Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/973
DC FieldValueLanguage
dc.contributor.authorPang, Raymond Wai Manen_US
dc.contributor.otherLiang, S.-
dc.contributor.otherChoi, K.-S.-
dc.contributor.otherQin, J.-
dc.contributor.otherHeng, P.-A.-
dc.date.accessioned2021-07-19T06:33:07Z-
dc.date.available2021-07-19T06:33:07Z-
dc.date.issued2014-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/973-
dc.description.abstractEffective user training could help us to improve the discrimination performance of our intention in brain computer interface (BCI). This paper aims to differentiate users left or right hand motor imagery (MI) tasks with different scenarios in 3D virtual environment, as non-object-directed (NOD) scenario, static-object-directed (SOD) scenario and dynamic-object-directed (DOD) scenario respectively. The results have significant differences by applying these three scenarios. Both SOD and DOD scenarios provide better classification accuracy, shorten single-trial period, and need smaller training samples comparing with the NOD case. We conclude that improving visual display may facilitate learning to use a BCI. Further comparing these results between single-subject and multiple-subject paradigm of BCI, we verify better classification performance could also be achieved by the multiple-subject paradigm. We believe these findings have the potential to improve discrimination performance of users intention for EEG-based BCI applications.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.titleEffective user training for motor imagery based brain computer interface with object-directed 3D visual displayen_US
dc.typeconference proceedingsen_US
dc.relation.publicationProceedings of the 7th International Conference on Biomedical Engineering and Informatics (BMEI, 2014)en_US
dc.identifier.doi10.1109/BMEI.2014.7002788-
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.relation.isbn9781479958382en_US
dc.description.startpage297en_US
dc.description.endpage301en_US
dc.cihe.affiliatedYes-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairetypeconference proceedings-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.cerifentitytypePublications-
crisitem.author.deptYam Pak Charitable Foundation School of Computing and Information Sciences-
Appears in Collections:CIS Publication
SFX Query Show simple item record

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.