Incremental Learning and Fault-Tolerant Classifier for Myoelectric Pattern Recognition Against Multiple Bursting Interferences
Author:
Affiliation:
1. Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China
2. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Liaoning Province
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Control and Optimization,Computer Science Applications,Human-Computer Interaction,Biomedical Engineering
Link
http://xplorestaging.ieee.org/ielx7/8253409/9860275/09777751.pdf?arnumber=9777751
Reference32 articles.
1. Myoelectric Control of Artificial Limbs—Is There a Need to Change Focus? [In the Spotlight]
2. Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms
3. Real-time Learning Method for Adaptable Motion-Discrimination using Surface EMG Signal
4. Towards reducing the impacts of unwanted movements on identification of motion intentions
5. Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control
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1. Incremental Classification for Myoelectric Manifold Representation With Matrix-Formed Growing Neural Gas Network;IEEE Transactions on Industrial Informatics;2024-08
2. Decision-Change Informed Rejection Improves Robustness in Pattern Recognition-Based Myoelectric Control;IEEE Journal of Biomedical and Health Informatics;2023-12
3. Adaptive Lower Limb Pattern Recognition for Multi-Day Control;Sensors;2022-08-24
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