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Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity

Authors
Dong, SungheeJin, YanBak, SuJinYoon, BumchulJeong, Jichai
Issue Date
12월-2021
Publisher
MDPI
Keywords
brain-computer interface (BCI); convolutional neural network (CNN); electroencephalogram (EEG); explainable artificial intelligence (XAI)
Citation
ELECTRONICS, v.10, no.23
Indexed
SCIE
SCOPUS
Journal Title
ELECTRONICS
Volume
10
Number
23
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/135638
DOI
10.3390/electronics10233020
ISSN
2079-9292
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.
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