Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 신재영 | - |
dc.date.accessioned | 2019-12-05T06:31:38Z | - |
dc.date.available | 2019-12-05T06:31:38Z | - |
dc.date.issued | 2018-02 | - |
dc.identifier.citation | FRONTIERS IN NEUROINFORMATICS, v. 12, Article no. 5 | en_US |
dc.identifier.issn | 1662-5196 | - |
dc.identifier.uri | https://www.frontiersin.org/articles/10.3389/fninf.2018.00005/full | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/117434 | - |
dc.description.abstract | The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 x 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 +/- 12.8, 64.1 +/- 9.7, and 82.2 +/- 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs (p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 +/- 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study. | en_US |
dc.description.sponsorship | This work was supported in part by the Institute for Information & communications Technology Promotion (IITP) grant 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), in part by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2015M3C7A1031969), and in part Basic Science Research Program through NRF funded by the Ministry of Education (No. 2017R1A6A3A01003543). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | FRONTIERS MEDIA SA | en_US |
dc.subject | brain-computer interface | en_US |
dc.subject | mental arithmetic | en_US |
dc.subject | motor imagery | en_US |
dc.subject | electroencephalography (EEG) | en_US |
dc.subject | near infrared spectroscopy(NIRS) | en_US |
dc.subject | pattern recognition | en_US |
dc.title | A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State | en_US |
dc.type | Article | en_US |
dc.relation.volume | 12 | - |
dc.identifier.doi | 10.3389/fninf.2018.00005 | - |
dc.relation.page | 5-5 | - |
dc.relation.journal | FRONTIERS IN NEUROINFORMATICS | - |
dc.contributor.googleauthor | Shin, Jaeyoung | - |
dc.contributor.googleauthor | Kwon, Jinuk | - |
dc.contributor.googleauthor | Im, Chang-Hwan | - |
dc.relation.code | 2018007338 | - |
dc.sector.campus | S | - |
dc.sector.daehak | RESEARCH INSTITUTE[S] | - |
dc.sector.department | INSTITUTE OF BIOMEDICAL ENGINEERING | - |
dc.identifier.pid | naraeshigo | - |
dc.identifier.researcherID | T-5173-2018 | - |
dc.identifier.orcid | http://orcid.org/0000-0003-2899-6893 | - |
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