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dc.contributor.authorMannan, Malik M Naeem
dc.contributor.authorKamran, M Ahmad
dc.contributor.authorKang, Shinil
dc.contributor.authorChoi, Hak Soo
dc.contributor.authorJeong, Myung Yung
dc.date.accessioned2021-09-10T00:38:20Z
dc.date.available2021-09-10T00:38:20Z
dc.date.issued2020
dc.identifier.issn1424-8220
dc.identifier.doi10.3390/s20030891
dc.identifier.urihttp://hdl.handle.net/10072/407856
dc.description.abstractSteady‐state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli‐responsive hybrid speller by using electroencephalography (EEG) and video‐based eye‐tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)‐based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI‐speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI‐spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued‐spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free‐spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI‐based system will ultimately enable a truly high-speed communication channel.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/
dc.relation.ispartofissue3
dc.relation.ispartofjournalSensors
dc.relation.ispartofvolume20
dc.subject.fieldofresearchHuman-computer interaction
dc.subject.fieldofresearchSoftware engineering
dc.subject.fieldofresearchAnalytical chemistry
dc.subject.fieldofresearchEcology
dc.subject.fieldofresearchDistributed computing and systems software
dc.subject.fieldofresearchcode460806
dc.subject.fieldofresearchcode4612
dc.subject.fieldofresearchcode3401
dc.subject.fieldofresearchcode3103
dc.subject.fieldofresearchcode4606
dc.subject.keywordsScience & Technology
dc.subject.keywordsPhysical Sciences
dc.subject.keywordsTechnology
dc.subject.keywordsChemistry, Analytical
dc.subject.keywordsEngineering, Electrical & Electronic
dc.titleA Hybrid Speller Design Using Eye Tracking and SSVEP Brain-Computer Interface
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationMannan, M.M.N.; Kamran, M.A.; Kang, S.; Choi, H.S.; Jeong, M.Y. A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface. Sensors 2020, 20, 891. https://doi.org/10.3390/s20030891
dcterms.dateAccepted2020-02-05
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-09-10T00:26:01Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2020 by the authors. Licensee MDPI, Basel, Switzerland. article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
gro.hasfulltextFull Text
gro.griffith.authorMannan, Malik Muhammad Naeem
dc.subject.socioeconomiccode2801 Expanding knowledge


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