Classification of Upper Limb Movements Using Convolutional Neural Network with 3D Inception Block

Do Yeun Lee, Ji Hoon Jeong, Kyung Hwan Shim, Dong Joo Kim

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

Abstract

A brain-machine interface (BMI) based on electroencephalography (EEG) can overcome the movement deficits for patients and real-world applications for healthy people. Ideally, the BMI system detects user movement intentions transforms them into a control signal for a robotic arm movement. In this study, we made progress toward user intention decoding and successfully classified six different reaching movements of the right arm in the movement execution (ME). Notably, we designed an experimental environment using robotic arm movement and proposed a convolutional neural network architecture (CNN) with inception block for robust classify executed movements of the same limb. As a result, we confirmed the classification accuracies of six different directions show 0.45 for the executed session. The results proved that the proposed architecture has approximately 613% performance increase compared to its conventional classification models. Hence, we demonstrate the 3D inception CNN architecture to contribute to the continuous decoding of ME.

Original languageEnglish
Title of host publication8th International Winter Conference on Brain-Computer Interface, BCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728147079
ISBN (Print)9781728147079
DOIs
Publication statusPublished - 2020 Feb 1
Event8th International Winter Conference on Brain-Computer Interface, BCI 2020 - Gangwon, Korea, Republic of
Duration: 2020 Feb 262020 Feb 28

Publication series

Name8th International Winter Conference on Brain-Computer Interface, BCI 2020

Conference

Conference8th International Winter Conference on Brain-Computer Interface, BCI 2020
CountryKorea, Republic of
CityGangwon
Period20/2/2620/2/28

Keywords

  • brain-machine interface
  • deep learning
  • electroencephalogram
  • movement execution
  • robotic arm

ASJC Scopus subject areas

  • Behavioral Neuroscience
  • Cognitive Neuroscience
  • Artificial Intelligence
  • Human-Computer Interaction

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  • Cite this

    Lee, D. Y., Jeong, J. H., Shim, K. H., & Kim, D. J. (2020). Classification of Upper Limb Movements Using Convolutional Neural Network with 3D Inception Block. In 8th International Winter Conference on Brain-Computer Interface, BCI 2020 [9061671] (8th International Winter Conference on Brain-Computer Interface, BCI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCI48061.2020.9061671