Explainable convolutional neural network to investigate age-related changes in multi-order functional connectivity

Sunghee Dong, Yan Jin, Sujin Bak, Bumchul Yoon, Jichai Jeong

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Article number3020
JournalElectronics (Switzerland)
Issue number23
Publication statusPublished - 2021 Dec 1

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Brain-computer interface (BCI)
  • Convolutional neural network (CNN)
  • Electroencephalo-gram (EEG)
  • Explainable artificial intelligence (XAI)

ASJC Scopus subject areas

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


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