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dc.contributor.authorWu, Dongruien_US
dc.contributor.authorKing, Jung-Taien_US
dc.contributor.authorChuang, Chun-Hsiangen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorJung, Tzyy-Pingen_US
dc.date.accessioned2018-08-21T05:53:29Z-
dc.date.available2018-08-21T05:53:29Z-
dc.date.issued2018-04-01en_US
dc.identifier.issn1063-6706en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2017.2688423en_US
dc.identifier.urihttp://hdl.handle.net/11536/144749-
dc.description.abstractElectroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77%, and at the same time increase the correlation to the true response speed by 19.39-86.47%.en_US
dc.language.isoen_USen_US
dc.subjectBrain-computer interface (BCI)en_US
dc.subjectcommon spatial pattern (CSP)en_US
dc.subjectelectroencephalogram (EEG)en_US
dc.subjectfuzzy setsen_US
dc.subjectpsychomotor vigilance task (PVT)en_US
dc.subjectresponse speed (RS) estimationen_US
dc.subjectspatial filteringen_US
dc.titleSpatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)en_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TFUZZ.2017.2688423en_US
dc.identifier.journalIEEE TRANSACTIONS ON FUZZY SYSTEMSen_US
dc.citation.volume26en_US
dc.citation.spage771en_US
dc.citation.epage781en_US
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000428613500029en_US
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