Nine States HCI using Electrooculogram and Neural Networks

Abstract

Human Computer Interface (HCI) translate biosignals to control external devices like computers wheelchairs, mouse and keyboard. This paper presents the feasibility of creating one such HCI using Electroooculography. Electrooculography is a technique of measuring the potential difference between the cornea and retina of the eye. Most of the EOG based HCI have focused on two to four and six states, this study focuses on increasing the possible states of the HCI to nine states. Two new eye movements were proposed. The proposed reference power technique was applied to extract the features from twenty subjects. Layered recurrent neural networks were used to classify the different EOG eye movement task signals. Experimental results validate the feasibility of using eleven different eye movements EOG signals for designing nine states HCI. From the result it was proved that feasibility of designing a nine states HCI is possible using reference power features with Layered Recurrent Neural Network. Keyword-Electrooculography, Human Computer Interaction, Layered Recurrent Network I. INTRODUCTION Communication is one of the requirements for human beings to interact with the society in daily life activity [1]. With increased life expectancy and medical research disabilities am elderly people is on the increase, which call for more rehabilitative devices to cater to the needs of the disabled people. Motor disability affects communication channels which effects the quality of living of the individuals. Alternative ways to communicate without speech and hand movements is important to increase the quality of life for these individuals. With the help of bio-based HCI, disabled people whose motor neurons controlling voluntary muscles are damaged can convey their intentions correctly to a certain extent. A rich source of information about the state of the user can be found in eye movements which include information related to the user's activities and their cognitive processes such as attention, saliency determination visual memory and perceptual learning. The eye movements produce an electrical activity which can be detected using Electrooculography (EOG) which records the cornearetinal potential [2]-[4]. A HCI can use the explicit eye movements directly as inputs. Present day HCI systems developed using EOG have limitations with respect to the number of states achievable to the HCI. To provide the best possible interface within given constraints, the HCI designers are supposed to develop systems that minimize the barrier between the human's cognitive model of what users want to accomplish and the computer's understanding of the user's task [5]. Researchers have used EOG signals in HCI studies, where various types of devices have been developed to aid communication and mobility of elderly disabled persons for improving their quality of life. In recent years EOG signals are used as a control signal for a variety of applications such as mobile robot control [6], mouse cursor control [7], tooth-click controller [8], hospital alarm sys...