Application of Empirical Mode Decomposition Combined With Notch Filtering for Interpretation of Surface Electromyograms During Functional Electrical Stimulation

Author:

Pilkar Rakesh,Yarossi Mathew,Ramanujam Arvind,Rajagopalan Venkateswaran,Bayram Mehmed Bugrahan,Mitchell Meghan,Canton Stephen,Forrest Gail

Funder

National Institute on Disability and Rehabilitation Research

Craig H. Neilsen Foundation

New Jersey Commission on Spinal Cord Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Cited by 26 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. FastICA-based noise reduction algorithm for surface electromyographic signals;2024 4th International Conference on Neural Networks, Information and Communication (NNICE);2024-01-19

2. MEFFNet: Forecasting Myoelectric Indices of Muscle Fatigue in Healthy and Post-Stroke During Voluntary and FES-Induced Dynamic Contractions;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024

3. Comparison of filtering methods for real-time extraction of the volitional EMG component in electrically stimulated muscles;Biomedical Signal Processing and Control;2024-01

4. Automatic selection of IMFs to denoise the sEMG signals using EMD;Journal of Electromyography and Kinesiology;2023-12

5. Ultrasound Imaging-Based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction;IEEE Transactions on Control Systems Technology;2023-05

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