Controlling a Human–Computer Interface System With a Novel Classification Method that Uses Electrooculography Signals
- 21 February 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 60 (8), 2133-2141
- https://doi.org/10.1109/tbme.2013.2248154
Abstract
Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.Keywords
This publication has 23 references indexed in Scilit:
- How Many Electromyography Channels Do We Need for Facial Nerve Monitoring?Journal of Clinical Neurophysiology, 2012
- A novel human–machine interface based on recognition of multi-channel facial bioelectric signalsAustralasian Physics & Engineering Sciences in Medicine, 2011
- Steering a Tractor by Means of an EMG-Based Human-Machine InterfaceSensors, 2011
- Sensory System for Implementing a Human—Computer Interface Based on ElectrooculographySensors, 2010
- Overestimation of saccadic peak velocity recorded by electro-oculography compared to video-oculography and scleral search coilClinical Neurophysiology, 2010
- EOG-based Human–Computer Interface system developmentExpert Systems with Applications, 2010
- Electroencephalography artifacts in workplace alertness monitoring.Scandinavian Journal of Work, Environment & Health, 2007
- Wheelchair Guidance Strategies Using EOGJournal of Intelligent & Robotic Systems, 2002
- Registrierung und Analyse des Elektrookulogramms mit einem Personalcomputer - Erste Erfahrungen bei Normalpersonen und Patienten mit Erkrankungen des hinteren Augenabschnitts und intraokulären FremdkörpernKlinische Monatsblätter für Augenheilkunde, 1989
- Common-Mode Rejection Ratio-Two DefinitionsIEEE Transactions on Biomedical Engineering, 1970