Electroencephalogram-based control of an electric wheelchair

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

BRAINWAVE MANEUVERED WHEELCHAIR

In this world, there are millions of people who suffer from quadriplegic, paralysis, mobility disorder, neuromuscular disorder in which organ below the neck can’t be controlled by the patients. The system which has been developed in this project is using electroencephalogram based promising and important technology using Brain Computer Interface. It helps unblessed people to control the organ below neck using their own brain. Modern electroencephalogram-based Brain Computer Interface uses gel type electrodes and this type of technology is only limited to hospitals and laboratories and it requires 30 minutes to acquire a brain signal and this proposed system is very costly. But to overcome this cup type electrodes are used and overall cost is reduced to make it cost effective. It has been made portable, so that users can handle and carry it easily. It is possible to operate an electric wheelchair for individuals with disabilities using electroencephalogram signals of their eye movements, which is accomplished via the application of algorithms in MATLAB. Finally, the outcomes of this suggested system provide useful outputs for the user.Keywords: Algorithms; Brain Computer Interface (BCI); Electroencephalogram (EEG); Electric wheelchair and Eye movements

Control of the electric wheelchair using EEG classification

Electric wheelchairs are some of the most important devices to assist physically handicapped persons. This paper presents the concept of brain controlled electric wheelchair designed for people who are not able to use other interfaces such as a hand joystick, and in particular for patients suffering from amyotrophic lateral sclerosis (ALS). The objective is to control the direction of an electric wheelchair using noninvasive scalp electroencephalogram (EEG)

Brain Operated Wheelchair Using a Single Electrode EEG Device and BCI

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

Review of Assistive Devices for Electric Powered Wheelchairs Navigation

The decreasing costs of microprocessor systems and increasing range of “Smart Sensors” have led to a boom in Assistive Device Technology. The annual rate of expenditure for mobility related devices has reached $1 billion dollars in the United States alone. The industries current focus is to develop a wider range of Independent Mobility Controllers to allow, even the most severely disabled person, the ability to control an Electric Powered Wheelchair (EPW). Advances in Autonomous Robot Design have led to corresponding improvements in EPW technology. This paper outlines user interfaces and input device technologies used at present to navigate an EPW

Design and Implementation of Wheelchair Controller Based Electroencephalogram Signal using Microcontroller

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 devide 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

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG) Device

The implementation of Artificial Neural Network in prediction the direction of electric wheelchair from brain signal input for physical mobility impairment.. The control of the wheelchair as an effort in improving disabled person life quality. The interaction from disabled person is helping in relation to social life with others. Because of the mobility impairment, the wheelchair with brain signal input is made. This wheel chair is purposed to help the disabled person and elderly for their daily activity. ANN helps to develop the mapping from input to target. ANN is developed in 3 level: input level, one hidden level, and output level (6-2-1). There are 6 signal from Neurosky Mindset sensor output, Alpha1, Alpha2, Raw signal, Total time signal, Attention Signal, and Meditation signal. The purpose of this research is to find out the output value from ANN: value in turning right, turning left, and forward. From those outputs, we can prove the relevance to the target. One of the main problem that interfering with success is the problem of proper neural network training. Arduino uno is chosen to implement the learning program algorithm because it is a popular microcontroller that is economic and efficient. The training of artificial neural network in this research uses 21 data package from raw data, Alpha1, Aplha2, Meditation data, Attention data, total time data. At the time of the test there is a value of Mean square Error(MSE) at the end of training amounted to 0.92495 at epoch 9958, value a correlation coefficient of 0.92804 shows that accuracy the results of the training process good.  Keywords: Navigation, Neural network, Real-time training, ArduinoÂ

Color-based classification of EEG Signals for people with the severe locomotive disorder

The neurons in the brain produces electric signals and a collective firing of these electric signals gives rise to brainwaves. These brainwave signals are captured using EEG (Electroencephalogram) devices as micro voltages. These sequence of signals captured by EEG sensors have embedded features in them that can be used for classification. The signals can be used as an alternative input for people suffering from severe locomotive disorder.Classification of different colors can be mapped for many functions like directional movement. In this paper, raw EEG signals from NeuroSky Mindwave headset (a single electrode EEG sensor) have been classified with an attention based Deep Learning Network. Attention based LSTM Networks have been implemented for classification of two different colors and four different colors. An accuracy of 93.5\% was obtained for classification of two colors and an accuracy of 65.75\% was obtained for classifcation of four signals using the mentioned attention based LSTM network.Comment: 6 pages, 3 figures, 14 graphs, 4 tables, 2 author

Emotional Brain-Computer Interfaces

Research in Brain-computer interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide signicant insight into the user's emotional state. This information can be utilized in two manners. 1) Knowledge of the inuence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subject's emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates.\ud These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of noninvasive EEG based BCIs