Neural network classification of myoelectric signal for prosthesis control
Author:
Publisher
Elsevier BV
Subject
Neurology (clinical),Biophysics,Neuroscience (miscellaneous)
Reference13 articles.
1. Analysis of hidden units in a layered network trained to classify sonar targets;Gorman;Neural Networks,1988
2. Stochastic analysis of myoelectric temporal signatures for multifunctional single-site activation of prostheses and orthoses;Graupe;J Biomed Eng,1985
3. Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes;Graupe;IEEE Trans Syst Man and Cybern,1975
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1. Machine Learning for the Control of Prosthetic Arms: Using Electromyographic Signals for Improved Performance;IEEE Signal Processing Magazine;2021-07
2. Feature Extraction from Infrared Spectroscopy by Machine Learning with Correlation Coefficient as Error Function;Transactions of the Japanese Society for Artificial Intelligence;2019-01-01
3. Myoelectric arm using artificial neural networks to reduce cognitive load of the user;Neural Computing and Applications;2015-10-29
4. Electromyographic prosthetic hand using grasping-force-magnification mechanism with five independently driven fingers;Advanced Robotics;2015-10-08
5. A Reduced-dimensional Recurrent Probabilistic Neural Network Based on Time-series Discriminant Component Analysis;Transactions of the Society of Instrument and Control Engineers;2014
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