Electrooculogram based system for computer control using a multiple feature classification model

Abstract

This paper discusses the creation of a system for computer-aided communication through automated analysis and processing of electrooculogram signals. In situations of disease or trauma, there may be an inability to communicate with others through standard means such as speech or typing. Eye movement tends to be one of the last remaining active muscle capabilities for people with neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS) also known as Lou Gehrig's disease. Thus, there is a need for eye movement based systems to enable communication. To meet this need, the Telepathix system was designed to accept eye movement commands denoted by looking to the left, looking to the right, and looking straight ahead to navigate a virtual keyboard. Using a ternary virtual keyboard layout and a multiple feature classification model, a typing speed of 6 letters per minute was achieved.

Wearable EOG goggles: Seamless sensing and context-awareness in everyday environments

“…Basic eye movement characteristics detected from EOG signals such as saccades, fixations, blinks and deliberate movement patterns have been used for handsfree operation of stationary human-computer [13,22] and human-robot [23,10] command interfaces. As part of a hospital alarm system, EOG-based switches provided immobile patients with a safe and reliable way of signalling an alarm [43].…”

An electrooculogram-based binary saccade sequence classification (BSSC) technique for augmentative communication and control

“…Previous attempts to harness the EOG as a control signal, such as an interface to a computer mouse [2], [3], [4], [5], for text input [6], [7] and for control [8], [9] have produced some promising results but have suffered from common problems such as drift and/or restrictions on head movement.…”

Design and Acquisition of EOG Based Interactive Communications for ALS Patients

“…Signal processing techniques have been used to reduce noise in the EOG signal during work and to eliminate unwanted signals caused by muscle movements around the eye. The right, left, and center positions of the eye were determined by asking them to print on the screen by eye movements with 13 words and 5 words with 6 letters [2]. In another work, they created a computer keyboard interface and implemented a virtual keyboard that can be controlled with EOG and EMG marks.…”

“…In the literature, Barea et al (2002) developed the humanmachine interface to control the wheelchair with EOG signals [1]. Kherlopian et al (2006) developed a computer-based system capable of automatic analysis and processing of EOG signals. Signal processing techniques have been used to reduce noise in the EOG signal during work and to eliminate unwanted signals caused by muscle movements around the eye.…”