Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity
DC Field | Value | Language |
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dc.contributor.author | Dong, Sunghee | - |
dc.contributor.author | Jin, Yan | - |
dc.contributor.author | Bak, SuJin | - |
dc.contributor.author | Yoon, Bumchul | - |
dc.contributor.author | Jeong, Jichai | - |
dc.date.accessioned | 2022-02-13T15:41:07Z | - |
dc.date.available | 2022-02-13T15:41:07Z | - |
dc.date.created | 2022-01-19 | - |
dc.date.issued | 2021-12 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/135638 | - |
dc.description.abstract | Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a lack of physiological understanding. To investigate the suitability of FC in BCIs for the elderly, we propose the decoding of lower- and higher-order FC using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. A total of 17 young adults 24.5 & PLUSMN;2.7 years and 12 older 72.5 & PLUSMN;3.2 years adults were recruited to perform tasks related to hand-force control with or without mental calculation. The CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increased the classification accuracy by 88.3% compared to the filter-bank common spatial pattern. The LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe, depending on the task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.subject | COMPUTER INTERFACE BCI | - |
dc.subject | WORKING-MEMORY | - |
dc.subject | EEG | - |
dc.subject | RECRUITMENT | - |
dc.subject | HEMISPHERES | - |
dc.subject | AGREEMENT | - |
dc.subject | ATTENTION | - |
dc.subject | SELECTION | - |
dc.subject | COGNITION | - |
dc.title | Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jeong, Jichai | - |
dc.identifier.doi | 10.3390/electronics10233020 | - |
dc.identifier.scopusid | 2-s2.0-85120714742 | - |
dc.identifier.wosid | 000735077500001 | - |
dc.identifier.bibliographicCitation | ELECTRONICS, v.10, no.23 | - |
dc.relation.isPartOf | ELECTRONICS | - |
dc.citation.title | ELECTRONICS | - |
dc.citation.volume | 10 | - |
dc.citation.number | 23 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordPlus | AGREEMENT | - |
dc.subject.keywordPlus | ATTENTION | - |
dc.subject.keywordPlus | COGNITION | - |
dc.subject.keywordPlus | COMPUTER INTERFACE BCI | - |
dc.subject.keywordPlus | EEG | - |
dc.subject.keywordPlus | HEMISPHERES | - |
dc.subject.keywordPlus | RECRUITMENT | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | WORKING-MEMORY | - |
dc.subject.keywordAuthor | brain-computer interface (BCI) | - |
dc.subject.keywordAuthor | convolutional neural network (CNN) | - |
dc.subject.keywordAuthor | electroencephalogram (EEG) | - |
dc.subject.keywordAuthor | explainable artificial intelligence (XAI) | - |
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