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dc.contributor.authorWu, Shang-Linen_US
dc.contributor.authorLiao, Lun-Deen_US
dc.contributor.authorLu, Shao-Weien_US
dc.contributor.authorJiang, Wei-Lingen_US
dc.contributor.authorChen, Shi-Anen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2014-12-08T15:31:03Z-
dc.date.available2014-12-08T15:31:03Z-
dc.date.issued2013-08-01en_US
dc.identifier.issn0018-9294en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TBME.2013.2248154en_US
dc.identifier.urihttp://hdl.handle.net/11536/22139-
dc.description.abstractElectrooculography (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.en_US
dc.language.isoen_USen_US
dc.subjectBiosignal processingen_US
dc.subjectclassification methodsen_US
dc.subjectelectrooculography (EOG)en_US
dc.subjecteye movement detectionen_US
dc.subjecthuman-computer interface (HCI)en_US
dc.titleControlling a Human-Computer Interface System With a Novel Classification Method that Uses Electrooculography Signalsen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TBME.2013.2248154en_US
dc.identifier.journalIEEE TRANSACTIONS ON BIOMEDICAL ENGINEERINGen_US
dc.citation.volume60en_US
dc.citation.issue8en_US
dc.citation.spage2133en_US
dc.citation.epage2141en_US
dc.contributor.department影像與生醫光電研究所zh_TW
dc.contributor.department電控工程研究所zh_TW
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentInstitute of Imaging and Biomedical Photonicsen_US
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000322025300008-
dc.citation.woscount2-
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