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Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface
Peng,Yufan1,2,3; Wang,Ze1,2; Wong,Chi Man1,2; Nan,Wenya4; Rosa,Agostinho5; Xu,Peng6; Wan,Feng1,2; Hu,Yong7
2020-07-10
Source PublicationJournal of Neural Engineering
ISSN1741-2560
Volume17Issue:4Pages:045006
Abstract

Objective. 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.

DOI10.1088/1741-2552/ab933e
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering ; Neurosciences & Neurology
WOS SubjectEngineering, Biomedical ; Neurosciences
WOS IDWOS:000552705300001
PublisherIOP Publishing Ltd
Scopus ID2-s2.0-85088200551
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding AuthorWan,Feng; Hu,Yong
Affiliation1.Department of Electrical and Computer Engineering,Faculty of Science and Technology,University of Macau,Macao
2.Centre for Cognitive and Brain Sciences,Institute of Collaborative Innovation,University of Macau,Macao
3.School of Engineering Technology,Beijing Normal University,Zhuhai,China
4.Department of Psychology,Shanghai Normal University,Shanghai,China
5.Department of Bioengineering,LaSEEB-System and Robotics Institute,Instituto Superior Tecnico,University of Lisbon,Lisbon,Portugal
6.Key Laboratory for NeuroInformation,Ministry of Education,School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu,China
7.Department of Orthopaedics and Traumatology,University of Hong Kong,Pokfulam, Hong Kong Special Administrative Region,Hong Kong
First Author AffilicationFaculty of Science and Technology;  INSTITUTE OF COLLABORATIVE INNOVATION
Corresponding Author AffilicationFaculty of Science and Technology;  INSTITUTE OF COLLABORATIVE INNOVATION
Recommended Citation
GB/T 7714
Peng,Yufan,Wang,Ze,Wong,Chi Man,et al. Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface[J]. Journal of Neural Engineering, 2020, 17(4), 045006.
APA Peng,Yufan., Wang,Ze., Wong,Chi Man., Nan,Wenya., Rosa,Agostinho., Xu,Peng., Wan,Feng., & Hu,Yong (2020). Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface. Journal of Neural Engineering, 17(4), 045006.
MLA Peng,Yufan,et al."Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface".Journal of Neural Engineering 17.4(2020):045006.
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