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Article: Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface

TitleChanges of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface
Authors
Keywordsbrain-computer interface
EEG
EOG
functional brain network
phase synchronization
Issue Date2020
PublisherInstitute of Physics Publishing. The Journal's web site is located at http://www.iop.org/EJ/journal/JNE
Citation
Journal of Neural Engineering, 2020, v. 17 n. 4, p. article no. 045006 How to Cite?
AbstractObjective. The steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI) has demonstrated relatively high performance with little user training, and thus becomes a popular BCI paradigm. However, due to the performance deterioration over time, its robustness and reliability appear not sufficient to allow a non-expert to use outside laboratory. It would be thus helpful to study what happens behind the decreasing tendency of the BCI performance. Approach. This paper explores the changes of brain networks and electrooculography (EOG) signals to investigate the cognitive capability changes along the use of the SSVEP-based BCI. The EOG signals are characterized by the blink amplitudes and the speeds of saccades, and the brain networks are estimated by the instantaneous phase synchronizations of electroencephalography signals. Main results. Experimental results revealed that the characteristics derived from EOG and brain networks have similar trends which contain two stages. At the beginning, the blink amplitudes and the saccade speeds start to reduce. Meanwhile, the global synchronizations of the brain networks are formed quickly. These observations implies that the cognitive decline along the use of the SSVEP-based BCI. Then, the EOG and the brain networks related characteristics demonstrate a slow recovery or relatively stable trend. Significance. This study could be helpful for a better understanding about the depreciation of the BCI performance as well as its relationship with the brain networks and the EOG along the use of the SSVEP-based BCI.
Persistent Identifierhttp://hdl.handle.net/10722/305868
ISSN
2023 Impact Factor: 3.7
2023 SCImago Journal Rankings: 1.094
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorPENG, Y-
dc.contributor.authorWANG, Z-
dc.contributor.authorWONG, CM-
dc.contributor.authorNAN, W-
dc.contributor.authorROSA, A-
dc.contributor.authorXU, P-
dc.contributor.authorWAN, F-
dc.contributor.authorHu, Y-
dc.date.accessioned2021-10-20T10:15:28Z-
dc.date.available2021-10-20T10:15:28Z-
dc.date.issued2020-
dc.identifier.citationJournal of Neural Engineering, 2020, v. 17 n. 4, p. article no. 045006-
dc.identifier.issn1741-2560-
dc.identifier.urihttp://hdl.handle.net/10722/305868-
dc.description.abstractObjective. The steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI) has demonstrated relatively high performance with little user training, and thus becomes a popular BCI paradigm. However, due to the performance deterioration over time, its robustness and reliability appear not sufficient to allow a non-expert to use outside laboratory. It would be thus helpful to study what happens behind the decreasing tendency of the BCI performance. Approach. This paper explores the changes of brain networks and electrooculography (EOG) signals to investigate the cognitive capability changes along the use of the SSVEP-based BCI. The EOG signals are characterized by the blink amplitudes and the speeds of saccades, and the brain networks are estimated by the instantaneous phase synchronizations of electroencephalography signals. Main results. Experimental results revealed that the characteristics derived from EOG and brain networks have similar trends which contain two stages. At the beginning, the blink amplitudes and the saccade speeds start to reduce. Meanwhile, the global synchronizations of the brain networks are formed quickly. These observations implies that the cognitive decline along the use of the SSVEP-based BCI. Then, the EOG and the brain networks related characteristics demonstrate a slow recovery or relatively stable trend. Significance. This study could be helpful for a better understanding about the depreciation of the BCI performance as well as its relationship with the brain networks and the EOG along the use of the SSVEP-based BCI.-
dc.languageeng-
dc.publisherInstitute of Physics Publishing. The Journal's web site is located at http://www.iop.org/EJ/journal/JNE-
dc.relation.ispartofJournal of Neural Engineering-
dc.rightsJournal of Neural Engineering. Copyright © Institute of Physics Publishing.-
dc.rightsThis is an author-created, un-copyedited version of an article published in [insert name of journal]. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/[insert DOI].-
dc.subjectbrain-computer interface-
dc.subjectEEG-
dc.subjectEOG-
dc.subjectfunctional brain network-
dc.subjectphase synchronization-
dc.titleChanges of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface-
dc.typeArticle-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.authorityHu, Y=rp00432-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1088/1741-2552/ab933e-
dc.identifier.pmid32408272-
dc.identifier.scopuseid_2-s2.0-85088200551-
dc.identifier.hkuros328185-
dc.identifier.volume17-
dc.identifier.issue4-
dc.identifier.spagearticle no. 045006-
dc.identifier.epagearticle no. 045006-
dc.identifier.isiWOS:000552705300001-
dc.publisher.placeUnited Kingdom-

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