pushing you has to take into account the tripback to the starting point.

A person who has lot energy at the start can getvery tired moving a wheelchair around before too long. That means an exhaustingtrip back.C.    Electric Wheelchairs Do the WorkThe most obvious reason that electricwheelchairs beat manual ones is that they do all the work it takes to getsomeone from place to place. While many people could get themselves around witha manual wheelchair, there are some hills and inclines that are hard for justabout everyone.

Of course, there are quite a few people who simply don’t havethe arm strength or ability to use their hands that it takes to work a manualwheelchair. Without a motorized wheelchair, they would always require someoneto move them around, while an electric wheelchair gives them freedom thatwheelchair-bound people from 30 years ago couldn’t experience. The paper is anessential need for disabled person to remove assistance. The paper implements arobot whose speed is controlled by the concentration level and the directionsare given by a accelerometer basically it is a need of a disabled person tomove around different places.  This robotalso avoid accidents in panic situationsdue to the use of concentration level as in panic situation the concentrationlevel drops down to zero so is the speed of the motor drops down zero.  This will help the person to independentlymove 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 handicappeople and normal people, in order to determine the   threshold value of the concentration that will in turn help to providethe variation in the speed of the motorsAs we move further with the survey it has beingdetermined 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 theconcentration levels Table no.

5.2 Results of the survey carried out for theconcentration 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 awheelchair which is fully automated and controlled using Beta wave (human brainattention) of Mind wave sensor which is detected from brain signal.

It usesArduino to control wheelchair. The mind-wave mobile provides the data dependingon the concentration level which in turn determines the speed of the wheelchairand accelerometer is used to provide direction to the wheelchair. Theexperimental results were very encouraging, which also demonstrated differentconcentration level of individuals which is used is controlling speed.


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