Motor Imagery Classification based on Multi-Kernel CNN with the amalgamated Cross Entropy Loss

Jinhyo Shin, Wonzoo Chung

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

3 Citations (Scopus)

Abstract

In this paper, we propose a novel motor imagery (MI) classification method using multi-kernel convolutional neural network (CNN) to optimize the subject dependent kernel size. We designed a multi-kernel CNN consisting of parallel sub-CNNs with different kernel sizes and a weight combiner with the soft-max activation to combine the sub-CNN features. In order to prevent over-fitting problem of the proposed structure, amalgamated cross entropy loss which is defined as the sum of the tentative cross entropy losses of each sub-CNN and the overall cross entropy loss is proposed. The performance of the proposed method is evaluated on BCI Competition IV dataset 2a. The results confirm performance improvement of the proposed method in comparison with existing parallel CNN-based MI classification methods.

Original languageEnglish
Title of host publication10th International Winter Conference on Brain-Computer Interface, BCI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665413374
DOIs
Publication statusPublished - 2022
Event10th International Winter Conference on Brain-Computer Interface, BCI 2022 - Gangwon-do, Korea, Republic of
Duration: 2022 Feb 212022 Feb 23

Publication series

NameInternational Winter Conference on Brain-Computer Interface, BCI
Volume2022-February
ISSN (Print)2572-7672

Conference

Conference10th International Winter Conference on Brain-Computer Interface, BCI 2022
Country/TerritoryKorea, Republic of
CityGangwon-do
Period22/2/2122/2/23

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Brain-Computer Interface (BCI)
  • Convolutional Neural Networks (CNNs)
  • Electroencephalogram (EEG)
  • Motor Imagery (MI)
  • Multi Kernel

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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