title: Controlling a human-computer interface system with a novel classification method that uses electrooculography signals.
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.
authors: Institute of Electrical Control Engineering, National Chiao Tung University, Hsinchu 300, Taiwan. shanglinwu.ece00g@nctu.edu.tw
authors: Shang-Lin
authors: SL
authors: Wu
authors: Lun-De
authors: LD
authors: Liao
authors: Shao-Wei
authors: SW
authors: Lu
authors: Wei-Ling
authors: WL
authors: Jiang
authors: Shi-An
authors: SA
authors: Chen
authors: Chin-Teng
authors: CT
authors: Lin
mesh_terms: D062207:Brain-Computer Interfaces; D004566:Electrodes; D004585:Electrooculography / Q000295:instrumentation*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005133:Eye Movements / Q000502:physiology*; D006801:Humans; D008328:Man-Machine Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013686:Telemetry / Q000295:instrumentation*