A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lin, Bo | - |
dc.contributor.author | Deng, Shuiguang | - |
dc.contributor.author | Gao, Honghao | - |
dc.contributor.author | Yin, Jianwei | - |
dc.date.accessioned | 2021-10-22T01:40:27Z | - |
dc.date.available | 2021-10-22T01:40:27Z | - |
dc.date.created | 2021-10-22 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 1545-5963 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82476 | - |
dc.description.abstract | Electroencephalogram (EEG) is a non-invasive collection method for brain signals. It has broad prospects in brain-computer interface (BCI) applications. Recent advances have shown the effectiveness of the widely used convolutional neural network (CNN) in EEG decoding. However, some studies reveal that a slight disturbance to the inputs, e.g., data translation, can change CNN's outputs. Such instability is dangerous for EEG-based BCI applications because signals in practice are different from training data. In this study, we propose a multi-scale activity transition network (MSATNet) to alleviate the influence of the translation problem in convolution-based models. MSATNet provides an activity state pyramid consisting of multi-scale recurrent neural networks to capture the relationship between brain activities, which is a translation-invariant feature. In the experiment, Kullback-Leibler divergence is applied to measure the degree of translation. The comprehensive results demonstrate that our method surpasses the AUC of 0.0080, 0.0254, 0.0393 in 1, 5, and 10 KL divergence compared to competitors with various convolution structures. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.relation.isPartOf | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | - |
dc.title | A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000704824500006 | - |
dc.identifier.doi | 10.1109/TCBB.2020.3024228 | - |
dc.identifier.bibliographicCitation | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, v.18, no.5, pp.1699 - 1709 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85091292307 | - |
dc.citation.endPage | 1709 | - |
dc.citation.startPage | 1699 | - |
dc.citation.title | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | - |
dc.citation.volume | 18 | - |
dc.citation.number | 5 | - |
dc.contributor.affiliatedAuthor | Gao, Honghao | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Electroencephalography | - |
dc.subject.keywordAuthor | Convolution | - |
dc.subject.keywordAuthor | Brain modeling | - |
dc.subject.keywordAuthor | Recurrent neural networks | - |
dc.subject.keywordAuthor | Decoding | - |
dc.subject.keywordAuthor | Brain-computer interfaces | - |
dc.subject.keywordAuthor | Electroencephalogram | - |
dc.subject.keywordAuthor | convolutional neural networks | - |
dc.subject.keywordAuthor | invariance | - |
dc.subject.keywordAuthor | data translation | - |
dc.subject.keywordAuthor | pyramid of activity states | - |
dc.subject.keywordPlus | DEEP | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.