Nine States HCI using Electrooculogram and Neural Networks

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