A Subject-Transfer Framework Based on Single-Trial EMG Analysis Using Convolutional Neural Networks

In recent years, electromyography (EMG)-based practical myoelectric interfaces have been developed to improve the quality of daily life for people with physical disabilities. With these interfaces, it is very important to decode a user's movement intention, to properly control the external devi...

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Vydáno v:IEEE transactions on neural systems and rehabilitation engineering Ročník 28; číslo 1; s. 94 - 103
Hlavní autoři: Kim, Keun-Tae, Guan, Cuntai, Lee, Seong-Whan
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Classifiers
convolutional neural networks
Disabilities
Electrodes
Electromyography
Feature extraction
Interfaces
myoelectric interfaces
Myoelectricity
Neural networks
Pattern recognition
Spectrogram
Subject-transfer framework
Support vector machines
ISSN:1534-4320, 1558-0210, 1558-0210
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Shrnutí:In recent years, electromyography (EMG)-based practical myoelectric interfaces have been developed to improve the quality of daily life for people with physical disabilities. With these interfaces, it is very important to decode a user's movement intention, to properly control the external devices. However, improving the performance of these interfaces is difficult due to the high variations in the EMG signal patterns caused by intra-user variability. Therefore, this paper proposes a novel subject-transfer framework for decoding hand movements, which is robust in terms of intra-user variability. In the proposed framework, supportive convolutional neural network (CNN) classifiers, which are pre-trained using the EMG data of several subjects, are selected and fine-tuned for the target subject via single-trial analysis. Then, the target subject's hand movements are classified by voting the outputs of the supportive CNN classifiers. The feasibility of the proposed framework is validated with NinaPro databases 2 and 3, which comprise 49 hand movements of 40 healthy and 11 amputee subjects, respectively. The experimental results indicate that, when compared to the self-decoding framework, which uses only the target subject's data, the proposed framework can successfully decode hand movements with improved performance in both healthy and amputee subjects. From the experimental results, the proposed subject-transfer framework can be seen to represent a useful tool for EMG-based practical myoelectric interfaces controlling external devices.
Bibliografie:ObjectType-Article-1
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2019.2946625