Human-Computer Interaction using Brain Waves Electric Signals

2013

This paper is aimed to analyze different levels of eye movement signals strength using Electrooculography (EOG). The eye movement that is known to be a significant communication tool for a tetraplegia, can be defined as a paralysis that is caused by serious injuries or illness to a human that lead to a partial or total loss of their lower limb and torso. A person who has such paralysis is highly dependent on an assistant and a wheelchair for movement. It is not always the case where the helper is with the patient all the time, therefore independence is encouraged among the wheelchair users. The signal from the eye muscles that is called electrooculogram is generated at different eye movements' directions and levels. The eye movement signals are acquired using g.USBamp from G.TEC Medical Engineering GMBH by using Ag/AgCl electrodes. The data is then passed to MATLAB/SIMULINK software for data analysis. Different directions and strength level of eye movement are fed to a virtual wheelchair model developed in MSC.Visual Nastran 4D software to study the effect of the signals on the distance and rotation travelled by the wheelchair. Simulation exercises has verified that different strength of eye movement signals levels that have been processed could be manipulated for helping tetraplegia in their mobility using the wheelchair.

Electrooculography is a technique for measuring the resting potential of the retina. The resulting signal is called the electrooculogramThe bio-potential signal also is one of the examples of human-machine interface using of nonverbal information such as electrooculography (EOG), electromyography (EMG), and electroencephalography (EEG) signals. The EOG and EMG signals are physiological changes; but here we are focusing the mainly on EOG signals for the human-machine interface. This papert has investigated that different EOG signals obtained from four different places around eye; (right, left, up, and down) have led to different level of distance and rotation of wheelchair. Those four signals are correspond to different levels of right and left steer, forward and backward motion. There are many research that have concentrated in making use of the eye movement signals for tetraplegia. Despite of all the complexity that arises when analyzing the eye movement signals. In this case the constraints are made such that the eye movement is assumes to be very limited to; (straight-to-up, straight-todown, straight-to-right and straight-to-left). The issue of other eye movement patterns.

Electrooculography is a technique for measuring the resting potential of the retina. The resulting signal is called the electrooculogram The bio-potential signal also is one of the examples of human–machine interface using of nonverbal information such as electrooculography (EOG), electromyography (EMG), and electroencephalography (EEG) signals. The EOG and EMG signals are physiological changes; but here we are focusing the mainly on EOG signals for the human–machine interface. This paper has investigated that different EOG signals obtained from four different places around eye; (right, left, up, and down) have led to different level of distance and rotation of wheelchair. Those four signals are correspond to different levels of right and left steer, forward and backward motion. There are many research that have concentrated in making use of the eye movement signals for tetraplegia. Despite of all the complexity that arises when analyzing the eye movement signals. In this case the constraints are made such that the eye movement is assumes to be very limited to; (straight-to-up, straight-to-down, straight-to-right and straight-to-left). The issue of other eye movement patterns.

International Journal of Biomedical Engineering and Technology, 2013

This paper is aimed to analyse different levels of eye movement signals strength using Electrooculography (EOG). The eye movement is a potential communication tool for a tetraplegia, which is defined as a paralysis that is caused by serious injuries or illness to a human that lead to a partial or total loss of their lower limb and torso. The signal from the eye muscles that is called electrooculogram is generated at different eye movements' directions and levels using g.USBamp from G.TEC Medical Engineering by using Ag/AgCl electrodes. Different directions and strength levels of eye movement are fed to Matlab-Simulink environment that was integrated with a virtual wheelchair model to study the effect of the EOG signals on the distance and rotation travelled by the wheelchair. Simulation results show that the EOG signals with different strengths acquired can be used as a reference input for wheelchair control.

International Journal of Engineering Sciences & Research Technology, 2013

The design and implementation of an Autonomous Movement Robot based on a Wheelchair based on EOG signal is to help a disable or handicapped person. These EOG electrodes are placed at right and left of eye and other pair of electrodes are at top and right of the eye. These electrodes used to response after gazing of one target point for a particular time period. After gazing of point, the wheelchair used to move to a target position. So, it produce delay during eye gaze. To overcome this delay, EEG amplifier are used. These EEG signals are placed to capture brain waves. These brain waves are controlled by microcontroller and it produce analog waves. To convert analog waves to digital output Analog to digital converter is used. Object Sensor is used to avoid obstacles in its path respectively. The main contribution of the work is the combination of several technologies and techniques that came from different areas such as mechanical, electronic engineering. Driver circuit with relay are used to move wheelchair automatically. ZigBee is used for long transmission of wheelchair. Accelerometer and interfacing circuit are done by using head movement. DLOA algorithm used to avoid obstacles while reaching destination point. The target coordinates of the destination place using EEG, to reduce delay for auto navigation process.

International Journal of Computer Applications

The use of vital signals as a connection interface between humans and computers has recently attracted a great deal of attention. The electro-oculogram (EOG) signal, which is due to eye potential, is one of these signals. More advanced, EOGbased Human-Machine Interfaces (HMIs) are widely investigated and considered to be a noble interface option for disabled people. Artificial neural networks were utilized in this study to detect eye movement from the EOG signal. Neural networks can detect and classify biological signals with nonlinear dynamics, including EOG signals, due to their ability to learn nonlinear dynamics and their pervasive approximation. In this study, two fundamentally distinct networks, MLP and ART, were used to detect sequential and random eye movements for controlling wheelchair. The results indicate that the MLP network could indeed detect consecutive eye movements with an accuracy of over 90%, although the accuracy of this network detection in the case of random movements is relatively poor. In the field of random eye movements, the greatest results are obtained using the ART2AE network, which allows having a diagnostic accuracy of over 70%.

Journal of Healthcare Engineering, 2021

Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed. Analog Digital Instrument T26 with a five-electrode system was used in this method. Voluntarily twenty subjects participated in this study. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences; the features were extracted by applying convolution theorem. Afterwards, extracted features were classified using Elman and distributed time d...

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2002

Describes an eye-control method based on electrooculography (EOG) to develop a system for assisted mobility. One of its most important features is its modularity, making it adaptable to the particular needs of each user according to the type and degree of handicap involved. An eye model based on electrooculographic signal is proposed and its validity is studied. Several human-machine interfaces (HMI) based on EOG are commented, focusing our study on guiding and controlling a wheelchair for disabled people, where the control is actually effected by eye movements within the socket. Different techniques and guidance strategies are then shown with comments on the advantages and disadvantages of each one. The system consists of a standard electric wheelchair with an on-board computer, sensors and a graphic user interface run by the computer. On the other hand, this eye-control method can be applied to handle graphical interfaces, where the eye is used as a mouse computer. Results obtained show that this control technique could be useful in multiple applications, such as mobility and communication aid for handicapped persons.

Journal of Physics: Conference Series, 2007

This paper presents a Human-Machine Interface (HMI) based on the signals generated by eye blinks or brain activity. The system structure and the signal acquisition and processing are shown. The signals used in this work are either the signal associated to the muscular movement corresponding to an eye blink or the brain signal corresponding to visual information processing. The variance is the feature extracted from such signals in order to detect the intention of the user. The classification is performed by a variance threshold which is experimentally determined for each user during the training stage. The command options, which are going to be sent to the commanded device, are presented to the user in the screen of a PDA (Personal Digital Assistant). In the experiments here reported, a robotic wheelchair is used as the device being commanded.