dc.citation.conferencePlace |
US |
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dc.citation.conferencePlace |
Orlando |
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dc.citation.endPage |
9 |
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dc.citation.startPage |
5 |
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dc.citation.title |
2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, CIBCI 2014 |
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dc.contributor.author |
Kim, Jinsoo |
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dc.contributor.author |
Kim, Min-Ki |
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dc.contributor.author |
Wallraven, C |
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dc.contributor.author |
Kim, Sung-Phil |
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dc.date.accessioned |
2023-12-19T23:06:45Z |
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dc.date.available |
2023-12-19T23:06:45Z |
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dc.date.created |
2015-03-17 |
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dc.date.issued |
2014-12-09 |
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dc.description.abstract |
This study was aimed at estimating subjects' 3-back working memory task error rate using electroencephalogram (EEG) signals. Firstly, spatio-temporal band power features were selected based on statistical significance of across-subject correlation with the task error rate. Method-wise, ensemble network model was adopted where multiple artificial neural networks were trained independently and produced separate estimates to be later on aggregated to form a single estimated value. The task error rate of all subjects were estimated in a leave-one-out cross-validation scheme. While a simple linear method underperformed, the proposed model successfully obtained highly accurate estimates despite being restrained by very small sample size. |
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dc.identifier.bibliographicCitation |
2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, CIBCI 2014, pp.5 - 9 |
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dc.identifier.doi |
10.1109/CIBCI.2014.7007785 |
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dc.identifier.isbn |
978-147994544-3 |
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dc.identifier.scopusid |
2-s2.0-84922979729 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/46904 |
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dc.identifier.url |
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7007785 |
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dc.language |
영어 |
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dc.publisher |
2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, CIBCI 2014 |
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dc.title |
Across-subject estimation of 3-back task performance using EEG signals |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2014-12-09 |
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