Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 임창환 | - |
dc.date.accessioned | 2021-07-07T01:50:16Z | - |
dc.date.available | 2021-07-07T01:50:16Z | - |
dc.date.issued | 2020-03 | - |
dc.identifier.citation | FRONTIERS IN NEUROSCIENCE, v. 14, article no. 168 | en_US |
dc.identifier.issn | 1662-453X | - |
dc.identifier.uri | https://www.frontiersin.org/articles/10.3389/fnins.2020.00168/full | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/162692 | - |
dc.description.abstract | Ensemble classifiers have been proven to result in better classification accuracy than that of a single strong learner in many machine learning studies. Although many studies on electroencephalography-brain-computer interface (BCI) used ensemble classifiers to enhance the BCI performance, ensemble classifiers have hardly been employed for near-infrared spectroscopy (NIRS)-BCIs. In addition, since there has not been any systematic and comparative study, the efficacy of ensemble classifiers for NIRS-BCIs remains unknown. In this study, four NIRS-BCI datasets were employed to evaluate the efficacy of linear discriminant analysis ensemble classifiers based on the bootstrap aggregating. From the analysis results, significant (or marginally significant) increases in the bitrate as well as the classification accuracy were found for all four NIRS-BCI datasets employed in this study. Moreover, significant bitrate improvements were found in two of the four datasets. | en_US |
dc.description.sponsorship | This work was supported in part by a grant from the Institute for Information and Communications Technology Promotion (IITP), funded by the Korea Government (MSIT) (2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought via AR/VR interface) and in part by grants from the Brain Research Program through the National Research Foundation of Korea, funded by the Ministry of Science and ICT (NRF-2015M3C7A1031969 and NRF-2017R1A6A3A01003543). | en_US |
dc.language.iso | en | en_US |
dc.publisher | FRONTIERS MEDIA SA | en_US |
dc.subject | brain-computer interface | en_US |
dc.subject | bootstrap aggregating | en_US |
dc.subject | ensemble learning | en_US |
dc.subject | near-infrared spectroscopy | en_US |
dc.subject | pattern classification | en_US |
dc.title | Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating | en_US |
dc.type | Article | en_US |
dc.relation.no | 168 | - |
dc.relation.volume | 14 | - |
dc.identifier.doi | 10.3389/fnins.2020.00168 | - |
dc.relation.page | 1-11 | - |
dc.relation.journal | FRONTIERS IN NEUROSCIENCE | - |
dc.contributor.googleauthor | Shin, Jaeyoung | - |
dc.contributor.googleauthor | Im, Chang-Hwan | - |
dc.relation.code | 2020047224 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DIVISION OF ELECTRICAL AND BIOMEDICAL ENGINEERING | - |
dc.identifier.pid | ich | - |
dc.identifier.orcid | http://orcid.org/0000-0003-3795-3318 | - |
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