Brainwave Controlled Wheelchair (BCW)

IOP Conference Series: Materials Science and Engineering, 2018

The purpose of this study was to design of electric wheelchair. A wheelchair is a tool for people with disability. In general, a wheelchair is still controlled by hand or using a Joystick electrically, so users with a disorder can perform activities without any help. The research was designed to help disabilities who do not have hands and feet. So there needs to be wheelchair control without the use of hand or foot muscles. Therefore, it is designed an electric wheelchair that can be controlled based on brain wave activity using EEG sensors. EEG sensor is a device that can detect the activity of human brainwaves, this sensor can be used for control on a movement direction of a wheelchair, based on the parameters Blink detection and Attention. The laptop device is used as a LabVIEW-based programming control center that is associated with the Arduino as a communication between input and output components. As well as the ultrasonic distance sensor HCSR-04 is used as a wheelchair safety. Wheelchair movement can be controlled automatically, with the movement forward, backward, turn right and turn left, with an average success rate of 80%. This is because there is instability in detecting brain signals. Wheelchair ability in bringing user load is less than 130 kg.

TJPRC, 2014

Every physical move we make is triggered by neural processes in the brain. With the right equipment and recent developments in both brain imaging technologies and cognitive neuroscience, it is possible to read and record these processes. This has led to the rapidly growing field of brain computer interfaces (BCI). The Brain Computer Interface (BCI) helps unblessed people to make use of the devices and applications through their mental activities. So people believe that BCI technology is a blessing for the unblessed persons who may be suffering from severe neuromuscular disorders. So in this paper, we develop a cost effective Brain Computer Interface device to control the wheel chair for physically disabled people. The EEG analysis concept has been utilized to drive an electric wheelchair system automatically for quadriplegics or immobile persons. The EEG signals are captured from user’s brain activity using “Neuro-sky Mind wave‟ EEG sensor which is placed on the user’s forehead. The EEG signals that are generated at different level of concentration, also the eye movement artifacts in the EEG are processed using Lab VIEW software by means of FFT algorithms. The direction on which wheel-chair has to move is decided based on the processed EEG signal. Microcontroller MSP430G2231 controls the motor circuitry to drive the wheel chair in a Non-jerky manner.

ijsce.org

Abstract―In this study, eight electrodes were to capture Electroencephalogram (EEG) from the brain to build a brain computer interface (BCI) based real time control for wheelchair to help the severely handicapped persons. To achieve this goal Wavelet Packet Transform ( ...

IJRASET, 2021

In this paper, a brain controlled wheelchair has been designed which tends to reduce the complexity of movement for paralyzed people who are not capable of using various wheelchairs operating on technologies like joystick, finger movement or gesture controlled due to disability of moving body parts. The entire model is centrally based on Brain-computer Interface (BCI) combined with Raspberry Pi 3 and EEG sensor headset capture signals based on Neurosky mindwave technology which are further processed using MATLAB. Despite of the physical disabilities, this model will help quadriplegic patients to assist on their own and feel independent.

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 maneuver a mechanical wheelchair, which many of them normally use for locomotion. Hence there is a need for designing a wheelchair that is intelligent and provides easy maneuverability. In this context, an attempt has been made to propose a thought controlled wheelchair, which uses the captured signals from the brain and eyes and processes it to control the wheelchair. Electroencephalography (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 wheelchair. Keywords – Neuroscience, Brain Computer Interface (BCI), EEG, Micro-controller.

2015

This paper presents a brain based control of the wheelchair for physically impaired users. The design of the system is focused on receiving electroencephalographic (EEG) signals from the brain, processing and turning the system and then performing control of the wheelchair. The number of experimental measurements of brain activity has been obtained using human control commands of the wheelchair. The obtained data including EEG signals and control commands are used to design brain based control mechanism in training mode. The classification of brain signals has been done using a Support Vector Machine (SVM) and neural networks. The training data is used before using the system under real conditions. Then test data is applied to measure the accuracy of the control. The system designed in this paper is adjusted to control a wheelchair with five commands: move forward, move backward, stop, turn left and turn right in real conditions. The provided approach allows reducing the probability...

International Journal of Artificial Intelligence

This paper predominantly explains the use of a simplistic uni-polar device to obtain EEG for the development of a Brain-Computer Interface (BCI). In contrast, BCI's eye-blinking stimuli can also be obtained. Consequently, focus and eye-blinking stimuli can be captured as control pulses in electric wheelchairs via a computer interface and electrical interface. This survey paper aims to provide a feasible solution to integrate a Brain-Computer Interface (BCI) with automated identification and avoidance of obstacles. The automated obstacle detection and avoidance system aims to provide a way to easily detect obstacles and easily correct the course.

IRJET, 2022

The inability is extreme to the point that they can't have any sort of developments. Confronted with the present circumstance, Brain PC Interface innovation has responded to the call of creating arrangements that permit conveying a superior personal satisfaction to those individuals, and quite possibly of the main region has been the versatility arrangements, which incorporates the mind PC interface empowered electric wheelchairs as perhaps of the most supportive arrangement. Confronted with everything going on, the current work has fostered a Brain PC Interface arrangement that permits clients to control the development of their wheelchairs utilizing the mind waves created at the point when flickers their eyes. For the production of this arrangement, the Steady Prototyping approach has been utilized to improve the advancement interaction by creating autonomous modules. The arrangement is comprised of a few parts for example EEG System (OpenBCI), Main Controller, Wheelchair Controller and Wheelchair that permits to have a measured quality to do refreshes (upgrades) of their functionalities in a basic way. The created framework has shown that it requires a low measure of preparing time and has a genuine material reaction time. Exploratory outcomes demonstrate the way that the clients can perform unique undertakings with an OK grade of mistake in a timeframe that could be thought of as OK for the framework. Considering that the model was made for individuals with handicaps, the framework could concede them a specific degree of freedom

International Journal for Research in Engineering Application & Management (IJREAM), 2021

People who have extreme coordination disabilities, such as Locked in Syndrome, is a condition in which the patient is conscious and aware of the surroundings but is unable to speak or take any activity due to weakness in nearly any voluntary muscle in the body (with the exception of eye movements and blinking).In order to improve their quality of life, we propose a Brainwave Controlled wheelchair that can detect obstacles and also help alert the guardian of the person sitting on the wheelchair using buzzer when he/she needs any help. The system uses EEG headset which yields Attention level, EEG signals and blink strengths, the data is passed to a Raspberry Pi that is connected with a screen. The blinks will help the user to change the direction and attention level will help to move the wheelchair forward in the selected direction. Ultrasonic sensors will be used for obstacle detection. The wheelchair will have storage section to carry items such as essential medicines, personal belongings, etc. And it will also give a reminder to the user to take medicines on time.

CYBERLAB, 2012

BRAIN Computer Interface (BCI) is a technique that provides direct interface between the human brain and the computer. BCI techniques are broadly classified into invasive and non-invasive techniques. Non-invasive techniques are becoming more popular and more research is being done on this topic. There are various non-invasive BCI techniques such as EEG, Electro-Oculography. EEG technique deploys an electrode cap that is placed on the user’s scalp for the acquisition of the EEG signal, which relates the scalp potential differences to various complex actions. Classification of the EEG signal has been made into several bands like alpha, beta, delta, theta and mu suppression, each corresponding to various states of being like relaxing, ranging over 8-14 Hz; concentrating, ranging over 13-30 Hz; deep sleep, from 0-4 Hz; meditating from 4-8 Hz; moving your hands or legs or just by imagining these motor actions respectively. As it is being non-invasive in nature, it has an advantage over traditional BMI, not being hazardous to health. With the advent of technology the EEG acquisition devices are made more compact, handy and wireless. Using the above mentioned technique, a simple thought controlled wheelchair system has been proposed in this paper. A section that briefly explains the various blocks included in the system is also added in this paper

International Journal of Electrical and Computer Engineering (IJECE), 2016

Wheelchair is a medical device that can help patients, especially for persons with physical disabilities. In this research has designed a wheelchair that can be controlled using brain wave. Mind wave device is used as a sensor to capture brain waves. Fuzzy method is used to process data from mind wave. In the design was used a modified wheelchair (original wheelchair modified with addition dc motor that can be control using microcontroller). After processing data from mindwave using fuzzy method, then microcontroller ordered dc motor to rotate.The dc motor connected to gear of wheelchair using chain. So when the dc motor rotated the wheelchair rotated as well. Controlling of DC motor used PID control method. Input encoder was used as feedback for PID control at each wheel. From the experimental results concentration level data of the human brain waves can be used to adjust the rate of speed of the wheelchair. The level accuracy of respons Fuzzy method ton system obtained by devides total true respons data with total tested data and the result is 85.71 %. Wheelchairs can run at a maximum speed of 31.5 cm/s when the battery voltage is more than 24.05V. Moreover, the maximum load of wheelchair is 110 kg.

People with physical disabilities depend on technology for assistance and physical control. This paper presents non-invasive brain controlled wheelchair. Electroencephalogram (EEG) signals are used for controlling the wheelchair movement. Proposed design includes a novel approach for control wheelchair using Brain Computer Interface (BCI) technology. For validation of design a robotic module has been developed which can move under the control of human thoughts.

2020

In the last two years the electric vehicle market has boomed exponentially. The turn of the decade has produced some of the most advanced vehicles which remove dependence on fossil fuels and mainly derive their power from rechargeable batteries. These technologies enable faster speeds, higher efficiencies, and a more environmentally friendly transportation system. Coupling the battery with a means of accessing the planet's most available energy supply, solar radiation, including solar panels, has helped to expand the range of such electric cars. Such hybrid vehicles cannot actually mean everyday commuter vehicles or road cars. The range of electric cars is incredibly large, ranging from commercial vehicles for children to multipurpose vehicles, to wheelchairs for the otherwise competent. In this review paper, we will be reviewing various technologies with respect to making an automated wheelchair that can not only make it user friendly, but also, we will be exploring various methods and techniques wherein we can make the wheelchair cost friendly as well.

IEEE Transactions on Robotics, 2005

This paper presents a study on electroencephalogram (EEG)based control of an electric wheelchair. The objective is to control the direction of an electric wheelchair using only EEG signals. In other words, this is an attempt to use brain signals to control mechanical devices such as wheelchairs. To achieve this goal, we have developed a recursive training algorithm to generate recognition patterns from EEG signals. Our experimental results demonstrate the utility of the proposed recursive training algorithm and the viability of accomplishing direction control of an electric wheelchair by only EEG signals.