A Style Transfer Mapping and Fine-Tuning Subject Transfer Framework Using Convolutional Neural Networks for Surface Electromyogram Pattern Recognition
Author
Abstract

Neural Style Transfer - Reducing inter-subject variability between new users and the measured source subjects, and effectively using the information of classification models trained by source subject data, is very important for human–machine interfaces. In this study, we propose a style transfer mapping (STM) and fine-tuning (FT) subject transfer framework using convolutional neural networks (CNNs). To evaluate the performance, we used two types of public surface electromyogram datasets named MyoDatasets and NinaPro database 5. Our proposed framework, STM-FT-CNN, showed the best performances in all cases compared with conventional subject transfer frameworks. In the future, we will build an online processing system that includes this subject transfer framework and verify its performance in online experiments.

Year of Publication
2022
Date Published
may
Publisher
IEEE
Conference Location
Singapore, Singapore
ISBN Number
978-1-66540-540-9
URL
https://ieeexplore.ieee.org/document/9747657/
DOI
10.1109/ICASSP43922.2022.9747657
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