EOG-Based Human–Computer Interface: 2000–2020 Review

Electro-oculography (EOG)-based brain-computer interface (BCI) is a relevant technology influencing physical medicine, daily life, gaming and even the aeronautics field. EOG-based BCI systems record activity related to users' intention, perception and motor decisions. It converts the bio-physiological signals into commands for external hardware, and it executes the operation expected by the user through the output device. EOG signal is used for identifying and classifying eye movements through active or passive interaction. Both types of interaction have the potential for controlling the output device by performing the user's communication with the environment. In the aeronautical field, investigations of EOG-BCI systems are being explored as a relevant tool to replace the manual command and as a communicative tool dedicated to accelerating the user's intention. This paper reviews the last two decades of EOG-based BCI studies and provides a structured design space with a large set of representative papers. Our purpose is to introduce the existing BCI systems based on EOG signals and to inspire the design of new ones. First, we highlight the basic components of EOG-based BCI studies, including EOG signal acquisition, EOG device particularity, extracted features, translation algorithms, and interaction commands. Second, we provide an overview of EOG-based BCI applications in the real and virtual environment along with the aeronautical application. We conclude with a discussion of the actual limits of EOG devices regarding existing systems. Finally, we provide suggestions to gain insight for future design inquiries

Development of EOG Based Human Machine Interface Control System for Motorized Wheelchair

Abstract Rehabilitation devices are increasingly being used to improve the quality of the life of differentially abled people. Human Machine Interface (HMI) have been studied extensively to control electromechanical rehabilitation aids using biosignals such as EEG, EOG and EMG etc. among the various biosignals, EOG signals have been studied in depth due to the occurrence of a definite signal pattern. Persons suffering from extremely limited peripheral mobility like paraplegia or quadriplegia usually have the ability to coordinate eye movements. The current project focuses on the development of a prototype motor wheelchair controlled by EOG signals. EOG signals were used to generate control signals for the movement of the wheelchair. As a part of this work an EOG signal acquisition system was developed. The acquired EOG signal was then processed to generate various control signals depending upon the amplitude and duration of signal components. These control signals were then used to control the movements of the prototype motorized wheelchair model

Applications of the electric potential sensor for healthcare and assistive technologies

The work discussed in this thesis explores the possibility of employing the Electric Potential Sensor for use in healthcare and assistive technology applications with the same and in some cases better degrees of accuracy than those of conventional technologies. The Electric Potential Sensor is a generic and versatile sensing technology capable of working in both contact and non-contact (remote) modes. New versions of the active sensor were developed for specific surface electrophysiological signal measurements. The requirements in terms of frequency range, electrode size and gain varied with the type of signal measured for each application. Real-time applications based on electrooculography, electroretinography and electromyography are discussed, as well as an application based on human movement. A three sensor electrooculography eye tracking system was developed which is of interest to eye controlled assistive technologies. The system described achieved an accuracy at least as good as conventional wet gel electrodes for both horizontal and vertical eye movements. Surface recording of the electroretinogram, used to monitor eye health and diagnose degenerative diseases of the retina, was achieved and correlated with both corneal fibre and wet gel surface electrodes. The main signal components of electromyography lie in a higher bandwidth and surface signals of the deltoid muscle were recorded over the course of rehabilitation of a subject with an injured arm. Surface electromyography signals of the bicep were also recorded and correlated with the joint dynamics of the elbow. A related non-contact application of interest to assistive technologies was also developed. Hand movement within a defined area was mapped and used to control a mouse cursor and a predictive text interface

Dwell-free typing using an EOG based virtual keyboard

This work presents the development of an asynchronous dwell-free virtual keyboard application which can be operated using electrooculographic (EOG) data. Unlike other EOG based eye typing applications, the developed system avoids the use of dwell-times and relieves the user from the need to perform repetitive and unnatural eye movements tomove a cursor towards the desired letter or the need to perform voluntary blinks to interact with the application. Instead, the proposed application requires the user to simply glance through the vicinity of the desired letters, as one would swipe through letters when typing on a touchscreen device, after which a set of word predictions are displayed for the user to select. The proposed application obtained a top five rate of 76.00 ± 12.61% using EOG data which is comparable to the top five rate of 79.00 ± 13.37% obtained when operating the application using a vision-based eye gaze tracker.peer-reviewe

Detection of Eye Movements

Import 05/08/2014Předmětem této bakalářské práce je detekce pohybů očí. Po provedení teoretického rozboru problematiky byla vybrána metoda elektrookulografie, která v dnešní době představuje možný kompromis mezi cenou a přesností zařízení. Rozvoji této metody bezpochyby prospívá i současný trend miniaturizace, kdy je možné elektrody upevnit např. do obrub brýlí. Elektrody tak není nutné mít přilepeny na obličeji, čímž se zvyšuje ochota lidí tuto metodu využívat. Cílem této práce je dostat elektrookulografii do podvědomí studentů. První část práce je věnována teoretickému rozboru elektrookulografie, nejnovějším poznatkům v této oblasti a jejímu srovnání s dalšími metodami. V praktické části je představeno vlastní hardwarové řešení elektrookulografu a vizualizační software, který pomůže studentům názorně předvést princip měření elektrookulografie a mohou si vyzkoušet přesnost elektrookulografu a ovládání jednoduché aplikace pohyby oka.The subject of this thesis is detection of eye movement. After the theoretical analysis, the method of electrooculography (EOG) was chosen. Nowadays, this method represents a possible compromise between the price and accuracy of equipment. Development of this method also benefits from the contemporary trend of miniaturization, when an electrode can be attached e.g. to the goggles so it is not necessary to have the electrodes attached on your face. This thesis aims at bringing the electrooculography to the awareness of students. In the first part I devote to the theoretical analysis electrooculography, the newest findings in this field and its comparison with other methods. In the practical part I present the hardware solution of electro-oculography and visualization software. This software helps students understand this method, enabling them to test the accuracy of electrooculograph and control of simple application with eye movement.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Studies on the impact of assistive communication devices on the quality of life of patients with amyotrophic lateral sclerosis

Tese de doutoramento, Ciências Biomédicas (Neurociências), Universidade de Lisboa, Faculdade de Medicina, 2016Amyotrophic Lateral Sclerosis (ALS) is a progressive neuromuscular disease with rapid and generalized degeneration of motor neurons. Patients with ALS experiment a relentless decline in functions that affect performance of most activities of daily living (ADL), such as speaking, eating, walking or writing. For this reason, dependence on caregivers grows as the disease progresses. Management of the respiratory system is one of the main concerns of medical support, since respiratory failure is the most common cause of death in ALS. Due to increasing muscle weakness, most patients experience dramatic decrease of speech intelligibility and difficulties in using upper limbs (UL) for writing. There is growing evidence that mild cognitive impairment is common in ALS, but most patients are self-conscious of their difficulties in communicating and, in very severe stages, locked-in syndrome can occur. When no other resources than speech and writing are used to assist communication, patients are deprived of expressing needs or feelings, making decisions and keeping social relationships. Further, caregivers feel increased dependence due to difficulties in communication with others and get frustrated about difficulties in understanding partners’ needs. Support for communication is then very important to improve quality of life of both patients and caregivers; however, this has been poorly investigated in ALS. Assistive communication devices (ACD) can support patients by providing a diversity of tools for communication, as they progressively lose speech. ALS, in common with other degenerative conditions, introduces an additional challenge for the field of ACD: as the disease progresses, technologies must adapt to different conditions of the user. In early stages, patients may need speech synthesis in a mobile device, if dysarthria is one of the initial symptoms, or keyboard modifications, as weakness in UL increases. When upper limbs’ dysfunction is high, different input technologies may be adapted to capture voluntary control (for example, eye-tracking devices). Despite the enormous advances in the field of Assistive Technologies, in the last decade, difficulties in clinical support for the use of assistive communication devices (ACD) persist. Among the main reasons for these difficulties are lack of assessment tools to evaluate communication needs and determine proper input devices and to indicate changes over disease progression, and absence of clinical evidence that ACD has relevant impact on the quality of life of affected patients. For this set of reasons, support with communication tools is delayed to stages where patients are severely disabled. Often in these stages, patients face additional clinical complications and increased dependence on their caregivers’ decisions, which increase the difficulty in adaptation to new communication tools. This thesis addresses the role of assistive technologies in the quality of life of early-affected patients with ALS. Also, it includes the study of assessment tools that can improve longitudinal evaluation of communication needs of patients with ALS. We longitudinally evaluated a group of 30 patients with bulbar-onset ALS and 17 caregivers, during 2 to 29 months. Patients were assessed during their regular clinical appointments, in the Hospital de Santa Maria-Centro Hospitalar Lisboa_Norte. Evaluation of patients was based on validated instruments for assessing the Quality of Life (QoL) of patients and caregivers, and on methodologies for recording communication and measuring its performance (including speech, handwriting and typing). We tested the impact of early support with ACD on the QoL of patients with ALS, using a randomized, prospective, longitudinal design. Patients were able to learn and improve their skills to use communication tools based on electronic assistive devices. We found a positive impact of ACD in psychological and wellbeing domains of quality of life in patients, as well as in the support and psychological domains in caregivers. We also studied performance of communication (words per minute) using UL. Performance in handwriting may decline faster than performance in typing, supporting the idea that the use of touchscreen-based ACD supports communication for longer than handwriting. From longitudinal recordings of speech and typing activity we could observe that ACD can support tools to detect early markers of bulbar and UL dysfunction in ALS. Methodologies that were used in this research for recording and assessing function in communication can be replicated in the home environment and form part of the original contributions of this research. Implementation of remote monitoring tools in daily use of ACD, based on these methodologies, is discussed. Considering those patients who receive late support for the use of ACD, lack of time or daily support to learn how to control complex input devices may hinder its use. We developed a novel device to explore the detection and control of various residual movements, based on sensors of accelerometry, electromyography and force, as input signals for communication. The aim of this input device was to develop a tool to explore new communication channels in patients with generalized muscle weakness. This research contributed with novel tools from the Engineering field to the study of assistive communication in patients with ALS. Methodologies that were developed in this work can be further applied to the study of the impact of ACD in other neurodegenerative diseases that affect speech and motor control of UL

A Study of recent classification algorithms and a novel approach for biosignal data classification

Analyzing and understanding human biosignals have been important research areas that have many practical applications in everyday life. For example, Brain Computer Interface is a research area that studies the connection between the human brain and external systems by processing and learning the brain signals called Electroencephalography (EEG) signals. Similarly, various assistive robotics applications are being developed to interpret eye or muscle signals in humans in order to provide control inputs for external devices. The efficiency for all of these applications depends heavily on being able to process and classify human biosignals. Therefore many techniques from Signal Processing and Machine Learning fields are applied in order to understand human biosignals better and increase the efficiency and success of these applications. This thesis proposes a new classifier for biosignal data classification utilizing Particle Swarm Optimization Clustering and Radial Basis Function Networks (RBFN). The performance of the proposed classifier together with several variations in the technique is analyzed by utilizing comparisons with the state of the art classifiers such as Fuzzy Functions Support Vector Machines (FFSVM), Improved Fuzzy Functions Support Vector Machines (IFFSVM). These classifiers are implemented on the classification of same biological signals in order to evaluate the proposed technique. Several clustering algorithms, which are used in these classifiers, such as K-means, Fuzzy c-means, and Particle Swarm Optimization (PSO), are studied and compared with each other based on clustering abilities. The effects of the analyzed clustering algorithms in the performance of Radial Basis Functions Networks classifier are investigated. Strengths and weaknesses are analyzed on various standard and EEG datasets. Results show that the proposed classifier that combines PSO clustering with RBFN classifier can reach or exceed the performance of these state of the art classifiers. Finally, the proposed classification technique is applied to a real-time system application where a mobile robot is controlled based on person\u27s EEG signal

Multimodal Wearable Sensors for Human-Machine Interfaces

Certain areas of the body, such as the hands, eyes and organs of speech production, provide high-bandwidth information channels from the conscious mind to the outside world. The objective of this research was to develop an innovative wearable sensor device that records signals from these areas more conveniently than has previously been possible, so that they can be harnessed for communication. A novel bioelectrical and biomechanical sensing device, the wearable endogenous biosignal sensor (WEBS), was developed and tested in various communication and clinical measurement applications. One ground-breaking feature of the WEBS system is that it digitises biopotentials almost at the point of measurement. Its electrode connects directly to a high-resolution analog-to-digital converter. A second major advance is that, unlike previous active biopotential electrodes, the WEBS electrode connects to a shared data bus, allowing a large or small number of them to work together with relatively few physical interconnections. Another unique feature is its ability to switch dynamically between recording and signal source modes. An accelerometer within the device captures real-time information about its physical movement, not only facilitating the measurement of biomechanical signals of interest, but also allowing motion artefacts in the bioelectrical signal to be detected. Each of these innovative features has potentially far-reaching implications in biopotential measurement, both in clinical recording and in other applications. Weighing under 0.45 g and being remarkably low-cost, the WEBS is ideally suited for integration into disposable electrodes. Several such devices can be combined to form an inexpensive digital body sensor network, with shorter set-up time than conventional equipment, more flexible topology, and fewer physical interconnections. One phase of this study evaluated areas of the body as communication channels. The throat was selected for detailed study since it yields a range of voluntarily controllable signals, including laryngeal vibrations and gross movements associated with vocal tract articulation. A WEBS device recorded these signals and several novel methods of human-to-machine communication were demonstrated. To evaluate the performance of the WEBS system, recordings were validated against a high-end biopotential recording system for a number of biopotential signal types. To demonstrate an application for use by a clinician, the WEBS system was used to record 12‑lead electrocardiogram with augmented mechanical movement information