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 language | English |
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Title of host publication | 10th International Winter Conference on Brain-Computer Interface, BCI 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665413374 |
DOIs | |
Publication status | Published - 2022 |
Event | 10th International Winter Conference on Brain-Computer Interface, BCI 2022 - Gangwon-do, Korea, Republic of Duration: 2022 Feb 21 → 2022 Feb 23 |
Publication series
Name | International Winter Conference on Brain-Computer Interface, BCI |
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Volume | 2022-February |
ISSN (Print) | 2572-7672 |
Conference
Conference | 10th International Winter Conference on Brain-Computer Interface, BCI 2022 |
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Country/Territory | Korea, Republic of |
City | Gangwon-do |
Period | 22/2/21 → 22/2/23 |
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