Deep learning-based classification using Cumulants and Bispectrum of EMG signals

Authors

  • Eugenio Orosco Instituto de Automática UNSJ-CONICET

Keywords:

EMG, Cumulants, Bispectrum, CNN, DNN.

Abstract

Surface electromyographic signals (EMG) historically have been used to classify tasks in basis of a feature extraction scheme and low complexity classifiers. Deep networks, as Dense and Convolutional Neural Network (DNN and CNN, respectively), avoid the traditional, complex and heuristic (handcrafted) process of feature extraction. Today, it is possible to face the computational cost that these automatic techniques require due to the technology advancement. This allowed deep learning techniques to be quickly generalized to countless applications. This paper proposes to use the third order cumulants and their 2D Fourier transform (Bispectrum) to directly feed CNN and DNN deep learning networks. The classifier is not user-dependent (same classifier for all users) and obtains better results than the classical scheme according to several metrics.

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Published

2020-02-16

How to Cite

Orosco, E. (2020). Deep learning-based classification using Cumulants and Bispectrum of EMG signals. IEEE Latin America Transactions, 17(12), 1946–1953. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/2595

Issue

Section

Special Isssue on Deep Learning