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

Author:

Dong Sunghee,Jin Yan,Bak SuJin,Yoon Bumchul,Jeong JichaiORCID

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±2.7 years and 12 older 72.5±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.

Funder

Institute of Information & Communications Technology Planning & Evaluation

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Explainable artificial intelligence approaches for brain–computer interfaces: a review and design space;Journal of Neural Engineering;2024-08-01

2. Recognition of Running Gait of Track and Field Athletes Based on Convolutional Neural Network;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024

3. Parallel Convolutional Neural Network Based on Multi-Band Brain Networks for EEG Classification;2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE);2022-04

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