Movement intention decoding based on deep learning for multiuser myoelectric interfaces

Ki Hee Park; Seong Whan Lee

doi:10.1109/IWW-BCI.2016.7457459

Movement intention decoding based on deep learning for multiuser myoelectric interfaces

Ki Hee Park, Seong Whan Lee

Department of Brain and Cognitive Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

48 Citations (Scopus)

Abstract

Recently, the development of practical myoelectric interfaces has resulted in the emergence of wearable rehabilitation robots such as arm prosthetics. In this paper, we propose a novel method of movement intention decoding based on the deep feature learning using electromyogram of human biosignals. In daily life, the inter-user variability cause decreases in performance by modulating target EMG patterns across different users. Therefore, we propose a user-adaptive decoding method for robust movement intention decoding in the inter-user variability, employing the convolutional neural network for the deep feature learning, trained by different users. In our experimental results, the proposed method predicted hand movement intention more accurately than a competing method.

Original language	English
Title of host publication	4th International Winter Conference on Brain-Computer Interface, BCI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781467378413
DOIs	https://doi.org/10.1109/IWW-BCI.2016.7457459
Publication status	Published - 2016 Apr 20
Event	4th International Winter Conference on Brain-Computer Interface, BCI 2016 - Gangwon Province, Korea, Republic of Duration: 2016 Feb 22 → 2016 Feb 24

Other

Other	4th International Winter Conference on Brain-Computer Interface, BCI 2016
Country	Korea, Republic of
City	Gangwon Province
Period	16/2/22 → 16/2/24

Keywords

Convolutional neural network
Deep feature learning
Electromyogram
Myoelectric interfaces

ASJC Scopus subject areas

Human-Computer Interaction

Access to Document

10.1109/IWW-BCI.2016.7457459

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Cite this

Park, KH & Lee, SW 2016, Movement intention decoding based on deep learning for multiuser myoelectric interfaces. in 4th International Winter Conference on Brain-Computer Interface, BCI 2016., 7457459, Institute of Electrical and Electronics Engineers Inc., 4th International Winter Conference on Brain-Computer Interface, BCI 2016, Gangwon Province, Korea, Republic of, 16/2/22. https://doi.org/10.1109/IWW-BCI.2016.7457459

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title = "Movement intention decoding based on deep learning for multiuser myoelectric interfaces",

abstract = "Recently, the development of practical myoelectric interfaces has resulted in the emergence of wearable rehabilitation robots such as arm prosthetics. In this paper, we propose a novel method of movement intention decoding based on the deep feature learning using electromyogram of human biosignals. In daily life, the inter-user variability cause decreases in performance by modulating target EMG patterns across different users. Therefore, we propose a user-adaptive decoding method for robust movement intention decoding in the inter-user variability, employing the convolutional neural network for the deep feature learning, trained by different users. In our experimental results, the proposed method predicted hand movement intention more accurately than a competing method.",

keywords = "Convolutional neural network, Deep feature learning, Electromyogram, Myoelectric interfaces",

author = "Park, {Ki Hee} and Lee, {Seong Whan}",

year = "2016",

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AB - Recently, the development of practical myoelectric interfaces has resulted in the emergence of wearable rehabilitation robots such as arm prosthetics. In this paper, we propose a novel method of movement intention decoding based on the deep feature learning using electromyogram of human biosignals. In daily life, the inter-user variability cause decreases in performance by modulating target EMG patterns across different users. Therefore, we propose a user-adaptive decoding method for robust movement intention decoding in the inter-user variability, employing the convolutional neural network for the deep feature learning, trained by different users. In our experimental results, the proposed method predicted hand movement intention more accurately than a competing method.

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