Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 22 October 2021 / Approved: 26 October 2021 / Online: 26 October 2021 (11:45:00 CEST)

A peer-reviewed article of this Preprint also exists.

Dong, S.; Jin, Y.; Bak, S.; Yoon, B.; Jeong, J. Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity. Electronics 2021, 10, 3020. Dong, S.; Jin, Y.; Bak, S.; Yoon, B.; Jeong, J. Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity. Electronics 2021, 10, 3020.

Journal reference: Electronics 2021, 10, 3020
DOI: 10.3390/electronics10233020

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 current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs 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. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. 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 increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

Keywords

Brain-Computer Interface (BCI), Convolutional neural network (CNN), Electroencephalogram (EEG), Explainable artificial intelligence (XAI)

Subject

ENGINEERING, Biomedical & Chemical Engineering

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