Feature Selection Based on Layer-Wise Relevance Propagation for EEG-based MI classification

Hyeonyeong Nam, Jun Mo Kim, Tae Eui Kam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Brain-computer interface (BCI) enables communi-cations between humans and their surroundings via external devices. Electroencephalogram (EEG) is one of the common measurements of brain activities in BCI due to its non-invasive and relatively inexpensive characteristics. There have been a number of studies on the EEG-based motor imagery (MI) classification and its practical applications, including healthcare and rehabilitative technologies. Recent studies have applied deep learning techniques for MI classification, especially utilizing con-volutional neural networks because of their ability to generalize feature representations. However, it is very crucial to select only useful features in MI classification networks, because EEG having a low signal-to-noise value may include unnecessary features that interrupt the model decision. In this study, we perform feature selection in the extracted EEG features based on the layer-wise relevance propagation (LRP) method for MI classification. Our proposed framework is evaluated on the BCI Competition IV-2a dataset in the subject-dependent scenario, and our experimental results show that LRP-based feature selection improves the MI classification performance.

Original languageEnglish
Title of host publication11th International Winter Conference on Brain-Computer Interface, BCI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665464444
Publication statusPublished - 2023
Event11th International Winter Conference on Brain-Computer Interface, BCI 2023 - Virtual, Online, Korea, Republic of
Duration: 2023 Feb 202023 Feb 22

Publication series

NameInternational Winter Conference on Brain-Computer Interface, BCI
ISSN (Print)2572-7672


Conference11th International Winter Conference on Brain-Computer Interface, BCI 2023
Country/TerritoryKorea, Republic of
CityVirtual, Online

Bibliographical note

Funding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program (Korea University), No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No. 2020R1C1C1013830).

Publisher Copyright:
© 2023 IEEE.


  • Brain-Computer Interface
  • Electroencephalo-gram
  • Feature Selection
  • Layer-Wise Relevance Propagation
  • Motor Imagery

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing


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