Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 임창환 | - |
dc.date.accessioned | 2022-12-06T07:08:38Z | - |
dc.date.available | 2022-12-06T07:08:38Z | - |
dc.date.issued | 2021-03 | - |
dc.identifier.citation | Frontiers in Human Neuroscience, v. 15, article no. 646915, Page. 1-9 | en_US |
dc.identifier.issn | 1662-5161 | en_US |
dc.identifier.uri | https://www.frontiersin.org/articles/10.3389/fnhum.2021.646915/full | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/178057 | - |
dc.description.abstract | Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in the field of brain-computer interfaces (BCIs) owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-retest reliability. Therefore, individual calibration sessions need to be employed before each use of fNIRS-based BCI to achieve a sufficiently high performance for practical BCI applications. In this study, we propose a novel deep convolutional neural network (CNN)-based approach for implementing a subject-independent fNIRS-based BCI. A total of 18 participants performed the fNIRS-based BCI experiments, where the main goal of the experiments was to distinguish a mental arithmetic task from an idle state task. Leave-one-subject-out cross-validation was employed to evaluate the average classification accuracy of the proposed subject-independent fNIRS-based BCI. As a result, the average classification accuracy of the proposed method was reported to be 71.20 +/- 8.74%, which was higher than the threshold accuracy for effective BCI communication (70%) as well as that obtained using conventional shrinkage linear discriminant analysis (65.74 +/- 7.68%). To achieve a classification accuracy comparable to that of the proposed subject-independent fNIRS-based BCI, 24 training trials (of approximately 12 min) were necessary for the traditional subject-dependent fNIRS-based BCI. It is expected that our CNN-based approach would reduce the necessity of long-term individual calibration sessions, thereby enhancing the practicality of fNIRS-based BCIs significantly. | en_US |
dc.description.sponsorship | This work was supported by the Institute for Information & Communications Technology Promotion (IITP) funded by the Korean Government, Ministry of Science and ICT (MSIT), under Grant 2017-0-00432 and Grant 2020-0-01373. | en_US |
dc.language | en | en_US |
dc.publisher | Frontiers Media S.A. | en_US |
dc.source | 79946_임창환.pdf | - |
dc.subject | brain– | en_US |
dc.subject | computer interface | en_US |
dc.subject | functional near-infrared spectroscopy | en_US |
dc.subject | deep learning | en_US |
dc.subject | convolutional neural network | en_US |
dc.subject | binary communication | en_US |
dc.title | Subject-Independent Functional Near-Infrared Spectroscopy-Based Brain-Computer Interfaces Based on Convolutional Neural Networks | en_US |
dc.type | Article | en_US |
dc.relation.volume | 15 | - |
dc.identifier.doi | 10.3389/fnhum.2021.646915 | en_US |
dc.relation.page | 1-9 | - |
dc.relation.journal | Frontiers in Human Neuroscience | - |
dc.contributor.googleauthor | Kwon, Jinuk | - |
dc.contributor.googleauthor | Im, Chang-Hwan | - |
dc.sector.campus | S | - |
dc.sector.daehak | 공과대학 | - |
dc.sector.department | 바이오메디컬공학전공 | - |
dc.identifier.pid | ich | - |
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