A EOG-based switch and its application for “start/stop” control of a wheelchair (original) (raw)
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An EOG-Based Human-Machine Interface for Wheelchair Control
IEEE transactions on bio-medical engineering, 2017
Non-manual human-machine interfaces (HMIs) have been studied for wheelchair control with the aim of helping severely paralyzed individuals regain some mobility. The challenge is to rapidly, accurately and sufficiently produce control commands, such as left and right turns, forward and backward motions, acceleration, deceleration, and stopping. In this paper, a novel electrooculogram (EOG)-based HMI is proposed for wheelchair control. Thirteen flashing buttons are presented in the graphical user interface (GUI), and each of the buttons corresponds to a command. These buttons flash on a one-by-one manner in a pre-defined sequence. The user can select a button by blinking in sync with its flashes. The algorithm detects the eye blinks from a channel of vertical EOG data and determines the user's target button based on the synchronization between the detected blinks and the button's flashes. For healthy subjects/patients with spinal cord injuries (SCIs), the proposed HMI achieved...
Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks
Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2021
The human brain is considered one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. An embedded Bluetooth module acquires and transfers the signals wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in realtime and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI. The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The wheelchair attained a command detection and execution accuracy of 96.4% which is an improvement from the existing systems. The work presented here gives a basic understanding of the functionality of a BCI system and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis.
Current Science, 2018
Here we report a system which can be operated using electroencephalography (EEG) signals generated during eye blink and thus may be useful for persons with locomotive and other disabilities for performing their day-today activities. EEG signals are processed by a microcontroller and based on programming, the microcontroller takes a decision to perform the desired task by actuating a corresponding device from several devices connected to the system. An important feature of the system is that it can be adapted to particular needs of the user and can be attached/detached for actuation of different appliances according to the user's condition and requirements.
EEG-based system for rapid on-off switching without prior learning
Medical & Biological Engineering & Computing, 1997
Details are reported of an EEG-based system that permits a person rapidly and reliably to switch on and off electrical devices without prior learning. The system detects and uti/ises increases in the amplitude of the aJpha component of the EEG spectrum that occur when people close their eyes for more than t s. In addition to conventional signal-processing elements, the system incorporates a module for suppressing switching at the output of the system when predetermined noise threshold levels (such as those due to sources of EMG) are exceeded. This work indicates that a majority, perhaps in excess of 90%, of the adult population can demonstrate the control necessary to operate an electrical device or appliance using this system. It is indicated that multi. level switching and quasi-continuous control options are feasible with further development of the system. This work has implications for the design of a system that could be used, for example, to assist the infirm or severely physically disabled to effect greater control over their environment.
Controlling a Wheelchair by use of EOG Signal
In this paper EOG signal processing and developing a low cost signal interface for wheelchair with high accuracy and reliability for severely disabled people is presented. Here the signal processing is done on a microcontroller which reduces the cost drastically rather than using a computer. Eye movement is detected by processing EOG signal, and associates the eye movement to motion commands of the wheelchair such as forward, reverse, left and right. A 99% accurate classification has been demonstrated experimentally.
IEEE transactions on bio-medical engineering, 2015
This study presents a novel human-machine interface (HMI) based on both electrooculography (EOG) and electroencephalography (EEG). This hybrid interface works in two modes: an EOG mode recognizes eye movements such as blinks, and an EEG mode detects event related potentials (ERPs) like P300. While both eye movements and ERPs have been separately used for implementing assistive interfaces, which help patients with motor disabilities in performing daily tasks, the proposed hybrid interface integrates them together. In this way, both the eye movements and ERPs complement each other. Therefore, it can provide a better efficiency and a wider scope of application. In this study, we design a threshold algorithm that can recognize four kinds of eye movements including blink, wink, gaze, and frown. In addition, an oddball paradigm with stimuli of inverted faces is used to evoke multiple ERP components including P300, N170, and VPP. To verify the effectiveness of the proposed system, two diff...
A Versatile Robotic Wheelchair Commanded by Brain Signals or Eye Blinks
2008
A system allowing a person with severe neuromotor disfunction to choose symbols in a Personal Digital Assistent (PDA) using electroencephalography (EEG) or electromyography (EMG) is implemented onboard an electrical wheelchair. Through this system the user is able to elicit personal needs or states, like sleep, thirst or hunger; to write texts using an alpha-numeric keyboard and to command a robotic wheelchair. The EEG patterns used are event-related synchronization and de-synchronization (ERS and ERD, respectively) occurring in the alpha band of the signal spectrum captured in the occipital region of the head, while the EMG patterns are eye-blinks. The results so far obtained with the system developed, in indoor and outdoor environments, are quite satisfactory. This paper describes the system so far implemented and shows some experimental results associated to it.
Wheelchair Motion Control Guide Using Eye Movement Based on EEG Signals.
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.
EMG-based hands-free wheelchair control with EOG attention shift detection
… and Biomimetics, 2007. …, 2007
This paper presents a novel hands-free control system for an electric-powered wheelchair, which is based on EMG (Electromyography) signals recorded from eyebrow muscle activity. By using a simple CyberLink [1] device, one-dimensional continuous EMG signals are obtained, analysed, and then translated into multi-directional control commands (forward, left, right, etc.) for the wheelchair that supports multi-directional control. At the same time, EOG (Electrooculography) signals detected from eye movements are used to adjust wheelchair speed. The system also allows a user to choose either control state or non-control state so that any non-intended muscle activity can be ignored during the non-control state. The system is reliable, easy to set up, and easy to use.
Human-Computer Interaction using Brain Waves Electric Signals
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-to-down, straight-to-right and straight-to-left). The issue of other eye movement patterns. Keywords—Brain computer interface, Electroculogram, Electrodes, Robotic Prototype Model