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Title: Controlling a human-computer interface system with a novel classification method that uses electrooculography signals
Authors: Wu, S.-L.
Liao, L.-D. 
Lu, S.-W.
Jiang, W.-L.
Chen, S.-A.
Lin, C.-T.
Keywords: Biosignal processing
classification methods
electrooculography (EOG)
eye movement detection
human-computer interface (HCI)
Issue Date: 2013
Source: Wu, S.-L., Liao, L.-D., Lu, S.-W., Jiang, W.-L., Chen, S.-A., Lin, C.-T. (2013). Controlling a human-computer interface system with a novel classification method that uses electrooculography signals. IEEE Transactions on Biomedical Engineering 60 (8) : 2133-2141. ScholarBank@NUS Repository.
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. © 1964-2012 IEEE.
Source Title: IEEE Transactions on Biomedical Engineering
ISSN: 00189294
DOI: 10.1109/TBME.2013.2248154
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