CNN-based Subject-Transfer Approach for Training Minimized Lower-Limb MI-BCIs

Ji Hyeok Jeong, Keun Tae Kim, Song Joo Lee, Dong Joo Kim, Hyungmin Kim

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

3 Citations (Scopus)

Abstract

The subject-transfer approach has recently been proposed to overcome the limitation of requiring a long training time in the motor imagery (MI)-based brain-computer interfaces (BCIs). However, the applicability for reducing the training time for lower-limb MI-BCI has not been investigated yet. In this study, we proposed a dual-domain convolutional neural network (CNN)-based subject-transfer method. We investigated how the classification accuracy changes according to the reduced number of training trials. Two lower-limb MIs (gait and sit-down) and rest electroencephalography (EEG) data were collected from five healthy subjects. The CNN model was pre-trained using other subjects' data and fine-tuned with the target subject's training data. There was a significant increase in classification accuracy (7% with 15 and 10 trials) compared to the self-training approach using the same CNN model trained only with the target subject's training data. Based on these results, the subject-transfer approach can contribute to minimizing the training time of lower-limb MI-BCIs while preserving the classification accuracy.

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
  • Convolutional Neural Network
  • Lower-Limb Motor Imagery
  • Subject-Transfer

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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